7 Python Projects to Boost Your Data Science Portfolio

As a data scientist, you should be comfortable programming with Python. Besides learning to use the important Python libraries for data science, you should also work on your core Python skills. And what’s a better way to do it than working on interesting projects?

This article outlines seven Python projects—all related to data science tasks. You’ll use Python libraries and some built-in modules. But more importantly, working on these projects will help you improve your Python programming skills and learn the best practices along the way. Let’s get started.

1. Automated Data Cleaning Pipeline

 
Data cleaning, as you know it, is necessary but quite daunting, especially for real-world datasets. So try to build a data cleaning pipeline that automatically cleans raw datasets by handling missing values, formatting data, and detecting outliers.

What to focus on:

• Data manipulation: Applying transformations to clean datasets
• Error handling: Dealing with potential errors during the cleaning process
• Modular code design: Creating reusable functions for different cleaning tasks

In this project, you’ll predominantly use pandas for data manipulation and logging for recording the cleaning actions and errors.

2. A Simple ETL (Extract, Transform, Load) Pipeline

 
An ETL pipeline automates the extraction, transformation, and loading of data from various sources into a destination database. As a practice, work on a project that requires handling data from multiple formats and integrating it into a single source.

What to focus on:

• File I/O and APIs: Working with different file formats, fetching data from APIs
• Database management: Interfacing with databases using SQLAlchemy to manage data persistence
• Error handling: Implementing required error-handling mechanisms to ensure data integrity
• Scheduling: Automating the ETL process using cron jobs

This is a good warm-up project before moving to libraries like Airflow and Prefect for building such ETL pipelines.

3. Python Package for Data Profiling

 
Creating a Python package that performs data profiling allows you to analyze datasets for descriptive statistics and detect anomalies. This project is a great way to learn about package development and distribution in Python.

What to focus on:

• Package structuring: Organizing code into a reusable package
• Testing: Implementing unit tests to ensure the functionality of the package
• Documentation: Writing and maintaining documentation for users of the package
• Version control: Managing different versions of the package effectively

By working on this project, you’ll learn to build and publish Python packages, unit testing them for reliability, and improve them over time so other developers may find them useful as well!

4. CLI Tool for Generating Data Science Project Environments

 
Command-line tools can significantly improve productivity (this shouldn’t be a surprise). Data science projects typically require a specific folder structure—datasets, dependency files, and more. Try building a CLI tool that generates and organizes files for a new Python data science project—making the initial setup faster.

What to focus on:

• Command-Line Interface (CLI) development: Building user-friendly command-line interfaces with argparse, Typer, Click, and the like
• File system manipulation: Creating and organizing directories and files programmatically

Besides the tool you choose for CLI development, you may want to use the os and pathlib modules, the subprocess for executing shell commands, and the shutil modules as needed.

5. Pipeline for Automated Data Validation

 
Similar to a data cleaning pipeline, you can build an automated data validation pipeline that runs basic data quality checks. It should essentially check incoming data against predefined rules—checking for null values, unique values, value ranges, duplicate records, and more. It should also log any validation errors automatically.

What to focus on:

• Writing data validation functions: Creating functions that perform specific validation checks
• Building reusable pipeline elements: Using function composition or decorators to construct the validation process
• Logging and reporting: Generating logs and reports that summarize validation results

A basic version of this should help you run data quality checks across projects.

6. Performance Profiler for Python Functions

 
Develop a tool that profiles the performance of Python functions, measuring metrics such as memory usage and execution time. This should provide detailed reports about where performance bottlenecks occur.

What to focus on:

• Measuring execution time: Using the time or timeit to assess function performance
• Tracking memory usage: Utilizing tracemalloc or memory_profiler to monitor memory consumption
• Logging: Setting up custom logging of the performance data

This project will help you understand bottlenecks in existing Python code through profiling and explore performance optimization techniques.

7. Data Versioning Tool for Machine Learning Models

 
When working on machine learning projects, tracking changes to data is just as important as tracking changes to code. Data versioning

You can use tools like DVC for this, but it’s worth building one from scratch. So if you’re up for a weekend challenge, you can build a tool that tracks and manages different versions of datasets used for training models.

What to focus on:

• Data version control: Managing dataset versions
• File I/O: Working with different file formats
• Hashing: Implementing a hashing mechanism to uniquely identify dataset versions
• Database management: Storing and managing metadata about datasets in a database

In this project, you’ll have to explore a variety of built-in modules in the Python standard library.

Wrapping Up

 
I hope you found these project ideas helpful. As discussed, you can work on these projects and feature them on your data science portfolio.

Each project not only showcases your data science technical skills but also your ability to solve relevant real-world problems using Python.

Happy coding!
 
 

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.