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Business analysts: What do they do for software implementation projects?
The business analyst role is evolving to include responsibility for successful adoption of new software applications.
Business analysts help ensure that software developers build applications that meet user needs, whether the developers are building an application from scratch or customizing an off-the-shelf solution. To do that, they bridge the enterprise IT department and the business units.
The International Institute of Business Analysis (IIBA), a nonprofit professional association, considers the business analyst “an agent of change,” writing that business analysis “is a disciplined approach for introducing and managing change to organizations, whether they are for-profit businesses, governments, or non-profits.”
The IIBA says the BA role can also have a number of other titles, including business systems analyst, systems analyst, requirements engineer, process analyst and enterprise analyst.
Identifying and then prioritizing technical and functional requirements tops the business analyst’s list of responsibilities, says Bob Gregory, a professor at Bellevue University and the academic program director for the business analysis and management degree program at the Bellevue, Neb., university.
“Elicitation of requirements and using those requirements to get IT onboard and understand what the client really wants, that’s one of the biggest responsibilities for BAs. They have to work as a product owner, even though the business is the product owner,” Gregory says.
BAs engage with business leaders and software users to understand the business process that the software will enable and how the software should operate to improve efficiencies in that process and to add value. They must articulate those ideas but also balance them against what’s technologically feasible as well as financially and functionally reasonable.
“[They need to ask:] What do the systems need to do, how do they do it, who do we need to get input from, and how do we get everyone to agree on what we need to do before we go and do it? The BA’s life revolves around defining requirements and prioritizing requirements and getting feedback and approval on requirements,” says Jeffrey Hammond, vice president and principal analyst with the advisory and research firm Forrester Research.
BAs also identify integration and compliance points, explains Kelly Emo,director of product and solutions marketing for application lifecycle and quality at HPE Software.
Effective BAs are skilled communicators who are also able to foster collaboration.
“You’re often going to find folks who want different things, so BAs have to bring them to some sort of consensus so you don’t have something that looks like a Swiss Army knife. They have to get everyone moving in the same direction,” Hammond says.
He adds: “And I find a little out of the box thinking is required. It’s really hard to get everyone on the same page, and it requires creativity to come up with a solution that’s not the obvious one.”
The responsibilities of the business analyst are changing, as the role evolves to meet the increased speed that business needs from its software development efforts. Enterprise IT departments are moving more of their development from a classic waterfall methodology, where BAs gather user requirements upfront and then hand them off to developers, to development processes that are more iterative and continuous, following agile and DevOps methodologies.
These changes have prompted some organizations to expand the responsibilities of their BAs so that they’re accountable for successfully gathering and presenting user requirements and serving as liaisons between business units and IT as well as the successful adoption of new software applications.
To that end, many organizations measure their BAs not on the number of requirements they identified but rather on how well those requirements meet the users’ needs, whether the new software meets business needs, how well the software is being used, and the users’ satisfaction with the application, Hammond says.
“Those are the things you measure a product manager on, so we do see organizations adopt the product manager title,” Hammond adds, noting that it tends to happen more frequently when a BA-type position sits within a digital business unit instead of within the IT department.
In his April 2016 report Develop Customer-Centric Applications Like The Pros Do , Hammond recommends replacing business analysts with product managers, writing “product managers are also ultimately responsible for getting functionality right and meeting customer demands for convenience, so there’s a stronger sense of accountability for functional decision-making.”
He explains in an interview: “It’s less the title and more the makeup of the person that’s important there. You can’t just take BAs and say you’re product manager; you have to have people willing to accept that level of responsibility.”
Emo says BAs working in such a manner use new tools for identifying needs. They’re increasingly using real-time user data and analytics programs to identify user trends, successful functions and potential user adoption problems with the applications.
“One of the key values in the concept of the BA moving into being a product owner, as the whole line between IT and digital and software development and business shifts, is that this role has become more and more exciting,” Emo adds.
Given the expanding list of responsibilities put on the position, some organizations also have created product managers who work with BAs or who have teams of BAs reporting to them, Hammond says.
Similarly, the expansion and the faster, more iterative pace of software development has changed the timing of the BA’s involvement with a given development project.
A BA working in a classic waterfall development environment is still more heavily involved at the front end, when he or she will gather, analyze and prioritize user requirements before handing those off to developers and then moving on to another software development project, experts say. Meanwhile, BAs working on a more agile project generally stay with the project through implementation and even through multiple releases.
Organizations often assign BAs to several projects at a time if the projects are small enough, or they may assign a BA to a single project if it’s complex. Hammond notes that organizations also assign multiple BAs to very large software development projects.
However, some IT departments today are not involving their business analysts in all in-house application development projects, Emo says.
She says organizations are less likely to assign BAs to development work on the new class of applications such as mobile marketing apps and apps for temporary sales promotions “because they’re operating very lean or doing DevOps.”
“It’s all happening very rapidly in continuous delivery mode, and it’s data driven and not [driven by] lengthy requirement documents. What I see today, especially in the digital first applications, like digital e-commerce, it’s not the traditional business analyst involved.”
On the other hand, BAs are almost universally used for the development of back-office applications and core business software products, where identifying and documenting requirements is particularly critical, Emo says.
“A lot of those applications are under a lot of regulations, so [organizations] need that BA interface to document and ensure compliance,” she says.
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15 Business Analyst Project Ideas and Examples for Practice
Explore business analyst real time projects examples curated for aspiring business analysts that will help them start their professional careers. Last Updated: 22 Feb 2023
Your search for business analyst project examples ends here. This blog contains sample projects for business analyst beginners and professionals. So, continue reading this blog to know more about different business analyst projects ideas.
Business analysts are the demand of the twenty-first century! One can easily affirm this by looking at a report by the U.S. Bureau of Labor Statistics, which has revealed that as of May 2020, the median annual salary received by management analysts is $87,660. The bureau’s report also suggests that we are likely to witness an increase in the jobs of management analysts by 11% between 2019 and 2029. The rate is pretty higher than the average for other occupations. Additionally, the bureau mentioned that there is likely to be intense competition for such jobs because the role offers handsome salaries.
Avocado Machine Learning Project Python for Price Prediction
Last Updated : 2023-01-28 20:57:57
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The role of a business analyst primarily deals with analysing the growth of a business and suggesting methods to improve the existing strategies. Thus, to play such a crucial, one needs to possess a robust set of skills. Let us discuss a few of these to give you a more clear understanding of the skills required to become a business analyst .
Excellent verbal and written communication.
Communicate with different stakeholders and hold different meetings.
Up-to-date knowledge of new technologies and methodologies.
The capability of learning different business processes.
Ability to layout different ways of improving business growth.
Strong time management skills.
Understanding of various analytical tools and their implementation in revealing insights about the business.
Host different workshops and training sessions.
Knowledge of writing formal reports.
Having motivated you with our introduction of this blog, we now present business analyst sample projects that you can try to test/enhance your skills.
Table of Contents
Business analyst practice projects for beginners, business analyst real-time projects for intermediate professionals, advanced business analyst projects examples , top 15 business analyst project ideas for practice.
This section has beginner-friendly projects for business analyst roles that newbies in this domain can start with.
1) Market Basket Analysis
Have you heard of the Beer-and-diapers story? In 2016, Mark Madsen, a research analyst, asked if there is a correlation between the sales of diapers and beers? It turned out that when a few stores placed beers closer to the diapers section, the beer sales went up. This strategy did not work for all the stores, but for a few, it did. By reflecting on this story, we want you to understand how important it is for a business to analyse the correlation between different purchased products, also called Market Basket Analysis.
Project Idea: In this project, you will work on a retail store’s data and learn how to realize the association between different products. Additionally, you will learn how to implement Apriori and Fpgrowth algorithms over the given dataset. You will also compare the two algorithms to understand the differences between them.
Source Code: Market basket analysis using apriori and fpgrowth algorithm
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2) Estimating Retail Prices
For any product-selling business, deciding the price of their product is one of the most crucial decisions to make. And, thus for an aspiring business analyst, it becomes essential to understand what factors influence the decision-making process of product prices.
Project Idea: Mercari is a community-driven electronics-shopping application in Japan. In this project, you will build an automated price recommendation system using Mercari’s dataset to suggest prices to their sellers for different products based on the information collected. You will learn how to use Exploratory Data Analysis (EDA) tools and implement different machine learning algorithms like Neural Networks, Support Vector Machines, and Random Forest in R programming language. If you are specifically looking for business analyst finance planning projects for beginners , this project will be a good start.
Source Code: Machine learning for Retail Price Recommendation with R
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3) Analyzing Customer Feedback
Collecting feedback from customers has become a norm for most companies. It provides them with the user’s perspective and guides them on what changes they should make to their product to increase its sales. Additionally, if the product reviews are public, potential customers feel motivated to trust the genuineness of the seller.
Project Idea: This project deals with the analysis of reviews of products available on an eCommerce website. You will work on textual data and implement data pre-processing methods like Gibberish Detection, Language Detection, Spelling Correction, and Profanity Detection. You will learn how to use the Random Forest model for ranking different reviews. Furthermore, you will explore the method of extracting sentiments and subjectivity from the reviews.
Source Code: Ecommerce product reviews - Pairwise ranking and sentiment analysis
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4) Predicting Avocado Prices
Did you know that more than 3 million new photos of avocado toasts were uploaded to Instagram every day in 2107? As per the British Vogue Magazine , this is indeed true. No doubt that so many of us enjoy avocado toasts in our breakfast. If you are also one of such people, this project idea will keep you hooked as it is all about avocados.
Project Idea: In this project, you will learn how a business analyst can use data analysis methods and help promote the growth of a business. You will work on the dataset of a Mexican-based company and layout an Avocado-price-map for them as they plan to expand their reach to different regions in the US. You will be testing the implementation of various models like the Adaboost Regressor, ARIMA time series model, and Facebook Prophet model to predict the Avocado prices.
Source Code: Avocado Price Prediction
5) Predicting the Fate of a Loan Application
Those interested in banking projects for business analysts will indeed consider this one their favorite from this section as this project deals with loans. For understanding banks’ business model, it is crucial to learn the whole process of approving a loan application.
