7 Best Business Analytics Projects for Your Resume (Updated for 2024)

Expected to exceed $23 billion in 2032, analytics as a service (AaaS) and business analysis are essentially about extracting patterns—and ultimately monetary value—from user data. As a skilled business analyst, your job is to improve decision-making, analyze new products, enhance customer experience, optimize processes, and manage risk in more sensitive cases.

When approaching a business analytics interview, you must recognize that your responsibilities will include understanding the business, identifying requirements, communicating, and sometimes, making presentations and managing.

These responsibilities are only given to candidates who demonstrate competence with existing business analytics projects.

However, we understand how challenging it is to find and get started on an analysis project to add to your resume. So, we’ve done the hard part for you.

We’ve compiled the 5 best business analytics projects with the most detailed and realistic datasets.

What is this project about?

Cross-selling and upselling are powerful tools used in retail to maximize sales potential and increase customer lifetime value. Market basket analysis strives to identify distinct patterns of items frequently purchased together.

Retail companies, such as Target and Best Buy, and e-commerce industries, like Amazon, often apply this type of analysis.

This particular project uses Instacart’s anonymized data on customer orders over time to allow you to predict which previously purchased products will be in a user’s next order.

The project involves taking account of customer demographics, order history, and product information to build a machine-learning model that accurately predicts patterns.

Examples:

As a business analyst, you aim to optimize the company’s revenue and profit potential. For example, you might identify that customers often purchase butter and bread together. With this insight, the store could place these items together or offer bundle discounts to increase sales.

Further, by analyzing which products are often bought together, the store can optimize inventory levels and product placement to ensure availability and minimize stockouts.

Why is it a must for your resume?

Successful completion of the project will highlight your proficiency in data manipulation, advanced data mining, and data visualization techniques. The specific skills will depend on your chosen tools, but SQL, pandas, Apriori, and Power BI/Tableau will likely be involved.

Mastering these skills is essential for extracting valuable insights from data, which can then be applied to enhance product placement, promotions, and overall sales—all outcomes that companies like Amazon and Best Buy highly value.

What is this project about?

Sales forecasting leverages past sales data, market trends, and economic indicators to estimate future sales for a specific period, such as a month, quarter, or year.

Accurate sales forecasting can help businesses make better profits, optimize resource allocation, improve inventory management, and set realistic sales targets.

This Python project dataset, sourced from a retail store, expects you to build and evaluate models to predict national store sales by holidays and department, focusing mainly on the seasonal nature of the business.

Example:

An example of the project’s significance could involve an automotive company like Ford, which can successfully leverage sales forecasting to predict future demand for a particular car model, optimizing production schedules and reducing unsold units.

Another industry where sales forecasting is heavily used is insurance sales forecasting, where companies promote insurance policies based on demographic trends, the economy, and regulatory changes.

For instance, favorable regulatory changes can cause an upsurge in young drivers, which may increase car insurance sales.

Why is it a must for your resume?

As sales forecasting is key to every industry, finding room for this project in your resume can prove significant.

This project allows you to showcase expertise in time series analysis and forecasting using statistical models, such as ARIMA and machine learning.

It highlights your ability to transform historical data into accurate predictions, improving business efficiency and decision-making. This skill set is highly valuable for roles requiring strategic forecasting and analysis throughout the market.

What is this project about?

Understanding human sentiments is critical to industries that rely heavily on customer feedback, preferences, and behavior to recommend products or improve their services.

Sentiment Analysis of Customer Reviews focuses on determining the emotional tone behind text using natural language processing, whether positive, negative, or neutral.

This project utilizes user ratings for various products to create a model to suggest items to users based on their purchase history and preferences. You’re expected to build a model using item-to-item collaborative filtering, a technique commonly used by e-commerce platforms like Amazon.

Example:

Apart from the example cited by Amazon on the business analytics project, you could consider a telecommunication company, such as Verizon. They might segment the customers based on their usage and satisfaction patterns to recommend personalized offers.

Social media platforms like Facebook or Twitter also use sentiment analysis to track public opinion on various topics, including brands, products, and events. This gives them leverage to recommend relevant content.

Why is it a must for your resume?

Sentiment analysis is essential in customer-facing products such as Netflix, Spotify, and YouTube that strive to deliver personalized feeds to cater to each user.

This project showcases your text mining, web scraping, and data processing skills with Python and pandas, along with expertise in machine learning for natural language processing.

It also allows you to demonstrate your ability to manage unstructured data from various sources, extract insights, and provide actionable recommendations.

What is this project about?

Pricing is a considerable factor in today’s competitive markets with global players. Price optimization analytics uses data and analytical techniques to identify a model that dynamically adjusts the price of a product to maximize revenue and profit.

This ClearSpark takehome challenge expects you to create a recommendation engine that involves handling missing values, processing data for analysis, understanding patterns, and building a recommendation model to facilitate the recommendation algorithms.

Example:

One of the most prominent use cases of price optimization involves ride-sharing companies such as Uber and Lyft. They use dynamic pricing models to adjust fares based on demand, traffic, and supply.

Retailers like Amazon and Walmart also leverage vast amounts of data to adjust product prices in real time.

Airlines, for instance, dynamically adjust ticket prices based on factors like booking time, demand, competition, and fuel costs.

Why is it a must for your resume?

Price optimization analytics uses regression analysis to study historical data and understand pricing patterns. This project shows your ability to perform detailed analysis and model pricing scenarios, combining analytical skills with business understanding.

