11 Data Analytics Projects for Every Level

Data Analytics Projects for Every Level

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Data analytics projects are great for practicing and showcasing your analytics skills. Here are project suggestions for every level and resources to use.

Every aspiring data analyst reaches a point of realization that theory is not enough. Usually, that moment comes when you get to a job interview and realize employers want to see your practical skills. Of which you have none if you’re fresh from university and/or don’t have work experience in the field.

To get experience, you need a job. To get a job, you need experience. That sounds like a catch-22. Or it would’ve sounded like that if there weren’t a solution: data analytics projects. Good real-world projects demonstrate your ability to clean, analyze, and visualize data. That way, you can get practical analytics skills even before you get an actual job.

In this article, you’ll find projects for every skill level, which will help you build a portfolio that stands out.

Why Data Analytics Projects Are Crucial

Having fancy academic credentials and a dozen certifications from online courses is a good guarantee of your technical knowledge. However, that alone won’t get you hired.

Employers want candidates who have those skills but also know how and when to apply them to solve real-world business problems.

Data analytics projects will get you the closest to that. You can see them as a simulation of business problems you’ll face in your actual work.

A strong portfolio of projects proves your ability to handle messy data, draw meaningful insights, and create visualizations. All that you’ll – surprise, surprise! – do when you get hired.

Additionally, projects are a great method of preparing for job interviews; you practice your skills and have real examples to discuss. Instead of theoretical answers, you can confidently share concrete problems you faced in your projects.

On a side note, the projects we’ll give below are not exhaustive. Feel free to find similar data analytics project ideas.

Beginner-Level Projects

These three projects will build your foundational data analysis skills.

Beginner Level Data Analytics Projects


For additional ideas, check the beginner data science project list.

1. Data Cleaning With Excel

Why Is This Important? Data cleaning is one of the initial steps in the data analysis process. This step ensures data accuracy and consistency, which directly impacts the quality and the accuracy of the insights you’ll derive from data.

Excel is a tool commonly used in businesses, as it has numerous features for data cleaning.

Project Link: Excel for Library Projects

Project Description: This project is one part of the four-part education for library workers. But, it will be useful to you, too, as the part I linked will teach you some Excel functions for data cleaning and re-structuring.

You’ll work with data from SCImago, a research group at the University of Granada in Spain. After learning how to import data, you’ll clean data using functions such as LEFT(), RIGHT(), CONCAT(), CLEAN(), TRIM(), and IF(). You’ll also learn basic Power Query.

2. Simple Sales Dashboard in Tableau

Why Is This Important?: Tableau is one of the most popular BI software for data analysis and visualization (charts, dashboards, reports). So, from one side, the project teaches you how to work with the tool you’ll probably encounter at your company.

Conversely, you will learn data visualization and dashboard creation even if your company doesn't use Tableau. No matter the tool, this is an essential skill, as visualizing data helps uncover trends and make decisions based on that.

Project link: Sports Sales Dashboard in Tableau

Project Description: This is a YouTube project with the dataset in the video description. The purpose of the project is to analyze the 2023 sales trend to optimize for 2024. You’ll learn how to:

  • Connect to and prepare datasets
  • Create KPIs, bar charts, maps, and time-series visualizations
  • Use color coding and formatting for better insights
  • Create an interactive dashboard with filters
  • Apply best practices for dashboard storytelling and presentation

3. Exploratory Data Analysis (EDA) With Python

Why Is This Important?: EDA is a set of techniques for analyzing, summarizing, and visualizing datasets. Its purpose is to understand main data features, such as distribution, patterns, and anomalies.

Python, with libraries such as pandas, NumPy, and Matplotlib, is a powerful tool for EDA. Even if you don’t plan to use Python for anything else, using it for EDA is a very valuable skill that employers appreciate.

Project Link: Insights from City Supply and Demand Data

Project Description: This project appeared in the Uber interviews. It provides you with a dataset and eleven questions you must answer using EDA. Some of the tasks you’ll perform are:

  • Forward filling empty data
  • Data aggregation
  • Create timestamps
  • Calculate rolling sums
  • Parse time interval
  • Calculate the percentage
  • Calculate the weighted average ratio
  • Visualize data
  • Find minimums and maximums

Intermediate-Level Projects

Once you have the basics in place, you can move on to intermediate projects. They build on what you already know and showcase more complex aspects of data manipulation, analysis, and visualization.