Project Idea: In this project, you will explore the different factors that influence the eligibility of a loan application’s approval. You will utilise different machine learning algorithms for predicting the chances of success of a loan application. This project will also help you learn about various statistical metrics used widely by business analysts like ROC curve, Gradient boosting, MCC Scorer, Synthetic Minority Over-sampling Technique, and XGBoost.
Source Code: Loan Eligibility Prediction
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6) Predicting Customer Churn Rate
When customers start declining at an unexpected rate, various stakeholders go to business analysts for guidance. It is indeed one of the critical responsibilities of a business analyst to check the rate of customers churning out.
Project Idea: This project will guide you about performing univariate and bivariate analysis on the given dataset of a bank. You will learn how different statistical methods like SHAP (SHapley Additive exPlanations), RandomSearch, GridSearch, etc. should be used and interpreted. This project is another instance of a banking project for business analysts . So, if that’s your bias in sample business analysis projects , do check this one out. Source Code: Customer Churn Prediction
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After you have completely solved the above-mentioned projects, proceed to the sample business analyst projects listed in this section to further enhance your skills. These projects are slightly more challenging as they are closer to real-world problems. So, please refer to the source code links for help.
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7) Prediction of Selling Price for different Products
You must have noticed a few brands sometimes send their loyal customers' coupon codes to attract them. These coupons are often customized according to their purchase history with the brand and thus the offer varies from customer to customer.
Project Idea: In this project, you will work on the dataset of a retail company to estimate the price at which a customer is likely to buy a specific product. Once that is complete, you will use your estimation to design offers for different customers. For the solution, you will use machine learning algorithms like Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks and use different metrics to test each of their performances.
You can add this project under the heading of business analyst finance projects on your resume to highlight the diversity of your skillset.
Source Code : Predict purchase amount of customers against various products
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8) Store Sales Prediction
In most firms, investors are usually external stakeholders that are not directly involved in the firm’s business but are definitely affected by it. And, it is the business analyst’s responsibility to keep the investors up-to-date with the existing and expected growth of the firm’s business model.
Project Idea: In this project, you will work on the dataset of 45 stores of the famous Walmart store chain. The goal is to predict the sales and revenue of different stores based on historical data. You will work with numeric and categorical feature variables and perform univariate & bivariate analysis to find the redundancy in variables. Additionally, you will learn the implementation of the ARIMA time series model and other machine learning models.
Source Code: Walmart Store Sales Forecasting
9) Analyzing Customer Churn
It's the customer who pays the wages. --Henry Ford
Customer churn is painful for all the stakeholders in a company. A business analyst must thus look for ways in which the customer churn rate can be minimised. Additionally, they have to identify the cause behind customer churn to improving business growth. Having a fair idea of which customer is likely to churn out will help a business analyst develop better strategies.
Project Idea: In this project, you will be introduced to one of the popular classification machine learning algorithms , logistic regression. The goal is to use logistic regression for estimating the chances of churn for each customer. Through this project, you will get to explore different statistical methods, including confusion metric, recall, accuracy, precision, f1-score, AUC, and ROC.
Source Code: Churn Analysis for Streaming App using Logistic Regression
10) Estimating Future Inventory Demand
While inventory management does not directly fall in the bucket of a business analyst’s responsibilities, one may still find it there as inventory demand directly impacts several other aspects of a business including sales, marketing, finance, etc. With so many advancements taking place in the IT industry, a business analyst can easily use various tools to forecast the inventory demand. Project Idea: Through this project, you will explore the application of various machine learning models, including Bagging, Boosting, XGBoost, GBM, light GBM, and SVM for predicting the inventory demand of a bakery. This project will also introduce you to the implementation of autoML/H 2 0 and LSTM models.
Source Code: Inventory Demand Forecasting using Machine Learning in R
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11) Predicting Coupon Sales
In the previous section, we mentioned a project that will help you in creating customised coupons for a business’s customers. The next step will be to keep track of which coupons have been purchased. This will further help in understanding customer behaviour and preferences.
Project Idea: In this project, you will work on the dataset of one of Japan’s famous joint coupon websites, Recruit Ponpare. The goal is to estimate which coupons a customer is likely to buy based on their previous purchases and browsing behaviour on the website. You will use different graphical methods to visualise the data and various methods of handling missing values in a dataset. You will evaluate the cosine similarities of coupons and users and use them to make the desired predictions.
Source Code: Build a Coupon Purchase Prediction Model in R
12) Creating Product Bundles
Often when we visit a McDonald’s outlet, we intend to buy only a burger, but when we look at the meal menu, we end up buying the full mean instead of a single burger. This method of combining a few products and selling them as a single unit is called product bundling. It helps in increasing the sales of a business.
Project Idea: In this project, you will identify product bundles from the given sales data. While market basket analysis is commonly used for solving such problems, you will be using the time series clustering method. The two techniques will be compared to understand the significance of both methods.
Source Code: Identify Product Bundles from Sales Data
Recommended Reading: 50 Business Analyst Interview Questions and Answers
Professional Business Analysts planning to aim for senior roles will find business analyst projects samples in this section. A senior business analyst is often expected to possess knowledge of Big Data tools. Thus, you will find the projects described below rely on these tools.
13) Analyzing Log Files
If you are new to Big data projects and want to learn the basics of data analysis using Hive, then this project will be a good start. This simple project has been added to this section to prepare you for the next two projects.
Project Idea: This project is simply about analyzing log files of different users of a website. You will learn how to use Apache Hive to extract meaningful data insights by executing real-time queries.
Source Code: Hive Sample Projects-Learn data analysis using sample data for Hive
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14) Retain Analytics
Retail Analytics refers to the complete analysis of various aspects of a business, including customer behavior and demands, supply chain analysis, sales, marketing, and inventory management. Such deeper analysis assists in deeply understanding the business model and smoothens various decision-making processes.
Project Idea: In this project, you will work with the Walmart stores dataset and use various Big Data techniques and tools to perform retail analytics. You will explore how to use tools like AWS EC2, Docker-composer, HDFS, Apache Hive, and MySQL for implementing the full solution.
Source Code: Retail Analytics Project Example using Sqoop, HDFS, and Hive
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15) Analyzing Airline Data
Data has become a huge asset for many industries, and the airline industry is no exception. They rely on big data to answer a few of the most vital questions like when the customers are likely to witness minimum delay in flight timings? Are older planes more prone to delays? etc. Project Idea: For this project, you will work on the dataset of an airline and find answers to questions like the ones mentioned above. You will be guided on how to ingest data and extract it using Cloudera VMware. After that, you will learn about preprocessing the data using Apache Pig. Next, you will use Hive for making tables and performing Exploratory Data Analysis. You will also get to explore the application of HCatloader and parquet through this project. Source Code: Hadoop Hive Project on Airline Dataset Analysis
Hey, Hey! The blog hasn’t ended yet. Going by what Steve Jobs said. “ ‘Learn continually. There's always “one more thing” to learn.’, we don’t want your learning journey to stop here. Check out more such Data Science Projects and Big Data projects from our repository to work on more exciting projects like the ones discussed in this blog.
10 Great Portfolio Projects for Business Analysis (2023)
You need a portfolio of relevant projects if you want to get a job as a business analyst. Why? There are at least two valid reasons:
- Creating business analyst projects is an excellent way to practice your skills . Doing different exercises is good, but building an end-to-end project lets you apply various skills to solve real-world challenges.
- Your portfolio of business analyst projects will be essential in your job hunt . To land an interview for a business analyst role, you need more than an eye-catching resume and list of all of your certificates and qualities. What you really need is to showcase your skills — the best way to do that is with a portfolio of projects.
In this article, we’ll share 10 great projects that you can add to your portfolio to help hone your skills and land your next interview.
1. Sales Data Analysis
As a business analyst, you’ll likely work with sales data because it plays a crucial role in the commercial success of your company. Whether that means understanding current sales or forecasting future sales, this is a key skill that employers look for.
Sales records usually contain information on a company’s customers, customers’ sales orders, payment history, product categories, etc. This data allows you to analyze your customers’ demographics, which products they buy, when they buy, how much revenue they generate, how well they respond to promotions, and more.
How to Build Your Project
You can take an available dataset (like this one: Sales Product Data ) and analyze sales data from various aspects. The main objective here is to extract key performance indicators (KPIs) that will enable you to make data-driven decisions and improve your company’s business.
Below are some questions you can try to answer in a project on sales data analysis::
- What is the total number of sales?
- What is the average sales per month?
- What is the monthly revenue?
- What are the key demographics of the customers?
- Which market (country) generated the most sales on average?
- What were the profits by segment?
- When were the best- and worst-selling periods?
- Which products sell best?
- Which products should the company order more or less of?
- How should the company adjust its marketing strategies to VIP customers and less-engaged ones?
- Should the company acquire new customers, and how much money should they spend on it?
2. Customer Churn Rate Prediction
Customer churn rate is also a key business indicator that can help you improve your business. It indicates the percentage of people who stopped using your company’s product or service during a defined period of time.
This metric is particularly relevant for subscription-based businesses where discontinuation of the product is easy to detect: the customer has stopped using your product or has canceled their subscription, so the company lost a client.
A high customer churn rate can indicate serious issues with your business: low-quality product, negative customer experience, lack of customer support, etc. That’s why a key goal of any business is to minimize customer churn.
You can build your own project on predicting customer churn rate. To do so, take an available dataset (like this one: Customer Churn Prediction 2020 ) and analyze a company’s data to identify customers who are likely to churn based on a variety of factors, such as the number of calls to customer service and the total charge for calls.
3. Customer Review Sentiment Analysis
Customer review sentiment analysis is a process of detecting customers’ feelings after they have purchased a company’s products. The company can gather this information from product reviews, feedback forms, tickets to their help center, online surveys, etc.
Every company is interested in conducting customer feedback sentiment analysis since it’s a secure way to determine possible reasons for customers’ complaints, and to strengthen the product features that make customers happy. As a result, the business can take measures to fix the issues in a timely manner, improve the customer experience, reduce the customer churn rate, adjust marketing campaigns, and maximize profits.
To build a project on customer review sentiment analysis, you need to find an available dataset (e.g., Sentiment analysis with hotel reviews ) with text data extracted from customer reviews of a certain company. Alternatively, consider parsing such data from the internet by yourself. Your task in this project is to preprocess the text data and explore it using specialized statistical and linguistic tools to identify positive, negative, and neutral experiences and, ideally, their strength and subjectivity.