It also demonstrates your ability to use data to influence decision-making, including setting prices based on customer segments and planning promotions. Tech companies and industries focusing on pricing strategies value this experience, as it helps maximize revenue and profitability.

What is this project about?

Life expectancy analysis is about studying and interpreting data related to the average lifespan of a population. By examining age, race, sex, income, education, access to healthcare, and various other factors, analysts can design models that can accurately describe the life expectancy of a demographic.

This project, while not directly correlating with business analytics, has you identify key factors influencing life expectancy, clean data, build statistical models, and evaluate the performance of the ML models. Data visualization of the outcomes can also significantly enhance your resume.

Example:

Life expectancy analysis’s most substantive industrial use revolves around determining insurance premiums and performing risk calculations against health insurance policy applications. Other examples include identifying potential target populations for new drugs and detecting regional and global health trends.

Apart from direct healthcare, non-profit organizations, and epidemiological studies may employ life expectancy analysis to identify areas of interest.

Why is it a must for your resume?

Completing this project showcases your skill in handling diverse datasets from various sources, such as mortality rates, health indicators, and demographics. It highlights your expertise in statistics and domain-specific models to analyze data, identify trends, and draw meaningful conclusions.

Moreover, it demonstrates your proficiency in data exploration, regression analysis, and predictive modeling, making you a valuable asset for strategic planning and policy-making through data-driven insights.

What is this project about?

This project involves analyzing resumes to identify patterns and trends in the skills and qualifications of individuals across various fields. By examining the resumes’ content, such as skills, education, and experience, we aim to categorize them into specific professional domains. The goal is to develop a model that automatically classifies resumes into categories like Data Science, Web Development, HR, etc.

The dataset provides textual data extracted from resumes along with their associated categories. This project challenges you to perform text preprocessing and feature extraction and build machine-learning models for text classification. Additionally, visualization of insights from this data can enrich your portfolio and highlight your analytical capabilities.

Example:

An industrial application of resume classification is in automating candidate shortlisting for recruitment. Companies can use similar models to filter resumes based on predefined job roles or categories, saving time and improving efficiency. Additionally, these models can be used to analyze market trends in skills demand and identify gaps in workforce capabilities.

Why is it a must for your resume?

Completing this project demonstrates your expertise in natural language processing (NLP), text data preprocessing, and machine learning for classification tasks. It highlights your ability to handle unstructured data, extract meaningful features, and build predictive models.

Moreover, this project showcases your text analytics, visualization, and practical problem-solving skills, making you a standout candidate for roles involving data science, machine learning, or talent analytics.

What is this project about?

This project analyzes McDonald’s financial performance over time using its financial statements. By examining key metrics such as revenue, earnings, market cap, and various financial ratios, we aim to uncover trends, patterns, and insights that can inform investment decisions and strategic planning. The dataset includes financial data for McDonald’s over 21 years, featuring details like operating margins, dividend yields, and total liabilities.

The dataset offers comprehensive financial information, providing an opportunity to perform time-series analysis, visualize trends, and create models to predict future financial performance. Additionally, it includes insightful metrics like the P/E ratio and dividend yield, which are crucial for evaluating company health and shareholder value.

Example:

This analysis could be an industrial application in building financial forecasting models to guide corporate decision-making. For instance, analysts could use similar data to predict earnings growth or evaluate how changes in market conditions impact financial metrics like operating margins or cash on hand. These insights are vital for stakeholders, including investors and executives, to make informed decisions.

Why is it a must for your resume?

Completing this project demonstrates your expertise in financial data analysis, time-series modeling, and visualization techniques. It showcases your ability to interpret and analyze structured datasets, draw meaningful conclusions, and present findings effectively.

Moreover, this project highlights your skills in financial modeling, strategic planning, and creating predictive insights, making you a strong candidate for financial analysis, corporate strategy, or data-driven investment consulting roles.

If you’re interested in expanding your resume with more business analytics projects or seeking machine learning project ideas, check out our articles. Still, if you can’t find a project that fits your interests, don’t hesitate to create your own, following the tips below:

Find Interesting Business Analyst Projects

Don’t stop at online projects!

Identify a question or problem you’re passionate about solving that addresses a common business need, possibly in the particular company you’re interested in.

By pursuing and completing a project relevant to a company’s challenges, you demonstrate how your skills are applicable in real-world scenarios, making your skills more relevant to potential employers.

Come Up with Creative Solutions

Once you’ve decided on a problem, think creatively about how to solve it. The project should highlight your unique approach and analytical skills. The more innovative your solution, the more likely it is to grab an employer’s attention.

For example, if you’re interested in real estate, you might consider projects to help a company decide which city to expand into by analyzing market demand, property prices, rental yields, and economic conditions.

Practice Takehome Projects

If you need more practice, we offer takehome projects that allow you to tackle real-world problems and sharpen your analytics, machine learning, and statistics skills.

Take on Volunteer Projects

Another great way to build your portfolio is by taking on volunteer projects. Non-profits and small businesses often need help with data analysis but don’t have the resources to hire full-time business analysts. You gain valuable experience and help a good cause by offering your skills.

And there you have it—the top 5 business analytics projects you can use to highlight your skills and boost your resume. Remember, every project you take on brings you one step closer to mastering the skills top employers seek. Showcasing these projects can set you apart in today’s competitive job market.