Intermediate Level Data Analytics Projects

1. SQL Query Practice

Why Is This Important? In most data jobs, SQL is an essential tool for extracting and managing data in relational databases. No data, no analysis, so you have to be proficient in writing complex SQL queries. Some examples are retrieving data, performing aggregations, joining tables, and performing various calculations.

Project Link: Answering Business Questions using SQL (Chinook Online Music Store)

Project Description: This project puts you in the data analyst role and asks you to answer business questions. To answer them, you’ll have to apply SQL techniques such as subqueries, JOINs, set operations, and aggregate functions. You can also see an example solution code here and expand your analysis based on it.

2. Customer Segmentation Analysis

Why Is This Important?: Customer or market segmentation is a technique of dividing customers into groups based on their common characteristics. Customer segment analysis allows businesses to tailor marketing strategies to different segments with the goal of improving customer retention and overall profitability.

Project Link: Customer Personality Analysis & Segmentation

Project Description: This project uses customer data to group them into four personality clusters or segments. You’ll perform basic dataset exploration, create various visualizations, and perform dimensionality reduction.

3. Time Series Analysis

Why Is This Important? Analyzing “data points collected over an interval of time” (or data points) is essential for understanding current trends (e.g., seasonality, and cycles), predicting future trends, detecting anomalies, and optimizing operations.

Project Link: Insights From Failed Orders

Project Description: In this take-home assignment by Gett (previously known as GetTaxi), you will investigate some matching metrics for orders that were not completed successfully. You will analyze time series data to determine why the taxi orders failed.

4. Interactive Dashboards

Why Is This Important? Data visualization is one of the essential skills for data analysts. By being proficient in building interactive dashboards, you’ll enable stakeholders to explore data trends, patterns, and insights in real-time on their own. (And leave you alone, at least for some time.)

Project Link: Heart Failure Interactive Dashboard

Project Description: In this project, you will build an interactive dashboard in Tableau using heart failure clinical records. You will use data visualizations such as donut charts, scatter plots, histograms, and box plots.

Advanced-Level Projects

The projects on this final-boss level will build more sophisticated skills that will make you enter the world of machine learning projects, AI projects, and data science.

Advanced Level Data Analytics Projects

1. Predictive Analytics With Machine Learning

Why Is This Important? Predictive analytics use historical data to predict future outcomes, which allows companies to move into the proactive decision-making direction. Models that can make predictions run on the machine learning algorithms.

Project Link: Predicting Price

Project Description: In this take-home assignment by Haensel AMS, you will build a basic ML model for price prediction. You’ll need to do some initial data analysis, fit some ML models, comment on the steps, and present the model results. If you follow the official solution, you’ll learn regression models, model evaluation techniques, grid search in random forest, and data scaling.

2. Sentiment Analysis

Why Is This Important?: Sentiment analysis is a method of determining the emotional tone (sentiment) of a text. It is extensively used to understand customer opinions and perceptions from customer feedback, surveys, and product reviews. Sentiment analysis requires applying natural language processing (NLP) techniques, as the unstructured text data is transformed into insights.

Project Link: Amazon Reviews Classification

Project Description: In this project, you will classify the sentiment of Amazon customer reviews. You will use NLTK’s Vader and Hugging Face RoBERTa models.

3. Big Data Analysis

Why Is This Important?: Analyzing big data means drawing conclusions and insights from data that is too big and diverse to be analyzed in a traditional way. That means you have to be proficient in big data tools and techniques.

Project Link: Yelp Dataset Analysis Using Scalable Big Data

Project Description: This project uses Yelp public dataset to “provide descriptive analytics to understand business performance, geo-spatial distribution for businesses, reviewers’ rating and other characteristics, and temporal distribution of check-ins in business premises”. Yes, this is a sentiment analysis project, but using big data. The authors used Oracle Big Data Cloud Service, but you can also use some alternatives, such as AWS Redshift, IBM Db2, Databricks Data Lakehouse, or Snowflake.

4. End-to-End Analytics Pipeline

Why Is This Important?: End-to-end analytics pipelines allow you to automate the complete data workflow, from data collection, storage, processing, analysis, and visualization. If you show a project where you demonstrate these skills, you’re setting yourself up for handling real-world business projects that involve continuous data updates.