Be aware of some intrinsic weaknesses of text analysis techniques. For example, they aren’t always able to interpret slang words or rarely used abbreviations — or detect sarcasm.
4. Market Basket Analysis
Market basket analysis explores customer shopping patterns. In other words, we have to answer the question, Which products are commonly purchased together? As a simple everyday example, when someone buys shoes, they would probably be interested in buying shoe polish as well. In real-world market basket analysis, however, the examples can be much less obvious.
Detecting specific product associations helps retailers adjust their recommendation systems, improve marketing strategies, maintain balanced stock, and place the correlated goods close to each other in their stores. In the long run, this approach leads to increasing the company’s sales, improving customer satisfaction, and finding new business opportunities.
As a business analyst project idea, you can take a large set of a retail company’s data (e.g., Groceries dataset for Market Basket Analysis (MBA) ) and investigate customers’ historical transactions. You should focus on descriptive analytics of customers’ purchase behavior, revealing interesting combinations of products that are frequently bought together, and creating valuable suggestions for the company.
5. Price Optimization
Estimating the optimal prices for their products is one of the most important tasks for any modern company. This regards both new companies that just appeared on the market and already-existing ones that are trying to adapt to changing economic conditions — or are planning to grow their business geographically or by market segment.
To solve the price optimization problem successfully, a business analyst needs to investigate historical prices, crucial price factors, the markets where the company operates (and their economic contexts), the profiles of potential clients, etc.
For this project, you can take a dataset of price data for a retail company (e.g., Retail Price Optimization ) containing such information as product names, historical prices, product categories and characteristics, volume of sales, and time and geographic notations. The task here is to select and analyze relevant price-forming factors and the degree of their influence on the prices. Your main goal should be to calculate the optimal selling prices for the products to create efficient, data-driven recommendations for the company.
6. Stock Market Data Analysis
Stock Market Data Analysis involves exploration of the stock market in general, a particular investment sector, or a specific trading instrument. Traders and investors need this analysis to understand past and current trends in the market — and, hence, make better buying and selling decisions.
The stock market generates a huge amount of data every day on the price values and trading volumes of a company.
Consider answering the following questions:
- How often did the company increase (or decrease) in price on a given day?
- What is the general trend of average monthly closing prices over the year?
- Are there any seasonal patterns in trading volumes?
- Is there a relationship between the daily maximum and minimum prices for the company?
- Do large differences in the daily maximum and minimum prices coincide with higher or lower trading volumes?
- Do the patterns in the most recent year match previous years?
In order to build your project, you can select a specific dataset (such as Microsoft Stock Data , Amazon Stock Data , or INTEL Stock Data , explore the company’s historical stock performance, and find insights about the future.
7. Customer Segmentation
Customer segmentation involves segregating a company’s clients into different groups based on their purchasing behavior, financial level, interests, needs, and loyalty to the business. This enables the company to direct its marketing campaigns and offers to the correct target audience. Such a strategy helps the business save time, optimize effort, maximize profits from each client, and improve customer experience.
For your project on customer segmentation, you can find an available dataset (like this one: Customer Segmentation Classification ) that contains customer data of a certain organization. Then, analyze the data from the standpoint of paying capacity and purchasing pattern similarities among the company’s clients. Most likely, you’ll discover that such patterns depend on a wide range of demographic and geographic factors. On the other hand, there are other criteria you can take into account, such as retail vs. wholesale customers. At the end of the project, try to determine some suggestions for which kinds of existing or products or products-in-development the company should advertise to each segment.
Keep in mind that for your customer segmentation model to be effective for the company’s needs, it should provide a reasonable number of classes.
8. Fraud Detection
Fraud is a widespread problem in many industries, like banking, sales, and insurance. The most common form of fraudulent activity is credit card fraud, but there are others, such as identity theft or a cyber attack. This problem is especially challenging because fraudsters’ strategies are constantly adapting and becoming more sophisticated. This means that there is no one-size-fits-all solution for detecting fraud.
A project on fraud detection would be an asset for your business analyst portfolio. What you need to do is to take a dataset with the data on online transactions (e.g., this one: Credit Card Fraud Detection ) and analyze it for suspicious operations using statistical methods. Are there any features that the fraud transactions have in common? Knowing such features (or combinations of features) in advance would help the company identify fraudulent actions timely and take preventive measures.
9. Life Expectancy Analysis
Life expectancy is a critical indicator of health in a certain country (or region). This metric depends not only on the level of medicine in that country but also on its environmental conditions, economic and political context, and social tendencies.
Analyzing the correlation between gross domestic product (GDP) per capita and life expectancy is a good idea for your next business analyst project.
Find a suitable dataset (e.g., Life Expectancy (WHO) ) that provides information on both life expectancy and GDP per capita by year for different countries and regions, explore and visualize the data using appropriate plots, and develop meaningful insights. You may notice some trends for each country or region, as well as an overall tendency. Think about the following questions:
- For each geographic unit, is there a clear correlation between GDP per capita and life expectancy?
- What are the geographic units with the highest and lowest life expectancy? What about their GDP?
- What other potential issues could take place in the geographic units with a lower life expectancy?
- In general, is life expectancy in the modern world growing? And GDP?
10. Building a BI App
In your everyday work as a business analyst, you’ll need to use business intelligence (BI) applications like Microsoft Power BI. Therefore, it’s important to become familiar with business intelligence before applying for business analyst jobs, and to showcase your BI skills in your project portfolio. Building a BI app by yourself is a great way to do so.
For this project, consider taking the available data for a certain company, building a data model for it, and creating a series of analysis and visualizations on various metrics related to the products of that company. Such metrics might be product popularity, which shows the level of customers’ engagement with different products, and product ratings, which indicate customer satisfaction. Try to answer the following questions:
- Which products have improved over time?
- Which products have deteriorated over time?
- Are there some distinct patterns for both categories of products (improved vs. deteriorated) in terms of their popularity and customer satisfaction?
Based on the outcomes of your exploration, you can make recommendations to the company on which products need improvement. You can find a project like this as part of the Business Analyst path that we developed at Dataquest to take you from beginner to job-ready in less than a year.
In this project round-up, we considered 10 cool ideas for business analyst projects to add to your portfolio. Building business analyst projects for your portfolio is a perfect way to practice your skills, and demonstrate your proficiency in business analytics.
By learning with Dataquest, you’ll create high-quality business analyst projects. Half of the project ideas we discussed in this article come from the Business Analyst Career Path . For each of those projects, you’ll receive the data to analyze and guidance to follow. It goes without saying that you’re very welcome to build upon the provided instructions, dig deeper into the data, and extract your own insights.
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Top 6 Business Analytics Project Ideas for Beginners
Business Analytics projects help in boosting a candidate’s resume while applying for a job in this domain. Having worked on a good project not only demonstrates your technical ability but also highlights your maturity, business acumen, and readiness to join the industry. In this blog, let's check out some of the popular Business Analytics project ideas that a beginner can work on.
Business Analytics is a very promising field in the 21st century. Almost all big organizations rely heavily on Business Analytics to plan and make decisions. Since it is such a hit in the market, there are a lot of jobs available. To land those jobs, you must have a promising resume. One of the things that can help you enhance your resume in this field is mentioning the Business Analytics projects that you have done. Business Analytics projects not only will show your employer that you have the skills to find insights from data but also will demonstrate that you are industry-ready.
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So, in this blog, we will discuss six Business Analytics project ideas and topics that can help you boost your resume:
Forecasting the Sales of a Supermarket During Festival Season
Sales conversion optimization, employee attrition and performance, predicting sales in tourism for the next 4 years, predicting the success of an upcoming movie, customer segmentation.
A supermarket has various departments, and it must stock up items that will be in demand in each of these departments. However, while stocking up, it must make sure that it does not have excessive stock, which it will not be able to ship out. Hence, you should be able to predict the impact of a festival season on the department-wise sales of a supermarket.
First, you can use a dataset from Kaggle , and for executing the project, you will need to choose a given holiday, let’s say Christmas. Then, you will have to check if, during the time of Christmas, the store marks the highest numbers in sales and which departments need to stock up more items to meet the rising demand.
Check out this Quick Guide to a Business Analytics Career:
A company does a lot of marketing to get sales, and various kinds of campaigns are initiated to market products. Campaigns such as email blasting and social media marketing are among the most popular ways of marketing a product.
The aim of this project is to understand what the most effective ways are in terms of ROI (return on investment) and which campaign generates more leads and then suggest the ways of going about this marketing campaign in the most optimized manner based on a provided budget.
For this Business analytics project, you can use the following dataset to get information on a company’s marketing campaign data .
Interested in becoming a data analyst? Sign up for the Data Analytics Training Courses in Bangalore offered by Intellipaat.
A company wants to understand what factors lead to employee attrition (i.e., it is trying to know when and why an employee decides to leave the company). By understanding these factors the company wants to change its business environment accordingly so that it can hold on to its best employees.
In this project, you will need to evaluate each factor and its relationship with attrition, for example, the distance from home to office, the job role impact on attrition, etc. For the dataset, you can click here and carry on with your evaluations.
Go through Different Types of Business Analytics to understand your projects better.
Tourism is one of the fastest-growing industries in the world. With the introduction of hashtags like ‘#wanderlust’, there has been an increasing amount of interest among people of different demographics to explore new places. However, this industry has very fluctuating numbers in terms of sales, and different places have different feelings according to the time of the year.
Hence, tourism forecasting has become an increasingly important task in planning, improving, and managing the industry. There is a lot of information and insights that are hidden in the data retrieved from the tourism industry. You can use techniques like data clustering to understand when and where tourists prefer to go, what they like at each location, the mode of transportation of tourists while travelling between spots, etc.
Using insights like the above, you need to forecast the sales for the upcoming 4 years. You can use this dataset for your evaluations and then compare them with the actual data.
How to start a career in Business Analytics? Read our blog on Business Analytics careers to become a successful Business Analytics professional.
Career Transition
The entertainment industry has been growing in every scope. Be it Netflix, Amazon, or Hotstar, there is a lot of content out there. Now, the challenge these streaming services face is what to buy, in the sense that which content will get them more viewers and also satisfy the existing customer base.
