8 Data Analytics project ideas for resume in 2024: Beginner to advanced

Building your data analytics portfolio can be a challenging task, especially if you're just starting out. The first step in building your data analytics portfolio will be starting to build some data analytics projects. You might feel like your projects need to be complex or intricate, but that's not true. Your main aim should be to demonstrate your skills and show off your expertise, and it's even better if you can use a dataset that you find interesting. The great thing is that data is all around us—you just need to know where to look and what to do with it.

This article will talk about 8 kinds of projects from beginner to advanced level that are good to have in your data analytics portfolio, especially if you're new to this. 

Here we'll discuss  tools, level, aim and description of the project and give you a list of public data sets you can use to start your own projects.

8 Data Analytics Project Ideas

1. Sales Analysis

Level: Beginner Level

Aim:

‍The goal of this project is to predict department-wide sales for 45 stores in the upcoming year, considering the impact of promotional markdowns during holiday weeks

Tools to be used:

• Excel, Python (pandas, Scikit-learn), tableau, Jupyter Notebook.

What you’ll Learn:

• Data cleaning, exploratory data analysis, predictive modelling, assessing the impact of markdowns, and deriving actionable business recommendations.

Description:

Explore retail data by looking at past sales. Clean and study the data, make models to predict future sales, and check how discounts during holidays affect things. Show you're really good at understanding what customers like and how they behave. Use Excel and Tableau to make cool charts that tell a story. Give smart suggestions to help the business do better. Improve your skills in using data to make good decisions in retail.

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2. Customer Segmentation 

Level: Beginner Level

Aim:

Sort customers into groups based on things like age, what they bought before, and more. This helps find the most valuable customers and plan marketing that suits them.

Tools to be used:

• Excel, Python(pandas), tableau.

What you’ll Learn:

• Understand customer data, organize it, and make groups. Learn who the important customers are and how to target them.

Description:

Explore data from an online store. Look at what people bought and figure out who the best customers are. Use Excel and a bit of Python to organize and analyze the data. Learn to group customers based on things like age and purchase history. The goal is to help the company make special ads for the customers who bring in the most business. Great for starting to understand how data can help in marketing!

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3. Marketing Campaign Analysis

Level: Beginner

Aim:

Analyze a social media ad campaign dataset to understand which factors influence ad conversions. Use cluster analysis to identify patterns in the data.

Tools to be used:

• Excel, Python (pandas, Scikit-learn), Jupyter Notebook.

What you’ll Learn:

• Learn how different factors like age, gender, interests, and campaign details affect ad conversions. Use cluster analysis to find groups with similar behaviors. Gain insights into optimizing marketing strategies for better conversion rates.

Description:

In this project you’ll explore a social media ad campaign dataset. You have to analyze factors affecting ad conversions like age, gender, and interests. Apply cluster analysis to find behavior patterns to Gain insights for optimizing future marketing efforts and Enhancing data analytics skills by extracting actionable insights from marketing data. This can help you optimize your marketing spend and improve your ROI.

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4. Inventory Management

Level: Beginner

Aim:

Explore inventory management using sample sales data. Learn to analyze, segment, and cluster data for better insights into sales and customer behavior.

Tools to be used:

• Excel, Python (pandas), Jupyter Notebook.

What you’ll Learn:

• Basics of inventory management analytics, customer segmentation, and clustering techniques. Gain hands-on experience in using data for sales simulation and training.

Description:

Learn about managing stuff in a store using a sample sales dataset. Use easy tools like Excel and Python to understand sales and how customers behave. Get the basics of inventory management and see how to group customers for better sales. Perfect for beginners who want to practice using data to make stores work better.

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5. Website Traffic Analysis

Level: Intermediate

Aim:

The goal of this project is to analyze user interactions on a news portal using a dataset from Globo.com. And to explore website traffic patterns and understand user behavior for contextual news recommendations.

Tools to be used:

• Python (pandas, NumPy), Jupyter Notebook, potentially deep learning frameworks for advanced analysis.

What you’ll Learn:

Gain insights into user behavior on a news portal, understand how contextual attributes influence interactions. Learn to work with large-scale datasets, utilize embeddings for content representation, and potentially apply advanced deep learning techniques.

Description:

You can analyze website traffic data to identify which pages are most popular and which pages are not. This can help you optimize your website for better user experience. Explore how people use a news website with data from Globo.com. Understand clicks and user sessions, covering 3 million clicks and 1 million sessions. Dive into CSV files, check metadata, and use embeddings for content representation. Learn about user behavior, website traffic patterns, and potential news recommendations. Gain hands-on experience with a real-world dataset for academic analysis.

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6. Customer Satisfaction Analysis

Level: Intermediate

Aim:

Analyze customer satisfaction data from Airbnb in Seattle using surveys, feedback forms, and reviews. Understand listing activity and trends.

Tools to be used:

• Excel, Python (pandas), Jupyter Notebook.

What you’ll Learn:

• Explore Airbnb data, analyze customer feedback, and understand trends in listing activity. Learn to use data for insights into neighbourhood vibes, peak visiting times, and overall growth in Airbnb presence.

Description:

Explore how people feel about staying in Airbnb homes in Seattle. Look at surveys, feedback, and reviews to see what guests and hosts say. Use tools like Excel and Python to understand things like the feel of different neighbourhoods, the busiest times for visits, and how Airbnb is growing in the city. Great for beginners wanting to understand how data can tell us about customer satisfaction and trends in the Airbnb world in Seattle.

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7. Customer Churn Data Analysis

Level: Advanced Level

Aim:

Use Python and SQL to deeply analyze customer data for predicting and reducing customer churn. Dive into purchase history, demographics, and contact info to develop targeted customer retention strategies.

Tools to be used:

• Python (pandas, Scikit-learn), SQL, Jupyter Notebook.

What you’ll Learn:

Advanced techniques in customer churn analysis, predictive modelling, and SQL database querying. Understand how to hold customer data for targeted retention programs and enhance decision-making.

Description:

This is an advanced project using Python and SQL to understand why customers might stop using a service. We'll dig into their data, like what they bought and their details. Learn advanced skills to predict and stop customers from leaving. Great if you want to get really good at understanding and keeping customers.

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8. Fraud Detection

Level: Advanced Level

Aim:

Create a sophisticated predictive model using advanced techniques to identify fraudulent transactions in banking and financial datasets.

Tools to be used:

Python (pandas, Scikit-learn), Jupyter Notebook, advanced machine learning algorithms.

What you’ll Learn:

Advanced skills in fraud detection, utilizing machine learning models for accurate predictions. Gain insights into building models that enhance security in banking and financial systems.

Description:

Advance your skills in fraud detection by building a powerful predictive model using Python and advanced machine learning. Dive into banking and finance datasets to identify fraudulent transactions quickly and accurately. Ideal for those aiming to master advanced techniques in enhancing security for financial systems.

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Conclusion

In conclusion, These data analytics projects, from predicting sales to fraud detection, cover all skill levels using tools like Excel and Python. They help you learn real-world skills like customer segmentation and predictive modelling. By working with actual data, you show your ability to find useful insights and tell a compelling story visually. These projects prove your expertise and make you valuable in the world of data analytics.