Project Link: Reddit Data Pipeline Engineering

Project Description: With this project you will learn how to create an ETL process using tools such as Apache Airflow, Celery, PostgreSQL, AWS S3, AWS Glue, AWS Athena, and AWS Redshift.

Showcasing Your Projects

Having quality projects in your portfolio is not enough; knowing how to showcase them is equally important. The idea is not only to show your project’s outcome, but also your ideas behind the project, problem-solving approach, and techniques used.

How To Showcase Data Analytics Projects

1. Create a Portfolio Website

Having your personal portfolio website allows you to present your projects in a structured and visually highly customizable way. Here are some examples you can look up to.

Generally, the idea is to include a brief bio, a list of your (best) projects with descriptions, and links to GitHub repositories and/or live dashboards.

To create a personal website, use platforms like GitHub Pages, Wix, WordPress, Ghost, or Jekyll.

2. Use GitHub for Version Control and Documentation

Uploading your projects to GitHub gives a structured view of your code. You can organize your projects in repositories nicely by having a README file (explaining the project), code scripts with detailed comments, and sample datasets.

3. Write Articles

A great way of showing your project is to write an article about it. By doing so, you can outline in detail the whole process, the problems you encountered, how you solved them, and what the project insights are.

Here are several suggestions on where to publish your articles:

You can even try to write guest articles for established data science and tech-focused platforms such as:

4. Share on Data Analytics and Data Science Communities

You should also engage with the data community.

There are several great blogging platforms that also give you the opportunity to benefit from their community.

In addition, you can share your projects and ask for feedback from other professionals.

5. Create an Interactive Dashboard

If your project employs data visualization tools like Tableau, Power BI, or Streamlit, you can publish dashboards for users to explore your findings. How to do that? You can host them for free on Tableau Public. For Python-based dashboards, host them on Heroku or Streamlit Community Cloud.

6. Present Your Work in Interviews

Being asked about your projects is a great opportunity to shine. Make sure you know all your relevant projects to the tiniest detail. Be ready to answer questions such as:

  • What problem were you trying to solve?
  • How did you clean and preprocess the data?
  • What challenges did you face, and how did you overcome them?
  • What insights did you gain, and how could they be used in real-world scenarios?

Don’t forget that the technical aspect is only a part of the project. Every project should attempt to solve a concrete problem; it’s the beginning and the end of the project, so make sure you think about that.

Resources to Get Started

It’s all great listing the projects to work on. They will be a good start. But, what if you want to be independent and work on some other projects not listed here? It can all get very confusing, especially if you’re only starting in data analytics. The question that many of you will ask is: Where do I start?

In this section, you will find several resource categories to get you started.

Resources for Starting Data Analytics Projects

1. Learning Platforms

Here are several platforms where you can learn essential data analytics skills you’ll need in your projects.

  • Coursera – Data analytics courses from universities and companies like Google and IBM.
  • Udacity – Offers nano-degree programs in data analysis and machine learning.
  • DataCamp – Great for interactive, beginner-friendly Python, SQL, and R courses.
  • Kaggle – Python, SQL, data analytics, data visualization, and ML courses, mainly suitable for beginners.

2. Free Online Tools and Software

Many data analytics tools are free or have free versions. Let’s split them into categories.

Programming and Data Processing

SQL and Database Management

  • PostgreSQL – Powerful, free, open-source relational database for structured data, (postgresql.org)
  • Microsoft SQL Server Express – Free version of SQL Server, ideal for learning and small-scale applications. (microsoft.com/sql-server)
  • SQLite – Lightweight and easy-to-use SQL database for local practice. (sqlite.org)
  • MySQL Community Edition – Free relational database for large-scale applications. (mysql.com)
  • BigQuery Sandbox – Google’s cloud data warehouse with a free tier for querying large datasets. (cloud.google.com/bigquery)
  • DBeaver – Free SQL database management tool that supports multiple databases, including PostgreSQL, MySQL, and SQL Server. (dbeaver.io)
  • Azure SQL Database Free Tier – Cloud-based SQL Server with free credits for developers. (azure.microsoft.com)

Data Visualization and BI Tools

  • Tableau Public – Free version of Tableau for creating interactive dashboards. (public.tableau.com)
  • Power BI Desktop – Free data visualization tool for Windows users. (powerbi.microsoft.com)
  • Looker Studio – Free cloud-based dashboard tool for creating interactive reports. (lookerstudio.google.com)
  • Metabase – Open-source BI tool for SQL-based analytics. (metabase.com)
  • Apache Superset – Open-source alternative to Power BI and Tableau for big data visualization. (superset.apache.org)