For this project, you need to predict the success of an upcoming movie so that whether or not a company should go for buying it based on ROI. To do this, you need to come up with a model and use the historical data of each element involved, such as the actors, the director of the movie, the production company, the genre of the movie, etc.
The main idea of doing this Business Analytics project is to predict the market for the upcoming media content based on some preset parameters as this is one of the most unpredictable industries: Big stars might not always shine, while the newcomers might actually do a great job! You will need to keep all of that in mind.
Looking to get started with Business Analytics? Read our blog to learn Business Analytics now.
An e-commerce company has a variety of customers. Every customer has a different set of tastes and interests and may belong to different financial levels. Therefore, it is a heavy challenge for the marketing and strategy team to decide what products it should be promoting or what kind of campaigns will lead to the most lucrative results.
Spending score is one such metric through which you can segregate customers. Spending score is not just determined by the financial situation, but it is also based on other factors such as customer behavior, the kind of products a customer buys, etc.
In this project, your marketing team basically wants you to identify the customers who will most easily converge and buy products. In doing so, you must show the different segments in percentage and also predict the kind of products and marketing campaigns that will be the most successful with your customers.
In this one of the best projects on Business Analytics, you can use this e-commerce dataset and mall customer dataset .
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If Business Analytics is something that excites you, then you must consider a career in the field as there are always new challenges in it, and the demand is never-ending. You can enrol in our Business Analyst Course to become an expert Business Analyst. Keep reading our blogs to build more Business Analytics project examples.
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I love the sales aspect of the project. I will like to do my project on sales.
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Top Business Analytics Projects to Sharpen Your Skills and Build Your Business Analytics Portfolio
Business analytics is the tool used by professionals to make sound business decisions. It fosters the profitability of a business and helps increase a business’s market value. This is why business owners are keen on employing business analysts or business intelligence analysts to help achieve their objectives.
With the increase in demand for such professionals, you will need to develop cutting-edge skills to land a position. If you don’t want to get certified yet, you should consider completing business analytics projects. These projects will help you gain hands-on experience and showcase to employers your expertise and skill level in business analytics .
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5 Skills That Business Analytics Projects Can Help You Practice
Completing business analytics projects can set you apart from other job applicants. These projects will help you develop real-world experience. Whether your focus is on customer relationship management, financial management, human resources, marketing, or supply chain management, these projects will be invaluable to your growth.
- SQL. SQL is a popular coding language used in databases. Through it, analysts and developers write queries to retrieve data from transaction databases. Data scientists and data analysts also rely on the coding language. After retrieving data from the databases, business analysts present the data visually to stakeholders.
- Statistical Languages. Working on business analytics projects will expose you to statistical languages such as R and Python. Analysts rely on R for statistical analysts and Python for programming. A combination of these languages can help you work easily with big data sets.
- Statistical Software. These projects are also quite beneficial in helping you build skills in statistical software. Through the projects, you will become familiar with SAS, SPS, and Excel.
- Data Visualization . A significant part of business analytics involves data visualization . As part of the projects, you will not only learn how to fetch data from different databases but also present them to stakeholders. This means you will get to familiarize yourself with data visualization tools and techniques.
- Machine Learning. As you work on the projects, you will encounter many instances where machine learning is vital . You should expect to use business intelligence tools for curating friendly user interfaces and augmented analytics to receive accurate insights.
Best Business Analytics Project Ideas for Beginners
Being a beginner in the field of business analytics does not mean you cannot pursue projects to boost your portfolio. There are plenty of beginner-friendly business analytics project ideas to help you grow your skills in business analytics. Below we curated a list of the best beginner project ideas to jumpstart your career.
Data on Employee Performance and Resignation
- Business Analytics Skills Practiced: Data Visualization, Machine Learning
In this project, you will provide a company with data that can explain why employees are resigning. The goal is to take these results and use them to improve the business environment. You can take into account the employee’s distance from home, work culture, or job role. You should evaluate each factor with the relationship to resignation.
Forecasting Sales of a Mall During December
- Business Analytics Skills Practiced: Machine Learning, Data Visualization
A mall features a variety of shops and stalls that see high traffic during the holiday season. In this project, you should be able to determine which is the most popular product and how to ensure the shop does not run out of stock. You should check the current inventory and the customer segmentation to ensure you can forecast the sales properly.
Predicting the Success of a Product
In this project, you can rely on your analytical skills to determine if a particular product will sell well in a specified market. For instance, you can focus on the entertainment industry. With thousands of hours of content being disseminated daily, it is quite challenging to establish which song or movie will do well. You will need to make use of historical data and models to make predictions.
Predicting Sales for an Upcoming Car Design
This project involves taking a deep dive into customer needs and wants. You can work on a project to determine if a new car design, color, or shape will appeal to the target audience. There is a wide variety of cars available in the market to help you determine the most popular vehicle.
Customer Segmentation
- Business Analytics Skills Practiced: Machine Learning, Data Visualization, Statistical Languages
In this project, you will deal with a wide customer base of an organization. The main aim of the project is to provide the best customer segmentation to the business leader, development, and marketing team to design campaigns. You should check on the spending ability of the customers and the most popular products.
Best Intermediate Business Analytics Project Ideas
If you have confidence in your business analytics skills and would like to take on new challenges, you should pursue intermediate business analytics projects. These mid-level projects will open you up to new horizons in business analytics. They can also help in landing a well-paying job position in tech or other fields.
Project Management and Business Analysis
- Business Analytics Skills Practiced: Machine Learning
You can get plenty of ideas by reading this paper on project management and business analytics. The paper covers lessons from A Guide to the Project Management Body of Knowledge and A Guide to the Business Analysis Body of Knowledge . The latter provides a comprehensive guide in the elicitation process, also referred to as enterprise analysis.
The above-mentioned books are instrumental to improving your project management skills and informing you on best practices in the industry. You will start with identifying the organizational problem and finish by defining systems capabilities.
Human Resources
- Business Analytics Skills Practiced: Data Visualization
This project involves automating processes, multidimensional analysis, self-service access, and recruitment methods. Your goal is to find ways to improve recruitment and retainment for a company while remaining within a set budget. This project will help you develop analytical skills in establishing the sensitive areas of a business that can lead to potential losses.
Business Analytics Capstone
- Business Analytics Skills Practiced: Data Visualization
If you have a computer science or business degree, you should consider working on this capstone project. This project-based course will help you learn real-world applications for data-driven decision making. By working on this project, you will familiarize yourself with using data to optimize businesses, maximize value, and make operations efficient.
This capstone project will take business professionals through challenges faced by global companies such as Yahoo and Google. You will learn how to use data for addressing business challenges. It’s a curated project by Yahoo to help you master how to make data-driven decisions after complete evaluation.
Sales Conversion Optimization
- Business Analytics Skills Practiced: Machine Learning, Statistical Software, Statistical Programming
This is a great project to work on Return on Investment. Through this project, you will be able to develop campaign strategies that will positively impact business operations. You will also optimize the budget to be more impactful by utilizing methods such as email blasting and social media marketing.
Optical Character Recognition
- Business Analytics Skills Practiced: Machine Learning, Statistical Programming
You can choose to work on optical character recognition, which deals with converting text in images to typed text. You can find open-source project templates for creating optical character recognition software with Python and Swift. You can program an application that turns handwritten documents into typed ones.
Advanced Business Analytics Project Ideas
These advanced business analytics project ideas can take your expertise to a professional level. These projects feature advanced concepts such as pattern matching, forecasting, sentiment analysis, graph analysis, and neural networks. Find out more about the business analytics project details and the skills you will gain below.
Credit Risk Classification Analysis
- Business Analytics Skills Practiced: Machine Learning, Statistical Programming
In this project, you can choose to focus on a particular financial organization or simply generalize. However, the more specific, the easier it will be to analyze the credit risk. You will start by analyzing the historical data of the customer, financial information, and loan purpose.
You should check on factors like age, gender, marital status, job type, and income in your project. This classification tool should inform the business on the best cause of action when issuing credit or loans.
Sales Data Exploration and Reduction
This project will help inform business leaders on the best course of action when it comes to remaining profitable. You can take a deep dive into the project to make it advanced by including the products or services that will generate more value or a higher ROI. You can also add customer segmentation to the project to help the leaders identify the target audience.
Music Sales in America
- Business Analytics Skills Practiced: Data Visualization, Machine Learning
This project involves assessing factors in music sales like genre, popular artists, and sales distribution. The project will require you to work with Tableau for data visualization. By the end of the project, you will be familiar with top musicians, data mining, data visualizations, and machine learning concepts.
University Fundraising
- Business Analytics Skills Practiced: Statistical Programming, Data Visualization
To complete this project, you will need to include the degree that attracts the most funds, gift donors, and pledge deals. It is best to present this data individually in Excel or a similar tool. Your project should display your ability to conduct in-depth research, data analysis, data visualization, and statistical programming.
Exploring Aircraft Hardware Suppliers
- Business Analytics Skills Practiced: Machine Learning, Data Analysis, Statistical Programming
This is an excellent advanced project idea in business analytics that tackles the demand and supply of aircraft hardware. In the project, you will be expected to create a menu, explore orders, minimum purchases, forms of payment, and customer preferences. To make it more complex, you can also feature the shopping time.
Business Analytics Starter Project Templates
To complete the named business analytics projects, you do not need to start from scratch. There are exceptional template samples that can help you work on your projects seamlessly. Find a list of business analytics starter project samples below.
- Business Analyst Template Toolkit . Whether you are a beginner or a seasoned business analyst, this template toolkit provides templates to address your needs. You will find about 12 sample templates, work samples, and guidebooks. Each of these templates can be customized to fit your needs.
- Software Requirements Documentation Template . This template features business requirements, rules, reports, user interfaces, and data requirements. It also comes with the process flows, use cases, service level agreements, business continuities, and data security plans.
- Attribute Metadata Template . Data features entities and attributes. Entities are identifiable classes of people or things, and attributes are characteristics that give further descriptions. You can rely on this template for the names, attributes, data types, values, and definitions of entities. You can also add extra segments like risk, priority, complexity, stability, and status.
- Business Analysis Plan Template . This template will help you develop a reliable business analysis plan. Through this template, you can document your business planning activities regarding the project.
