How to Learn Python for Data Science in 5 Steps (2024)

Many students also find it helpful to create a Kaggle account and to join a local Meetup group.

If you’re a Dataquest subscriber, you get access to Dataquest’s learner community, where you’ll find access to support from both current students and alums.

Step 2: Practice with hands-on learning

One of the best ways to accelerate your education is through hands-on learning.

Practice with Python projects

It may surprise you how quickly you catch on when you build small Python projects. Fortunately, virtually every Dataquest course contains a project to enhance your learning. Here are a few of them:

• Prison Break — Have some fun, and analyze a dataset of helicopter prison escapes using Python and Jupyter Notebook.
• Profitable App Profiles for the App Store and Google Play Markets — In this guided project, you’ll work as a data analyst for a company that builds mobile apps. You’ll use Python to provide value through practical data analysis.
• Exploring Hacker News Posts — Work with a dataset of submissions to Hacker News, a popular technology site.
• Exploring eBay Car Sales Data — Use Python to work with a scraped dataset of used cars from eBay Kleinanzeigen, a classifieds section of the German eBay website.

This article also has tons of other Python project ideas for beginners:

• Build a rock, paper, scissors game
• Build a text adventure game
• Build a guessing game
• Build interactive Mad Libs

Alternative ways to practice and learn

To enhance your coursework and find answers to the Python programming problems you encounter, read guidebooks, blog posts, Python tutorials, or other people’s open-source code for new ideas.

If you still want more, check out this article on different ways to learn Python for data science.

Step 3: Learn Python data science libraries

The four most-important Python libraries are NumPy, Pandas, Matplotlib, and Scikit-learn.

• NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.
• pandas — A Python library created specifically to facilitate working with data. This is the bread and butter of a lot of Python data science work.
• Matplotlib — A visualization library that makes it quick and easy to generate charts from your data.
• Scikit-learn — The most popular library for machine learning work in Python.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets.

Here’s a helpful guide to the 15 most important Python libraries for data science.

Step 4: Build a data science portfolio as you learn Python

For aspiring data scientists, a portfolio is a necessity — it’s one of the most important things hiring managers look for in a qualified candidate.

These projects should include work with several different datasets, and each should share interesting insights that you discovered. Here are some types of projects to consider:

• Data Cleaning Project — Any project that involves dirty or “unstructured” data that you clean up and analyze will impress potential employers, since most real-world data requires cleaning.
• Data Visualization Project — Making attractive, easy-to-read visualizations is both a programming and a design challenge, but if you can do it well, your analysis will be considerably more useful. Having great-looking charts in a project will make your portfolio stand out.
• Machine Learning Project — If you aspire to work as a data scientist, you will definitely need a project that shows off your ML skills. You may want a few different machine learning projects, with each focused on a different algorithm.

Present your portfolio effectively

Your analysis should be clear and easy to read — ideally in a format like a Jupyter Notebook so a technical audience can read your code. (Non-technical readers can follow along with your charts and written explanations.)

Does your portfolio need a theme?

Your portfolio doesn’t necessarily need a particular theme. Find datasets that interest you, then develop a way to link them. If you want to work at a particular company or in a particular industry, showcasing projects relevant to that industry is a great idea.

Displaying projects like these demonstrates to future employers that you’ve taken the time to learn Python and other important programming skills.

Step 5: Apply advanced data science techniques

Finally, improve your skills. Your data science journey will be full of constant learning, but there are advanced Python courses you can complete to ensure you’ve covered all the bases.

Learn to be comfortable with regression, classification, and k-means clustering models. You can also step into machine learning by studying bootstrapping models and creating neural networks using Scikit-learn.

Helpful Python Learning Tips for Beginners

Ask questions

You don’t know what you don’t know!

Python has a rich community of experts who are willing to help you as you learn data science with Python. Resources like Quora, Stack Overflow, and Dataquest’s learner community are full of people excited to share their knowledge and help you learn Python programming. We also have an FAQ for each lesson to help with questions you encounter throughout your programming courses with Dataquest.

Use Git for version control

Git is a popular tool that helps you keep track of changes to your code. This makes it much easier to correct mistakes, experiment, and collaborate with others.

Learn beginner and intermediate statistics

While learning Python for data science, you’ll want to develop a solid background in statistics. Understanding statistics will give you the mindset you need to focus effectively to find valuable insights (and real solutions).

Start learning Jupyter Notebook

Jupyter Notebook is an incredibly important tool, which you should start learning right away. It comes prepackaged with Python libraries, which is helpful.

Python for Data Science FAQs

How long will it take to learn Python?

While everyone is different, we’ve found that it takes three months to a year of consistent practice to learn Python for data science.

We’ve seen people move through our courses at lightning speed, and we’ve seen others who have taken a slower pace. It all depends on how much time you can dedicate to learning Python programming — and how quickly you can pick up new information.

Fortunately, we’ve designed Dataquest’s courses for you to go at your own speed.

Each path is full of lessons, hands-on learning, and opportunities to ask questions so you can master data science fundamentals. Our hands-on learning method uses real-life datasets, which will not only helps you learn faster but also helps you see how to apply your knowledge.

Get started for free. Learn Python with our Data Scientist path, and start mastering a new skill today!

Where can I learn Python for data science?

Because Python is used in a variety of other programming disciplines, from game development to mobile apps, generic “learn Python” resources try to teach a bit of everything, but this means you’ll be learning things that are irrelevant to data science.

When your main objective is to learn Python for data analysis and instead you’re struggling through a course that’s teaching you to build a game, it’s easy to become frustrated and want to quit.

There are many free Python for data science tutorials out there. If you don’t want to pay to learn Python, these can be a good option. This link provides dozens of tutorials sorted by difficulty level and area of focus.

If you want to maximize your learning, it may be best to find a platform that offers a curriculum developed for data science education. Dataquest is one such platform. We have courses that can take you from beginner to job-ready as a data analyst, data scientist, or data engineer in Python.

Is Python Necessary in the data science field?

It’s possible to work as a data scientist using either Python or R. Each language has its strengths and weaknesses. Both are widely used in the industry. Python is more popular overall, but R dominates in some industries (particularly in academia and research).

For data science, you’ll definitely need to learn at least one of these two languages. (You’ll also have to learn some SQL, no matter which language you choose.)

Is Python better than R for data science?

This is a constant topic of discussion in data science, but the true answer is that it depends on what you’re looking for and what you like.

R was built specifically for statistics and mathematics, but there are some amazing packages that make it incredibly easy to use for data science. Additionally, it has a very supportive online community.

Python is a better all-around programming language. Your Python skills are transferable to many other disciplines. It’s also slightly more popular. Some would argue that it’s easier to learn, although plenty of R folks would disagree.

Rather than reading opinions, check out this article about how Python and R handle similar data science tasks, and see which one looks more appealing to you.