February 28, 2024

How to Learn Python for Data Science in 5 Steps

Why learn Python for data science?

Data professional juggling various programming languages like Python, C#, JavaScript, C++, and PHP.

Python is the programming language of choice for data scientists. Although it wasn’t the first primary programming language, its popularity has grown throughout the years.

What’s more, data science experts expect this trend to continue. 

What does the current labor market look like for data scientists? 

According to Glassdoor, the average salary for a data scientist in 2024 is $123,902.

The U.S. Bureau of Labor Statistics expects a 35% growth in data scientist jobs from 2022 to 2032, faster than the average for all occupations. Approximately 17,700 openings for data scientists are projected yearly over the decade. According to ZipRecruiter, salaries for the top data scientist roles can reach up to $322,500 annually. 

The future appears very bright for data science and Python. Fortunately, learning Python is now easier than ever. We’ll show you how in five simple steps.

How to Learn Python for Data Science

Illustration of a woman with a Python programming mind map, symbolizing concept analysis in data science.

Step 1: Learn Python fundamentals

Everyone starts somewhere. This first step is to learn Python programming basics. (You’ll also want an introduction to data science if you’re not already familiar.)

You can do this with an online course (which Dataquest offers), data science bootcamps, self-directed learning, or university programs. There is no right or wrong way to learn the Python basics. The key is to choose a path and stay consistent.

Find an online community

For help staying motivated, join an online community. Most communities allow you to learn with questions that you or others ask the group. 

You can also connect with other community members and build relationships with industry professionals. This also increases your opportunities for employment, as employee referrals account for 30% of all hires.

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:

  • 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:

    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

    Graphic of Python code leading to structured data blocks, representing data organization

    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.)

    Do I Need Python for AI?

    Yes, Python is highly recommended for artificial intelligence (AI) development. It is the preferred language in the field due to its simplicity and the powerful suite of libraries tailored for AI tasks, such as TensorFlow, PyTorch, and Scikit-learn. Python's syntax is user-friendly, which makes it accessible for beginners, yet it's robust enough for complex AI projects. Whether you're implementing machine learning algorithms or developing deep learning models, Python provides the tools that can help you succeed in AI.

    If you want to build the skills necessary for working with AI, from automating tasks to engaging with LLMs via API in Python, and progress to building AI-driven applications ,then check out our Generative AI skill path.

    Charlie Custer

    About the author

    Charlie Custer

    Charlie is a student of data science, and also a content marketer at Dataquest. In his free time, he's learning to mountain bike and making videos about it.