- Templates for Business Analysts . Tech Canvas features several business analytics templates to provide a solid structure to use in your organization. For example, they feature a strategy analysis template, solicitation and collaboration template, requirement analysis template, and Pareto analysis template.
Next Steps: Start Organizing Your Business Analytics Portfolio
A well-curated portfolio might be what you need to get to the next level in your career. After you have amassed solid real-world skills from the business analytics projects, you need to know how to present them for job applications. The tips we list below will guide you to designing a winning resume.
Pinpoint Your Achievements
Use your portfolio for marketing your skills and experience. Always try to capture the recruiter’s attention from the onset by displaying your best work. Often hiring managers receive hundreds of applications, so it’s important for you to highlight your achievements to showcase your skills.
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Key in Relevant Information
Align your portfolio to the job requirements and description. Use these job sections to guide you in adding relevant information to your portfolio. You will gauge the skills and experience needed, which will help you curate the best-suited portfolio.
Make It Simple
You must present a straightforward portfolio. Your portfolio should have concise documents which are organized. Keep it updated so that it can be easier for the employer to track your progress over the years.
Business Analytics Projects FAQ
Yes, a well-curated business analytics portfolio can lead to a well-paying career. As a professional, consider aligning your documents according to the job requirements. This will significantly increase your chances of employment.
No, you do not need to learn how to code to complete a business analytics project. However, having basic knowledge of software programming can be highly beneficial. You will have a broad understanding of the technical side of the business.
There are four different types of business analytics. There are descriptive, diagnostic, prescriptive, and predictive.
No, business analytics projects are not difficult to complete. As long as you have the motivation and experience to complete a project, you will be able to see it through. Ensure you have a strict schedule to help you remain consistent.
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Top 13 Business Analytics Projects To Enhance Your Resume & Portfolio
Introduction
The fascinating world of data and AI has brought forth many scientific tools, algorithms, processes, and knowledge extraction systems to identify meaningful patterns from structured as well as unstructured data. The boom in data analytics in the last couple of years is only growing and will reach the next level with so many innovations in the artificial intelligence domain.
If Data Analytics is something you fancy and want to get a solid foundation on this topic, then you must have a portfolio of data analytics projects to showcase. If you are wondering how to start with data analytics, we have here data analytics project ideas that are good for beginners as well as those who are in intermediate or higher levels. If you are a student, then our ideas could also be used for data analytics projects for students.
Why Data Analytics Projects?
Data Analytics projects are the best way for aspiring Data Science professionals to gain hands-on experience. In these projects, you will get to deploy various Data Science and Machine Learning algorithms in real-world scenarios to uncover connections between data points and understand how different variables may impact each other. The more you practice on Data Analytics projects, the stronger you build your portfolio to showcase your expertise to potential employers.
Tips To Include Data Analytics Projects To Enhance Your Resume
Showcasing all the Data Analytics projects you’ve worked on can help you create a resume that distinguishes you from other candidates with similar job experiences and academic credentials. Below are some tips for including Data Analytics projects on your resume.
- Adjust As Per The Job Description : Go through the job description keenly, identify what skills the recruiting manager is looking for, and then choose relevant projects that demonstrate your abilities in those areas.
- Highlight Under A Separate Project Section: If you have worked on a diverse range of projects, try including a separate projects section on your resume to showcase them. Alternatively, you can try including projects in the job experience or education sections.
- Adding A Link To Your Portfolio: You might provide a link to your portfolio and your contact information to urge hiring managers to look into other projects you’ve worked on.
Data Analytics Projects (Easy, Medium, Hard)
To get started with data analytics project topics, you would first need to understand what level you are comfortable in and then decide whether you want to get on with data analytics projects for beginners, intermediate, or higher levels. Let us take a look at what it entails to do a project in these 3 levels:
- Beginner level – If you are someone who is just starting with data analytics, you must go through the data analytics project examples in the beginner section. These projects do not employ heavy application techniques, and their simple algorithms would let you move forward smoothly.
- Intermediate – Here, medium to large data clusters are taken and need you to have a sound foundation of data mining projects along with machine learning techniques. If this is something you are well-versed with, then you can work on the projects outlined in the intermediate section.
- Expert – This section is for industry experts where neural networks and high-dimensional data are worked with. If you have the blend of creativity and expertise required for such projects, then the data analytics mini project in the advanced section is for you.
Easy or Beginner level projects
Intermediate level projects, advanced level project.
- Fake News Detection – If you know python, then you could develop this data analytics project in python which can detect a hoax or false news that is generated to fulfill some political agenda. This news is propagated through social media channels and other online media. The model is built using the python language, which can accurately detect the genuineness of a news item. You could use a PassiveAggressiveClassifier to build a TfidfVectorizer, which can classify news into “fake” or “real.”
- EDA or Exploratory Data Analysis Project – This is the first thing a data analyst needs to do as part of their job. In this project, we look into data to recognize and identify patterns. Using data modeling techniques, you can summarize the overall features of data analysis. EDA could be done with or without the help of graphics. You could also use univariate or bivariate quantities to perform EDA. The IBM Analytics community is valuable if you want to delve into an EDA project.
- Sentiment Analysis – This analysis is used widely in online communities for brand reputation management or to perform competitor analysis using the R framework. This data analytics project in r will try to understand the opinions and sentiments of viewers based on the words they use. In this classification, classes are either binary (positive or negative) or multiple (happy, angry, sad, confused, disgusted, etc.). You could use the “Jane Austen” package with a relevant dataset. Using general-purpose lexicons like bing, Loughran, and AFINN and performing an inner join, you could build a word cloud for the final display of the data analytics project report.
- Colour Detection Project – This is a good data analytics project for students where they can build an interactive app to detect the selected color from an image. Many of us can not recognize or remember the name of color since there can be around 16 million colors based on RGB values.
- Forecasting Sales for a New Car Design – This project requires a thorough examination of consumer needs and desires. You can work on a project to see if a new automotive design, color, or form will appeal to the target demographic. There are numerous autos on the market to assist you in determining the most popular vehicle.
- Predicting a Product’s Success – In this project, you will use your analytical skills to analyze whether a specific product will sell well in a given market. You may, for example, concentrate on the entertainment industry. With thousands of hours of content distributed daily, determining which music or movie will do well is difficult. To make forecasts, you will need to leverage previous data and models.
- Insights From Employee Performance and Resignation Statistics – In this assignment, you will provide statistics to a corporation that can explain why employees depart. The intent is to use these insights to improve the company environment. You can consider the employee’s proximity to home, work culture, or job description. It would be best to weigh each element concerning the likelihood of resignation.
- Chatbots – Chatbots are an extremely useful tool in businesses as the huge surge of customer queries and messages can be handled by chatbots without slowing down business. Artificial Intelligence, Data Science, and Machine Learning are the three pillars of designing a chatbot. Chatbots can be trained using recurrent neural networks and intent JSON datasets. The main implementation could be done in python.
- Handwritten digit recognition – Machine learning enthusiasts widely use the MNIST datasets of handwritten digits. You use convolutional neural networks and do the real-time prediction of digits drawn on a graphical user interface.
- Gender and Age detection – You can build this interesting data analytics project in python which can predict gender and age by analyzing just one image. To do this project, you would need to know about computer vision and its principles.
- Movie recommendation system – The concept of recommending movies is complex and is based on the abstract click method. It requires a huge implementation of machine learning and accessing humungous datasets that include users’ movie browsing history, preferences, etc. You would need to use collaborative filtering to get a hang of user’s behavior and the R Framework, along with the MovieLens dataset, is a good fit for such projects. To channel through the datasets, you could use surprise model selection and matrix factorization too. Brands like NetFlix use this method, and is a lot of grueling work even for industry experts.
- Credit Card Fraud Detection – Another data analytics project in r will need you to work with decision trees, gradient boosting classifiers, logistic regression, and artificial neural networks. By using the card transactions dataset, you can classify transactions on a credit card into fraudulent or genuine categories.
- Customer Segmentation – This is one of the most popular data analytics projects for companies as they need to create various groups of customers at the beginning of any of their campaigns. This project is an implementation of unsupervised learning and uses clustering to identify different segments of customers so that companies can target the customer base they need to. Customers are divided into groups based on age, gender, preferences, spending habits, etc. This is done to market to each group more effectively. You can use K-means clustering and visualize gender and age distributions.
We know that finding a perfect idea for your Data Analytics project could be more daunting than actually working on the project. We hope the above-mentioned Data Analytics Project ideas will be just the inspiration you’re looking for. The bottom line is that Data Science has high growth potential and continues to increase, promising in-demand opportunities for people proficient in the subject. Including projects on your resume is a definite way to make it stand out.
Finding the right place to learn and become proficient in all these skills and languages is also important. UNext, recognized as one of the Top 10 Data Science Institutes in India, is the right place for you. UNext in collaboration with IIM Indore, offers the Integrated Program In Business Analytics for enthusiasts in this field. The course runs for 10-month and is conducted live online to aid interested learners in mastering the tricks and trades of the domain.
- Role of Business Analyst: Key Responsibilities of a BA
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The Different Types of Business Analysis Projects
Business analysis for dummies.
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Data warehouse projects.
Data warehouses are useful for trend analysis, forecasting, competitive analysis, and targeted market research. Data is often summarized by specific subject area, function, department, geographic region, time period, or all of these.
Most data warehouse projects fall into the “large project” category and result in a substantial project planning effort for you as the business analyst. These projects often have a company-wide focus. The business priority for the project depends on what critical decisions need to be made to address a business threat or opportunity.
Include these types of tasks in your data warehouse project work plan:
Identifying what information the data warehouse must contain, identifying who should have access to it, and making sure users have the right level of access.
Identifying and prioritizing subject areas to be implemented.
Managing the scope of each subject area iteration or release.
Validating the data accuracy and consistency during the extract/transform/load (ETL) process.
Defining the correct level of data summarization.
Establishing a data refresh schedule that’s consistent with business needs, timing, and cycles.
Researching and reviewing available commercial off-the-shelf (COTS) business intelligence tools used for complex reporting.
Planning for a user-friendly, powerful desktop query tool for users to access data without IT assistance.
Planning for the user training and support needed to learn how to use tools and access data.
Ensuring thorough testing is done prior to user acceptance testing (UAT).
Process improvement projects
As a business analyst, your evaluation of the business process may result in a recommendation for software changes, procedural changes, organizational changes, or personnel changes.
