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Python Data Science Project Ideas

Wondering what your first-ever data science project or your first big project in Python should be? Or are you looking for your next data science project? This article will give you some ideas and directions.

Creating data science projects in Python is essential for your career development. It’s the best way to learn new data science tools, practice the skills you’ve acquired, and demonstrate your competencies to potential employers. Your ability to finish a big project on your own, without any external incentives, is also a good indication of your motivation to pursue a data science career.

So, where do you start? The first step is to define the project idea.

How to Choose a Data Science Project

Let’s start with some important things to consider when picking the topic of your next data science project in Python.

Create a project around your true interests. Working on something that genuinely interests you is a great source of motivation. So, while you are in a position to decide on the topic of your data science project, pick something you will enjoy working on. You can build your project around football statistics, blockchain technology, or Trump’s tweets. Just follow your interests!

Work on topics that are understandable to others. While you are free to choose any topic, I recommend avoiding topics that require deep domain knowledge to follow the project. With a data science project, you want to present your skills to a wide audience. That project on theoretical particle physics might not be the best idea.

Be creative and avoid common datasets. While commonly available toy datasets are great for practicing newly acquired Python skills, it’s better to choose more unique data for your project. It’s hard to impress somebody by solving a problem that has been already addressed by thousands of aspiring data scientists. On the other hand, choosing a non-trivial problem will help you stand out from the crowd.

Have a diverse portfolio. When thinking about your next Python project, keep in mind that your data science portfolio should demonstrate the diversity of your skills. For example, you may want to build projects that show your data visualization chops or your ability to work with time series, unstructured text data, images, etc. For more details, read my article on how to build a strong data science portfolio.

Ideas for Your Next Data Science Project in Python

As we discussed, the goal of your data science project is usually to demonstrate the skills you have in the field. So, I’ve grouped my data science project ideas based on the competencies you may want to showcase. If you want to build a strong and diverse data science portfolio, these are some directions to consider:

  1. Exploratory data analysis (EDA). Every data science project starts with exploring the dataset. Thus, demonstrating your skills with exploratory data analysis can be a good idea for one of your first data science projects. Python has several key libraries that can assist you with EDA. Use pandas and NumPy to prepare summary statistics for your dataset. Use matplotlib and seaborn to build histograms, scatter plots, and other visualizations that will help you understand your data better and identify possible outliers. The topic of your EDA project can be just about anything, such as analyzing your customer data or exploring crime statistics in your city.
  2. Data visualizations like histograms and scatter plots are often part of an exploratory data analysis. However, you may also have visualization-focused projects, where more advanced plots are a key outcome. For example, you may build a heatmap demonstrating how audience engagement with your social media posts varies depending on the day of the week and the time you post. A more advanced project might be to visualize climate change data with Python.
  3. Tabular data analysis. In the business world, lots of data arrives in tables. Thus, one of your first data science projects should demonstrate your ability to work with tabular data using Python. There are many popular datasets with tabular data; one interesting option is Titanic, where you are asked to predict which passengers survived the sinking of the Titanic based on key attributes. You may prefer to search for a more ‘businesslike’ dataset. Depending on the data you can get, you could build your project around predicting a product category based on its attributes, making loan decisions based on applicants’ credit history and other characteristics, or classifying inbox messages as spam or non-spam based on their sender, subject line, and other attributes.
  4. Time series forecasting. You are very likely to encounter time series prediction problems in the business setting and beyond. When working with time-series data, you’ll need to use a variety of classical and machine learning forecasting methods. At the very minimum, you should be familiar with autoregression (AR), moving average (MA), and autoregressive moving average (ARMA). Luckily, Python has tools like the statsmodels library that are very helpful for predicting time series. To demonstrate your ability to deal with this kind of data, you may want to tackle a project on forecasting cryptocurrency prices, future sales, GDP and inflation, weather, web traffic, etc.
  5. Text data analysis. The vast majority of real-world data is stored in an unstructured format, but this shouldn’t be an obstacle for a good data scientist. Python provides many tools for capturing and processing unstructured data. To show your skills with processing unstructured textual data, consider building a project around discovering the most frequent words in Reuters articles, classifying tweets as normal or offensive, summarizing long documents into brief paragraphs, or answering questions based on information found in a set of documents.
  6. Sentiment analysis. One of the most frequent business applications of text data analysis is analyzing customer reviews using sentiment analysis. This type of text research allows for classifying customer feedback as positive, neutral, or negative (in general or with respect to specific product attributes such as price, quality, location). I am featuring this type of project separately, as it requires the application of more advanced machine learning tools. Still, Python makes sentiment analysis pretty straightforward. You may start by analyzing Amazon reviews for any product – books, video games, laptops, Lego kits, etc.
  7. Anomaly detection. This is yet another data science topic that you may want to cover with your next Python data science project. It is also quite common in the business world. For example, you may create a project on identifying fraudulent credit card transactions, detecting defective products in manufacturing, or classifying astronomical objects – which, despite being named like a classification problem, actually turns out to be another anomaly detection problem.
  8. Image classification. For a more advanced data science project, consider an image classification problem. State-of-the-art machine learning models help Google classify your images in Google Photos and assist Pinterest in suggesting relevant pictures based on your search and view histories. Building this kind of model requires lots of training data and computational resources, but you can start with simpler projects, like recognizing hand-written digits, detecting pneumonia based on chest X-ray images, or classifying images based on the depicted scene.

Of course, this isn’t all there is to a data science project – there are a lot more things to do after you’ve picked a topic. Read this beginner’s guide to Python data science projects to learn about the other essential steps for building a worthwhile project.

Time to Practice Your Python Skills!

There are many exciting data science projects that are best approached with Python. Python is easy to learn, has a rich selection of libraries, and helps you create production-ready data science models. If you haven’t started learning this programming language yet, it’s a good idea to learn Python in 2021.

To master the skills necessary for a data science career, I recommend starting with the interactive Introduction to Python for Data Science course. It includes 141 coding challenges covering Python basics, the processing of tabular data, data visualizations, and other topics.

If you also want to learn how to process CSV, Excel, JSON files, and text data in Python, consider taking our Python for Data Science track. It includes five interactive courses that have a total of 329 coding challenges. That’s gonna be lots of fun!

Bonus. Read this article to learn Python tips and tricks that every data scientist should know.

Thanks for reading, and happy learning!