12 Best Business Analytics Project Ideas for Your Resume in 2024

The job market is brutal now, although the outlook for 2024 generally looks positive. Regardless, tech jobs remain competitive, especially if you’re a recent graduate with limited work experience or trying to break into analytics for the first time.

Perhaps you’ve heard that adding business analytics projects to your CV will boost your chances of securing an interview at your dream company (it will!), and you’re wondering which projects will showcase your skills best. We’ve been listening to feedback from Interview Query members and have written an article to address precisely this.

This article lists the best projects to demonstrate your analytical skills and knowledge of business analytics tools and concepts. Adding these to your resume and portfolio will significantly boost your chances of getting better responses from your job hunt.

If you often wonder why business analysts undertake projects, these could be the most likely answers as to why:

1. Showcases your hands-on experience

Employers are often concerned that their recruits lack business acumen or data-handling skills. Working on real-world data and business problems will give you insight into the challenges that various organizations face, and this hands-on expertise is invaluable to companies.

2. Demonstrates your skill sets and problem-solving abilities

Projects are a great example of “show, don’t tell.” Instead of simply stating that you’re skilled in Python and Power BI in your resume, you actually get to show how well you can apply these tools to solve problem statements. Further, you can build the project in a way that communicates your critical thinking to potential recruiters.

3. Shows your initiative

Companies highly value employees who demonstrate initiative and leadership. By adding projects to your resume, particularly ones relevant to the role or industry you are applying for, you are communicating that you can make decisions independently and have the discipline to navigate a complex problem statement.

Including a project on which you’ve worked with others will set you apart even more by showing that you are a team player.

Now that we understand the value of adding business analytics projects to your resume, let’s move on to the next section, where we’ll examine the most popular ones.

What Are the Best Business Analyst Projects to Do Right Now?

Certain tools are needed in your analyst toolkit, namely SQL, Python, and a data visualization tool like Power BI or Tableau. Apart from these, different industries face common business problems in domains like tech, retail, finance, and e-commerce. We analyzed these issues and chose the following projects for an excellent analyst portfolio.

1. Market Basket Analysis

There is a fascinating story about the correlation between beer and diaper sales from a Midwestern retailer in the 90s. The legend goes that when the beer section was placed closer to the diapers section, the sale of beer went up. While this may be a classic example of correlation not equalling causation, it does go to show that market basket analysis is seen as a profitable venture in the retail industry.

• Objective: Identify products frequently purchased together to optimize store layout and improve cross-selling strategies.
• How to build: Use SQL to extract transaction data and apply the Apriori algorithm for association rule mining. This will help you identify which products tend to be bought in the same transaction.
• Tools: SQL, R, or Python.

2. Customer Buying Behavior Analysis

This is another popular retail problem, although the use case can be extended to any industry. Customer buying behavior analysis helps the company define and focus on its ideal customers. It makes it easier for a business to modify products according to the specific needs of its target demographic(s).

• Objective: Define the ideal customer(s) and understand their purchase patterns.
• How to Build: Analyze customer demographics and transaction history to segment customers based on buying behavior. Use clustering techniques to categorize them.
• Tools: SQL and Python with libraries like scikit-learn for clustering.

Here is our takehome project on a related business problem: analyzing user behavior for Coda.

3. Sales Forecasting with Python

Forecasting and predictive modeling are necessary skills in almost every business function, making this an evergreen topic for business analytics.

• Objective: Predict future sales based on historical data to aid inventory planning.
• How to Build: Use time-series analysis with ARIMA or exponential smoothing models in Python to forecast sales. Consider external factors such as holidays and economic indicators that could impact sales.
• Tools: Python with statistical libraries like TensorFlow.

4. Sentiment Analysis of Customer Reviews

Business analysts are often tasked with analyzing quantitative feedback from users, particularly after new product launches. Analyzing customer sentiment helps businesses refine their products and optimize features.

• Objective: Gauge customer sentiment from reviews to understand pain points and refine product features.
• How to Build: Use natural language processing techniques to analyze sentiment and categorize reviews as positive, negative, or neutral. You can also implement text mining techniques and build word cloud visualizations to showcase topics that customers frequently mention.
• Tools: Python with NLP libraries like NLTK or spaCy and visualization with Matplotlib or Seaborn to display sentiment trends and word clouds. You can also build visualizations using Power BI or Tableau.

Read our sentiment analysis projects if you want to explore more.

5. Economic Impact of COVID-19

COVID-19 had a huge impact on various businesses like hotels, airlines, and aggregator platforms. An analysis like this is very topical as this is similar to situations when businesses ask their analysts to look into the impact of external factors such as competitors, catastrophes, and other events.

• Objective: Analyze the economic impact of COVID-19 across different sectors and predict recovery paths.
• How to Build: Use economic indicators, stock market data, and employment rates to analyze impacts and forecast recovery. Apply regression analysis to predict future economic conditions.
• Tools: Python/R for data analysis and forecasting.

6. Fraud Detection System

Virtually every industry needs sophisticated fraud detection systems, and this particular project is especially relevant for banking and financial firms.

• Objective: Develop a system to detect fraudulent transactions and minimize risk.
• How to Build: Focus on building a logical framework on what actions could be flagged as fraudulent, the weightage you would assign to each type of action, and a scoring system to categorize actions as potentially fraudulent. You may then explore building a model using logistic regression or anomaly detection algorithms like Isolation Forest.
• Tools: Python with scikit-learn.

Here is our takehome project on a similar business problem: detecting credit card fraud.

7. Price Optimization

Pricing analytics is a vital business problem for companies like Uber, for instance, as a balance needs to be struck between maximizing profits and ensuring customers—and drivers—are getting the best deal.