The tasks you perform when completing a process improvement project include analyzing the current process, capturing metrics as a baseline, identifying the problems, and identifying solutions that fix those problems to achieve better performance.
Reengineering — another approach to changing a business process — happens when you start from scratch to ask what the organization needs in order to succeed instead of fixing something that already exists. You ignore current roles, silos (compartmentalized departments in organizations), and outdated business rules, and challenge assumptions to create enterprise-wide changes. Reengineering implies that you’re innovating dramatically to design new, streamlined processes.
Tasks related to process reengineering projects include the following:
Performing root cause analysis to find out the real problem that exists within the business
Brainstorming with the project team alternative approaches to address the problem area
Choosing the best approach that solves the business problem
Infrastructure projects
Tasks to include on your work plan include the following:
Assessing how software interface changes (even small ones) may impact usability
Assessing how the project may impact user productivity and whether training may be required
Determining whether any change to a work process needs to be made based on the project
Business analysts sometimes underestimate or miscommunicate business impact, technical risks, and priorities, so be careful. In particular, don’t forget about implementation considerations and transition requirements (user training, timing, and support).
Although infrastructure projects aren’t intended to change user functionality, user productivity often decreases during the learning curve as users get used to the new elements.
Because these projects are technology improvements, they may often be delayed to make room for more business-critical efforts, assuming their delay doesn’t significantly impact the business.
These projects may be initiated because vendor support is no longer available.
Web development projects
W eb development projects are customer-facing web applications that are targeted at consumers and are available inside or outside the organization. As such, they require some special considerations in your work plan.
When planning for this type of project, make sure to prioritize the features and functions. Doing so allows the team to work on and implement the highest value features first. Using an agile approach (building a highly skilled, tightly knit, self-managed, and collocated team that stays with the project from beginning to end and delivers software quickly) works well for these types of projects.
Key stakeholders involved in these projects include usability experts, marketing product owners, and a customer representative or surrogate representative, such as marketing or business analyst.
The following are some tasks to include on a web development work plan:
Eliciting usability and security requirements
Use cases, user stories, wireframes, prototypes, and simulations
Testing activities like UAT
About This Article
This article is from the book:.
- Business Analysis For Dummies ,
About the book authors:
Paul Mulvey, CBAP , Director, Client Solutions, B2T Training, has been involved in business analysis since 1995. Kate McGoey, Director, Client Solutions, B2T Training, has more than 20 years' experience in application development and life cycle processes business. Kupe Kupersmith, CBAP, President of B2T Training, possesses more than 14 years of experience in software systems development. He serves as a mentor for business analysis professionals.
https://www.b2ttraining.com/about-us
This article can be found in the category:
- General Business ,
- Business Analysis For Dummies Cheat Sheet
- 10 Roles for Business Analysis Professionals
- 5 Tips for Conducting a Requirements Review Session
- Division of Labor in Large Business Analysis Projects
- Maximize the Business Analyst–Project Manager Relationship
- View All Articles From Book
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Skills and Projects You Need to Get a Data/Business Analyst Position 2021
Part 2 of a new graduate’s guide to becoming a data/business analyst.
Projects SQL Website
You’ve probably heard a lot about the importance of projects.
Projects are particularly crucial for a recent graduate applying to be a data analyst or a business analyst because they demonstrate proficiency in skills that you probably didn’t use outside of a classroom context .
As much as I might want to list my Data Structures class as experience because I used Java during it, please for the love of god, do not put any computer science labs or classes on your resume. Not only is putting labs on the internet likely directly against your school’s honor code, but it also looks amateur. The exception to this is that you can list classes under a Course Highlights section on your resume that is located beneath your degree.
What makes a good project?
- Cool, uncommon dataset
- Clear, answerable question
- Visualization and statistical techniques used to answer the question
- Some form of an answer to your question
There are some crazy talented people on the internet making insane and complicated data science projects. Keep in mind that most of those people have Master’s degrees, PhDs, and tons of work experience. Rest assured, you do not have to be one of those people to get a job.
You’ll notice that I didn’t mention any machine learning techniques. It’s because you do not have to know machine learning to get an entry-level data analyst job. If you’re lucky , they’ll let you play with machine learning while you’re on the job. The data scientists you work with will be doing most of the actual machine learning.
The analytical techniques you use don’t have to be anything crazy, at least for a data analyst or business analyst. In fact, you don’t even have to include any sort of machine learning techniques. Just make some pretty visualizations of the data that tell you something that isn’t immediately obvious when you look at a spreadsheet.
That said, if you want to use machine learning, go for it! Machine learning is fun and there are lots of ways to integrate it into your projects.
How do I find a dataset?
There are countless sources of datasets. Kaggle and data.gov are a couple of good ones. Most datasets are going to be downloadable as .csv files, which is an easy file format to work with in most languages and applications.
If you’re interested in machine learning, you should know about some of the classic datasets. You can find them at the UCI machine learning repository .
Keep in mind: Try not to use any of the UCI datasets for your projects. They are extremely commonly used, which means that people have analyzed them to death. They’re mainly useful as a means of comparing machine learning techniques in academic papers. But as a machine learning person, you should know about them.
My recommendation:
Instead of looking through existing datasets, I would think about what kind of data you want to work with. After you come up with an idea, specifically search for a dataset. You’ll find less commonly used datasets and end up with a more interesting project.
Ask yourself:
- What’s something I’m curious about? What’s something I’m passionate about?
- What’s something I know something a little bit about already that others don’t know anything about?
- Do I want to work primarily with quantitative data or qualitativ e data?
- What skills do I want to prove that I know or learn through this project?
If you have the programming skills, you should feel free to scrape your own data from the internet! It’s not that difficult and there are lots of tutorials that will tell you exactly how to do it.
Make sure the data is quality — before you touch it, look for the ratio of missing data to total data, and total lines of data. A little missing data is fine, but a lot of missing data is a problem. If you find a dataset that has less than 1000 rows, it’s probably not going to be able to tell you anything interesting or useful.
How do I come with a question?
You’re not going to finish your project or do it well if you don’t really care about the answer to your question. So pick a question that genuinely interests you!
Also — don’t feel bad if it takes a while to come up with a question! I would argue that counter-intuitively, coming up with a question is often the hardest part of the entire project .
This is why I would recommend against using Kaggle as a resource for a personal data project unless you’re going hardcore for Data Scientist jobs. Kaggle usually defines the question for you, which basically means that Kaggle does the hardest part for you.
Luckily, since you just completed an entire undergraduate degree, you probably have some experience coming up with good questions!
Think about the last time you came up with a project in school without much guidance from professors or wrote a thesis statement for an essay based on papers you read in class. The skills that helped you craft good arguments and completable projects are the same skills that you’re going to use right now to come up with a question.
The project can be anything from a random thought once you had to something you’ve spent years thinking about. One day, I was wondering what people complained about on the internet. I taught myself about some relevant natural language processing techniques and had a complete project within a week. You can see it here .
Another example: I knew I wanted to do a project that had something to do with politics since I was a politics major in college and I wanted to teach myself how to use Tableau. So I searched for a dataset, found some cool ones at openelections.net, and ended up with this project .
Final thoughts on projects
- Don’t be afraid to do things that you’ve seen other people do. If it demonstrates your skills and you do it in a different way than other people, it’s okay to come to the same conclusion as other people. Your future employer cares a lot more about how you think and work through a problem than a unique conclusion.
- Try to have at least 3 different projects that demonstrate your skills , preferably different skills in different projects.
- If you did a data science-related project in school that was entirely your idea, you should feel free to list it in the projects section of your resume. For example, my course recommender project was something I originally did for an Artificial Intelligence class.
SQL — what’s the deal with SQL and how do I get good at it?
SQL is a skill that many, but not all, employers hiring for data analysts and business analysts are going to want you to know well and will likely test you on during the interview process.
SQL is a logic-programming language, which basically means that every query is a logical statement in pseudo-English that tells the computer exactly what subset of the data you want. In general, you’re not going to be iterating over anything. This is different from many programming languages like Python, Java, and C.
Anyone who took Discrete Mathematics in college should know more than enough set theory to understand and write SQL logic. If you didn’t take Discrete Mathematics or don’t feel comfortable with set theory, I’d recommend that you go through an online course on basic set theory before you even start writing SQL queries . It’ll make your life so much easier.
By the way — you shouldn’t bother including any SQL in your projects unless you really want to. If a potential employer really cares that you know SQL, they’ll give you a timed test. Honestly, I have a lot of test anxiety, so I didn’t exactly ace those tests. Given that, aspects of this section are my best guess at how good at SQL you need to be, rather than hands-on experience.
So, how do I learn and get good at SQL?
There’s a ton of versions of SQL out there that all have slightly different syntax and keywords. MySQL is the most common, so I’d recommend focusing on learning that.
There’s a lot of resources out there to learn and get good at SQL — CodeAcademy, W3Schools, and more. I don’t really have a preference for one over another but you probably will.
To practice, I found LeetCode to be really useful. LeetCode is mainly used by people trying to practice for Software Engineering interviews, but they have tons of SQL problems on there that were quite similar to ones I saw on actual tests. Drill “easy” and “medium” questions until you can consistently correctly answer them in 5–10 minutes. Throw in a few “hards” to challenge yourself. Be sure to time yourself — it may make you feel bad initially, but you will improve and it’ll give you a lot of insight into how proficient you are.
LeetCode is free for limited problems or $35 per month to get access to all of the problems. Frankly, I think the $35 is worth it if you are really serious about practicing.
If you don’t want to pay anything, there are many free resources to practice online. You also can download MySQL for free onto your computer if you want to practice a version of the real thing, especially if you want to practice creating and deleting tables and rows.
Here’s an example of real SQL interview questions that you can look over.
Specific keywords/functions to know:
- JOINS (all of them, and how to use them quickly and proficiently)
- the difference between WHERE and HAVING
- the difference between UNION and UNION ALL
This article and this article have some good tips on this as well.
To be fair, I’m not really the best person to be telling you how to get good at SQL, but this is what I would be doing if I had found a job a month later than I actually did.
Do you need a website to get a data analyst job? No. Does it help? Absolutely.