Big Data and Cloud Platforms

Machine Learning and AI Tools

Data Scraping and ETL (Extract, Transform, Load)

3. Finding Projects and Datasets

You can use some of these sites for finding real-world projects and datasets here:

  • StrataScratch – A platform with real-world Python-based projects sourced from companies, perfect for interview prep and analytics challenge. (platform.stratascratch.com/data-projects)
  • Kaggle – A massive collection of datasets on various topics, including finance, healthcare, and marketing. (kaggle.com/datasets)
  • Google Dataset Search – A search engine for publicly available datasets. (datasetsearch.research.google.com)
  • UCI Machine Learning Repository – A collection of datasets for machine learning and data science projects. (archive.ics.uci.edu)
  • Data.gov – Open datasets from the US government, covering economics, climate, health, and more. (data.gov)
  • FiveThirtyEight – Datasets on politics, sports, and culture, often used for storytelling and data journalism. (data.fivethirtyeight.com)

4. Communities and Forums for Help

Use the vastness and the experience of data science communities for learning and asking for advice:

Conclusion

This article equipped you with some valuable suggestions regarding data analytics projects. You got the list of eleven such projects for different experience levels.

You also learned the importance of doing data analytics projects and how to best showcase them to other users (read: interviewers).

Last but not least, there’s an extensive list of resources to get you started with the projects.

FAQs

1. What tools do I need to start working on data analytics projects?

Beginners can start with Excel, SQL, and Tableau or Power BI for basic analysis and visualization. For more advanced projects, use Python and libraries like pandas, NumPy, Matplotlib, and Seaborn. If you’re working with big data, you’ll need tools like Apache Spark and Google BigQuery.

2. Where can I find datasets for practice?

  • Kaggle
  • Google Dataset Search
  • UCI Machine Learning Repository
  • FiveThirtyEight

3. How long does it take to complete a data analytics project?

  • Beginner projects – 2 to 7 days
  • Intermediate projects – 1 to 3 weeks
  • Advanced projects – 3+ weeks

4. How can I showcase my projects to potential employers?

Use these methods to maximize the potential of your project portfolio.

  • Create a portfolio website with project descriptions
  • Upload well-documented GitHub repositories
  • Write blog posts on Medium or LinkedIn
  • Share dashboards on Tableau Public or Power BI
  • Engage in Kaggle competitions

5. Can I use open-source datasets in my portfolio?

Not only that you can (they’re open-source, after all!); it’s highly recommended! Most open datasets are free for use in personal projects. However, always check licensing terms if you plan to use them for commercial purposes.

6. What are some beginner-friendly project ideas?

  • Cleaning messy data in Excel
  • Building a simple sales dashboard in Tableau
  • Exploratory data analysis (EDA) on the Titanic dataset
  • Basic SQL queries on an e-commerce dataset

7. What makes a project “advanced”?

Advanced projects typically involve:

  • Handling large (millions of rows) datasets
  • Building ML models (predictive analytics, sentiment analysis)
  • Automating workflows with Apache Airflow or cloud-based tools
  • Integrating multiple data sources in an end-to-end analytics pipeline

8. How do I choose the right project for my skill level?

If you’re a beginner, focus on data cleaning, simple dashboards, and SQL queries. At the intermediate level, work on customer segmentation, time series analysis, and interactive dashboards.

For advanced level, try machine learning, big data processing, and end-to-end automation.

9. Can I collaborate with others on data analytics projects?

Again, not only that you can, but it’s also recommended to enhance your learning and be ready for team-based work. Here are some collaboration options:

  • Join Kaggle competitions and team up with others
  • Contribute to open-source analytics projects on GitHub
  • Participate in hackathons or data science meetups

10. How can I use these projects to prepare for job interviews?

These projects are chosen to cover most skills that you’ll need in data analytics and then some more. So, the primary way to prepare for job interviews is to complete these and similar projects. In addition, you should:

  • Prepare a short case study for each project, explaining your thought process
  • Practice answering technical interview questions based on your projects
  • Be ready to discuss challenges you faced and how you solved them
  • If possible, create interactive dashboards or live demos to showcase in interviews

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Published: March 18, 2025

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Data Analytics Projects for Every Level

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