• Objective: Develop a model to optimize pricing to maximize revenue based on demand, such as predicting taxi fares.
• How to Build: Utilize historical sales data to model the price elasticity of demand. Employ regression to predict how price changes could affect bookings or customer satisfaction scores.
• Tools: Python for predictive modeling and data analysis, with libraries like pandas and scikit-learn.

Here is an interesting pricing problem for calculating electricity consumption.

8. Movie Recommendation System

If you’re looking for a fun exercise that also has tremendous value from a business lens, you can try building a movie recommender. Users have diverse tastes and interests, and companies like Netflix are always looking to fine-tune their systems for better engagement.

• Objective: Create a system that recommends movies to users based on their viewing history and preferences.
• How to Build: To start with, you can build the framework to recommend movies based on various factors like age, language preference, geographical location, and other factors you deem relevant. Use collaborative filtering techniques such as matrix factorization or deep learning models to predict user preferences.
• Tools: Python with libraries like TensorFlow or PyTorch for building recommendation algorithms.
• Dataset: The MovieLens dataset is a classic choice for building recommendation systems.

Here is an interesting takehome problem on recommending Airbnb homes to users.

9. Stock Market Analysis

This project is highly relevant for banking and financial firms, although you can extend the concepts to other business domains as well.

• Objective: Analyze historical stock data to identify investment opportunities.
• How to Build: Employ statistical analysis and time-series forecasting models like ARIMA to predict stock price movements.
• Tools: Python, with libraries such as NumPy, pandas, and Matplotlib for data manipulation and visualization.

Here is a list of more fintech projects you can try your hand at.

10. Life Expectancy Analysis

There is an excellent dataset from WHO that explores life expectancy patterns across the globe.

Some of the questions posed are:

1. What are the predicting variables affecting life expectancy in different countries?
2. Does life expectancy have a positive or negative correlation with eating habits, lifestyle, exercise, smoking, drinking alcohol, etc.?

As a business analyst, you will frequently be asked to look at complex datasets and present your findings to senior stakeholders so they can make strategic decisions. Hence, being skilled in descriptive analytics and visualization is a must.

• Objective: Explore factors affecting life expectancy across different countries to answer questions like “Should a country with a lower life expectancy value increase its healthcare expenditure?” and “What impact do immunization drives make on life expectancy?”
• How to Build: Use regression analysis to determine the impact of economic indicators, health expenditure, and education on life expectancy.
• Tools: Python or R for statistical analysis; Tableau or Power BI for visualizing the findings.

We’ve covered the life expectancy dataset in our main data science projects article—check it out if you want to learn more about other relevant projects.

11. Customer Churn Prediction

Customer churn is the percentage of customers who stopped using a company’s product or service during a specified period. This is a vital metric because retaining existing customers is more cost-effective than acquiring new ones.

• Objective: Predict which customers are likely to churn and suggest strategies to retain them.
• How to Build: Clearly define churn and benchmark churn thresholds based on the industry or product. Utilize logistic regression, decision trees, or ensemble methods like random forests to model churn. Explore multiple churn datasets here.
• Tools: SQL, Python, and/or R.
• Dataset: The Telco Customer Churn dataset on Kaggle is also very popular for churn prediction.

12. Marketing Mix Analysis

This is a favorite problem tackled by consulting firms like McKinsey, BCG, and Bain. A company’s marketing mix is the combination of products, pricing, places, and promotions it uses to differentiate itself from the competition. Conducting a successful marketing mix will help you recommend marketing optimization strategies to your business stakeholders and marketing teams.

• Objective: Imagine you are a part of the marketing team working on budget optimization. You need to develop a market mix model to observe the impact of different marketing variables. You are also asked to recommend the optimal budget allocation for different marketing levers for the next year.
• How to Build: Use regression models or multivariate analysis to understand the impact of each marketing channel on sales. This involves collecting data on marketing spend and corresponding sales figures across different channels and periods.
• Tools: SQL, Python, or R for statistical analysis; Tableau or Power BI for visualizing how changes in the marketing mix affect sales outcomes.

Explore our 13 top marketing analytics projects and datasets here.

What are some tips for including business analytics projects in my resume?

Here are our favorites:

• Write a clear title and description to outline the project’s purpose, the tools and techniques you used, and the problem it solved. Include a brief section on the key insights.
• Use action verbs like “developed,” “built,” “implemented,” or “analyzed” to increase persuasion.
• Wherever possible, quantify the impact of the project, for example, the model’s accuracy.
• Lastly, rehearse how you would present your project in an interview, an overlooked but crucial step in getting selected. On a related note, you can try a mock interview with us to test your current preparedness for a project presentation.

How can I source data for my business analytics projects?

You can use public datasets provided by Kaggle or UCI Machine Learning Repository or look into Interview Query’s storehouse of takehome assignments.

How do I start a business analytics project?

It’s normal to feel some resistance when starting a new project. To help you beat procrastination and get started, we’ve created a comprehensive guide on how to start a data analytics project.

The main questions to ask yourself are “What problem are you trying to solve?” and “What outcome are you hoping to achieve through the project?”

To wrap up, incorporating business analytics projects into your resume is a clever strategy to showcase your hands-on experience and set you apart in a competitive job market. Plan your interview strategy, considering the perspective of your desired future employer and tailoring your project selection to the skills they want to see.

If you’re looking for tool-specific project ideas, we have curated lists on SQL, Python, and data visualization projects. For an exhaustive list of ideas, you can also check out these master lists of data analytics projects and datasets. Our blog has even more articles on different types of projects and datasets relevant to data-related roles.

Lastly, you can explore our premium learning paths, which are tailored to help job candidates like yourself. However, that decision is completely yours.

We know you will land that dream job with hard work, good planning, and confidence, and we wish you all the best on your journey!