Advantages of a website:
- It shows potential employers that you have an eye for visual design and that you can communicate well
- Enables you to control how you appear on the internet
- It’s easier to highlight and summarize projects on a website than on GitHub or Medium
- Can host your personal blog, if you have one
You can think of your website as a nicer, better version of your LinkedIn page — I would recommend linking to it from your LinkedIn page. The major advantage of a personal website over a LinkedIn page is that you can customize it precisely to your liking.
For an example, feel free to check out my website . If you want to find others, just search “data science portfolios” or message me and I can send you a few links to some portfolio websites that I saved.
Your website will particularly impress business people. You will inevitably interview with business people if you make it far enough in the interview process, so impressing them matters. It also gives you an easy way for people to check out all of your work without you having to send them multiple links.
Be aware, however, that your hiring manager will probably not take it as any indication of your technical skills, even if the business people do.
What should I put on my website?
- A landing page that contains information the first thing that you want any potential employer to know about you
- Links to all of the websites you want to highlight
- About Me section
- Projects, including a summary and relevant links
- Blog (optional) — blogs are a great way to talk about your projects, so I’d recommend one, and it’s perfectly ethical to cross-post on your personal website and Medium
- Resume (optional)
How should I make it?
Just use WordPress. It’s okay, even if you have a CS degree, to make it in WordPress. It’s more important to have a nice-looking website than hard-coded HTML.
Use a custom domain, if you can. Ideally, it’s your first and last name followed by “.com” (i.e. mine is elianagrosof.com). If that’s not available, feel free to get creative about getting as close as you can to your actual name.
You’ll need to buy the domain (usually about $20 per year) and likely pay for some sort of hosting service, which should also be inexpensive.
Where should I link to my website?
- On your LinkedIn page
- On your resume (along with a link to your LinkedIn page and GitHub)
- In the link spots for “Portfolio” and “Other” on some job application forms
A New Graduate’s Guide to Becoming a Data/Business Analyst
Part 1: How to Become a Business Analyst 2021 Part 2: Skills and Projects You Need to Get a Data/Business Analyst Position Part 3: How to Network and Use LinkedIn to Find a Job Part 4: How to Use Professional Groups to Find a Business Analyst Job 2021 Part 5: How to Keep Your Sanity During Your Job Search
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10 Data Analytics Project Ideas for Final Year Students
Are you approaching the final year of your data science course?
If so, it may be the case that a data analytics project is among the requirements for earning your degree.
However, choosing the right data analytics project can be challenging due to the complexity associated with such projects . Many of them involve a steep learning curve, which may not be ideal if you don’t have too much time to spare.
In addition to that, some of these projects require data science tools with costly subscription plans.
It is for these reasons that I will be recommending proven project ideas, some of which I have tried out over the years to enhance my professional portfolio.
In this article, I’ll be taking you through some data analytics project ideas for final year students, so you can find one that meets your goals in terms of both budget and simplicity.
Let’s get started.
1. Telecommunications Churn Prediction
Customer churn is a huge problem in the telecommunications industry.
A study by the University of Zambia established that churn rates reach as high as 67% annually for telecom providers.
Among the leading factors was dissatisfaction with various services. It is for this reason that a telecommunications churn prediction system is among the most interesting data analytics project topics.
Binary classification can help with customer segmentation if you’re working with large telecommunications datasets, slotting your users into groups depending on a classification rule and in line with certain attributes.
Via a logistic regression model and a telecommunication data set, you can predict how many customers are likely to opt-out of a service . This project enables you to identify at-risk customers from various customer points, most notably a CRM system.
For a head start, these machine learning courses offer excellent ways to implement these data analytics project ideas for students. Using machine learning and python, you’ll learn how to predict customer churn and economic trends.
2. Fake News Detection Software
Do you encounter fake news often?
According to a Statista survey, over 52% of Americans feel many online news websites regularly push out fake news. Fake news entails articles with inaccurate facts and figures, and those deliberately created to mislead the audience.
It consequently becomes difficult to trust any information you find online. That’s why your data analytics final year project ideas could include a fake news detection system.
Using a K-Nearest Neighbour classifier, which is ideal in this case because the outcome of the algorithm doesn’t necessarily depend on one variable, you can lay the groundwork for your model. As an example, here’s a sample fake news detection model by IEEE.
This algorithm can then be trained by data scraping information in the world wide web to build a database against which you can verify suspicious articles , thereby classifying them as either fake or trustworthy. The classification process could be based on the source of the article and the length of the article, among other parameters.
3. Movie Recommendation System
Choosing the right film on a streaming platform can be challenging, because of the sheer volume of options at your disposal.
Statista reports that 2,734 movies were released between 2017 and 2020 in North America alone. This attests to how endless the choices are, which easily leads to you finding a movie outside your taste preferences, and consequently leaving a bad review on the platform and opting for another service altogether.
To solve selection issues, one of the big data analytics project ideas you could try out is a movie recommendation system .
We’ll focus on two techniques, namely:
- User-based filtering
- Collaborative filtering
User-based filtering entails building an algorithm to recommend new movies to your users, depending on past data about similar items. In other words, your system would recommend similar genre movies to viewers.
Alternatively, you could go with an item-based collaborative filtering algorithm, which reverses this process. Using datasets about user ratings, your system would recommend movies according to past preferences of users with similar review habits. These data science courses will help you get started on your movie recommendation system.
Some of the courses will show you how to build the project from scratch, and even offer data sets you could use for your movie recommender.
4. AI-Powered Analytics Chatbot
Modern customer service offers excellent data analysis project ideas.
If you’ve ever made an inquiry to a business after-hours on the verge of a public holiday, then you know the pain of having to wait days before getting a response to your urgent request. A Statista survey finds that 12% of poor customer service is down to a lack of speed.
By building your own chatbot, you can improve customer satisfaction for business owners. To create a bot that can handle unstructured data, you could use the Seq2seq approach for your natural language processing. This algorithm implores a recurrent neural network to inform the next step based on the context of the first.
Using SQL language, you can connect your bot to question-answer pairings to offer appropriate responses when prompted.
These artificial intelligence courses can help you build a chatbot quickly and easily. If you aren’t too keen on the coding work involved in creating such a conversational AI, some of these courses include no-coding data analytics project ideas for beginners.
5. Stock Market Prediction Application
The stock market is a very volatile field.
Stock prices change every day, and it’s easy for you to bank on a stock that’s doing well one day, only to see it drastically plummet the very next day. Consequently, traders are always looking for a better way to anticipate trends and inform investment decisions.
An AI-powered stock market prediction app is one of the best data analytics project ideas for students, as it is very applicable in this trillion-dollar industry.
Basically, you’ll be building a program around an LSTM neural network using various data points from stock market websites to power your model . Afterward, you can take real-world datasets about specific stock exchanges, which you can easily find online, and run them through your program to generate trading predictions.
Try out these amazing TensorFlow courses to start building your reliable stock market app today, and get other valuable data analysis project ideas for students. You’ll learn how to build a simple stock market app for day trading based on daily volume exchanges, among other key factors.
6. Credit Card Fraud Detection
Have you ever experienced credit card fraud?
There were over 459,297 cases of credit fraud in 2020, with the resulting losses reaching more than $28.65 billion worldwide according to The Nelson Report. With businesses of all kinds affected, prioritizing data analytics project topics to solve this could be highly rewarding.
If you take up a credit card fraud detection system, you’ll be able to enhance your portfolio to work for financial institutions .
The first step entails building a training database for your machine learning algorithm, involving information on fraud risk scores from sources like MicroBilt. Using this data, the model can predict the likelihood of a fraudulent transaction. In addition, your algorithm could work on conditional settings e.g. SSN matches, for instance, to verify transactions.
Here are some business analytics courses to enact these data analysis project ideas for students. One of them covers how you can analyze credit transactions to not only detect fraud but also use predictive analytics to anticipate buying patterns.
7. Weather Prediction App
The weather impacts every sphere of our lives today.
With inaccurate weather forecasts, farmers wouldn’t know how to adjust their farming techniques. What’s more, air traffic control teams would miss hazardous conditions, leading to unplanned redirects and even accidents.
A weather prediction app can help you solve problems for the transport, agricultural and military sectors. That is why it’s among the most in-demand data analytics project ideas.
First, you’ll need to find a reliable data source for your model. Some great and free options include OpenWeatherMap API, which collects meteorological data from more than 400,000 weather stations worldwide.
Panda is an excellent python library option that you could use for data processing and interacting with your database. Geolocation features can further help you provide app users with specific location-based information. If you’d like to get a move on your weather prediction mobile app, these Android app development courses will show you the ropes.
The classes mostly focus on Kotlin, which enables you to set up an application development environment with little to no coding background.
8. Sentiment Analyzer Project
Sentiment analysis offers a solid way for businesses to gauge brand perception.
This is becoming increasingly important given that customer experience (CX) is where most business competition is today. Findings from Gartner’s customer experience survey further establish this, given that 66% of marketers today believe CX is a huge differentiating factor.
A sentiment analyzer is among excellent data analytics final year project ideas , as you can appeal to many businesses that prioritize the customer experience.
You can build a rule-based system that uses natural language processing techniques like parts-of-speech tagging and tokenization to identify negative words in textual data. Alternatively, you can save yourself the work of crafting rules by using machine learning classifiers, which work on text feature extraction to identify tone.
To get a clearer idea of how to make it all work, these deep learning tutorials can surely prove useful.
They cover key concepts on recursive neural networks, which offer a great building block for your sentiment analysis model.
9. Flight Delay Prediction Model
Flight delays impact everyone involved in the industry.
The US Department of Transportation estimates that close to 20% of flights were delayed in 2022. For passengers, it means not getting to appointments on time. The consequences for airlines, on the other hand, are tainted brand reputation and churn while air traffic controllers have to rework complex data systems to fit new schedules.
An online analytical processing (OLAP) cube is an excellent option to base your flight delay production model on and solve these challenges.
OLAP enables you to consider multiple data sources and clean the data in a single warehouse.
Speaking of which, Apache Spark is an excellent option for data cleaning in this case and you could use readily available Amadeus flight status datasets as your source. You can then build a qualitative prediction model on AWS SageMaker, for example.
To get a better understanding of Sagemaker, these AWS courses can be of huge assistance. Amazon Web Services supports many top educational and government agencies today, and these classes include many data analytics project ideas for beginners to improve your job market appeal.
10. Market Basket Analysis
For a retailer, convincing your customer to make an additional purchase is far from easy.
A lot of variables factor into the equation, from preferences to time of day. Without proper data analytics, it’s hard to recommend a product that appeals to your market. In fact, it is the case today that 51% of customers who receive product recommendations end up not buying suggested items.
A market basket analysis is among excellent data analysis project ideas to resolve this by personalizing recommendations to ignite sales.
This market basket analysis project is basically a product recommendation system that provides buyers with additional sales offers they’re likely to act on. It works on historical purchase data about previous buyers, then recommends items to your new buyer based on similar patterns , mostly using If-Then, probabilistic scenario rules.
For instance, if most of your customers buy product A then B, any time a purchase occurs for A, your conditional algorithm automatically recommends B.
To get a head start on this project, check out these SQL courses on Udemy. You’ll get to learn marketing analytics, involving product abandonment so you can better create an effective product recommendation system.
Conclusion
Are you ready to start your data analytics project?
A lack of clarity is typically the enemy when pondering the way forward for your project .
If you have little idea about your end product, you may find yourself starting on a project and abandoning it midway when it becomes unattainable. With these proven data analytics project ideas for final year students, you can make a successful project to impress your examiners and even prospective employers.
To build up underlying knowledge to power through your data analytics project, consider these incredible algorithm and data structure courses .
These classes contain easy approaches to analyzing and developing various algorithms. They also offer valuable pointers on what and how to use various algorithm-building resources.
Lerma is our expert in online education with over a decade of experience. Specializing in e-learning and e-courses. She has reviewed several online training courses and enjoys reviewing e-learning platforms for individuals and organizations.
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5 Data Analytics Projects for Beginners
Build a job-ready portfolio with these five beginner-friendly data analysis projects.
If you’re getting ready to launch a new career as a data analyst , chances are you’ve encountered an age-old dilemma. Job listings ask for experience, but how do you get experience if you’re looking for your first data analyst job?
This is where your portfolio comes in. The projects you include in your portfolio demonstrate your skills and experience—even if it’s not from a previous data analytics job—to hiring managers and interviewers. Populating your portfolio with the right projects can go a long way toward building confidence that you’re the right person for the job, even without previous work experience.
In this article, we’ll discuss five types of projects you should include in your data analytics portfolio , especially if you’re just starting out. You’ll see some examples of how these projects are presented in real portfolios, and find a list of public data sets you can use to start completing projects.
Tip: When you’re just starting out, think in terms of “mini projects.” A portfolio project doesn’t need to feature a complete analysis end-to-end. Instead, complete smaller projects based on individual data analytics skills or steps in the data analysis process .
Data analysis project ideas
As an aspiring data analyst, you’ll want to demonstrate a few key skills in your portfolio. These data analytics project ideas reflect the tasks often fundamental to many data analyst roles.
1. Web scraping
While you’ll find no shortage of excellent (and free) public data sets on the internet, you might want to show prospective employers that you’re able to find and scrape your own data as well. Plus, knowing how to scrape web data means you can find and use data sets that match your interests, regardless of whether or not they’ve already been compiled.
If you know some Python , you can use tools like Beautiful Soup or Scrapy to crawl the web for interesting data. If you don’t know how to code, don’t worry. You’ll also find several tools that automate the process (many offer a free trial), like Octoparse or ParseHub.
If you’re unsure where to start, here are some websites with interesting data options to inspire your project:
Job portals
Tip: Anytime you’re scraping data from the internet, remember to respect and abide by each website’s terms of service. Limit your scraping activities so as not to overwhelm a company’s servers, and always cite your sources when you present your data findings in your portfolio.
Example web scraping project: Todd W. Schneider of Wedding Crunchers scraped some 60,000 New York Times wedding announcements published from 1981 to 2016 to measure the frequency of specific phrases.
2. Data cleaning
A significant part of your role as a data analyst is cleaning data to make it ready to analyze. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and making sure the formatting of data is consistent.
As you look for a data set to practice cleaning, look for one that includes multiple files gathered from multiple sources without much curation. Some sites where you can find “dirty” data sets to work with include:
/r/datasets
Example data cleaning project: This Medium article outlines how data analyst Raahim Khan cleaned a set of daily-updated statistics on trending YouTube videos.
3. Exploratory data analysis (EDA)
Data analysis is all about answering questions with data. Exploratory data analysis, or EDA for short, helps you explore what questions to ask. This could be done separate from or in conjunction with data cleaning. Either way, you’ll want to accomplish the following during these early investigations.
Ask lots of questions about the data.
Discover the underlying structure of the data.
Look for trends, patterns, and anomalies in the data.
Test hypotheses and validate assumptions about the data.
Think about what problems you could potentially solve with the data.
Example exploratory data analysis project: This data analyst took an existing dataset on American universities in 2013 from Kaggle and used it to explore what makes students prefer one university over another .
10 free public datasets for EDA
An EDA project is an excellent time to take advantage of the wealth of public datasets available online. Here are 10 fun and free datasets to get you started in your explorations.
1. National Centers for Environmental Information : Dig into the world’s largest provider of weather and climate data.
2. World Happiness Report 2021 : What makes the world’s happiest countries so happy?
3. NASA : If you’re interested in space and earth science, see what you can find among the tens of thousands of public datasets made available by NASA.
4. US Census : Learn more about the people and economy of the United States with the latest census data from 2020.
5. FBI Crime Data Explorer (CDE) : Explore crime data collected by more than 18,000 law enforcement agencies.
6. World Health Organization COVID-19 Dashboard : Track the latest coronavirus numbers by country or WHO region.
7. Latest Netflix Data : This Kaggle dataset (updated in April 2021) includes movie data broken down into 26 attributes.
8. Google Books Ngram : Download the raw data from the Google Books Ngram to explore phrase trends in books published from 1960 to 2015.
9. NYC Open Data : Discover New York City through its many publicly available datasets on topics like the Central Park squirrel population to motor vehicle collisions.
10. Yelp Open Dataset : See what you can find while exploring this collection of Yelp user reviews, check ins, and business attributes.
4. Sentiment analysis
Sentiment analysis, typically performed on textual data, is a technique in natural language processing (NLP) for determining whether data is neutral, positive, or negative. It may also be used to detect a particular emotion based on a list of words and their corresponding emotions (known as a lexicon).
This type of analysis works well with public review sites and social media platforms, where people are likely to offer public opinions on various subjects.
To get started exploring what people feel about a certain topic, you can start with sites like:
Amazon (product reviews)
Rotten Tomato (movie reviews)
Example sentiment analysis project: This blog post on Towards Data Science explores the use of linguistic markers in Tweets to help diagnose depression.
5. Data visualization
Humans are visual creatures. This makes data visualization a powerful tool for transforming data into a compelling story to encourage action. Great visualizations are not only fun to create, they also have the power to make your portfolio look beautiful.
Example data visualization project: Data analyst Hannah Yan Han visualizes the skill level required for 60 different sports to find out which is toughest.
Five free data visualization tools
You don’t need to pay for advanced visualization software to start creating stellar visuals either. These are just a few of the free visualization tools you can use to start telling a story with data:
1. Tableau Public: Tableau ranks among the most popular visualization tools. Use the free version to transform spreadsheets or files into interactive visualizations ( here are some examples from April 2021).
2. Google Charts: This gallery of interactive charts and data visualization tools makes it easy to embed visualizations within your portfolio using HTML and JavaScript code. A robust Guides section walks you through the creation process.
3. Datawrapper: Copy and paste your data from a spreadsheet or upload a CSV file to generate charts, maps, or tables—no coding required. The free version allows you to create unlimited visualizations to export as PNG files.
4. D3 (Data-Driven Documents): With a bit of technical know-how, you can do a ton with this JavaScript library.
5. RAW Graphs: This open source web app makes it easy to turn spreadsheets or CSV files into a range of chart types that might otherwise be difficult to produce. The app even provides sample data sets for you to experiment with.
Bonus: End-to-end project
There’s nothing wrong with populating your portfolio with mini projects highlighting individual skills. But if you’ve scraped the web for your own data, you might also consider using that same data to complete an end-to-end project. To do this, take the data you scraped and apply the main steps of data analysis to it—clean, analyze, and interpret.
This can show a potential employer that you not only have the essential skills of a data analyst but that you know how they fit together.
Three data analysis projects you can complete today
There’s a lot of data out there, and a lot you can do with it. Trying to figure out where to start can be overwhelming. If you need a little direction for your next project, consider one of these data analysis Guided Projects on Coursera that you can complete in under two hours. Each includes split-screen video instruction, and you don’t have to download or own any special software.
1. Exploratory Data Analysis with Python and Pandas : Apply EDA techniques to any table of data using Python.
2. Twitter Sentiment Analysis Tutorial : Clean thousands of tweets and use them to predict whether a customer is happy or not.
3. COVID19 Data Visualization Using Python : Visualize the global spread of COVID-19 using Python, Plotly, and a real data set.
Next steps: Get started in data analysis
Another great way to build some portfolio-ready projects is through a project-based online course. By completing the Google Data Analytics Professional Certificate on Coursera, you can complete hands-on projects and a case study to share with potential employers.
professional certificate
Google Data Analytics
This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.
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Average time: 6 month(s)
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Skills you'll build:
Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study
Frequently asked questions (FAQ)
What are some books on data analytics for beginners .
There are many great books for those just starting out in data analytics. The following three books, in particular, offer accessible introductions to key aspects of field: Data Analytics Made Accessible by Dr. Anil Maheshwari Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo Python for Everybody: Exploring Data in Python 3 by Dr. Charles Russell Severance
To supplement their reading, beginners may also consider taking the online Python for Everybody Specialization offered by the University of Michigan and taught by Dr. Severance himself.
What is data visualization?
Data visualization is the process of graphically representing data through visual means. Common forms of data visualization include the use of graphs, charts, and diagrams to visually represent otherwise abstract data sets. Today, data visualization is considered a key skill in the world of data analytics.
What skills do data analysts need?
Beginning data analysts should make sure they have a solid technical understanding of Structured Query Language (SQL), Microsoft Excel, and either R or Python. Additionally, they should be able to think critically, present confidently, and know how to tell their data’s story visually. Read more about these and other key data analyst skills .
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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