How to Learn Python for Data Science in 5 Steps
Why learn Python for data science?
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.
- In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
- In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most-used tools.
- In 2018, 6
- In 2021, it overtook Java on the TIOBE index and is now the most popular programming language.
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 2022 is $119,118.
That number is only expected to rise as demand for data scientists increases. In 2020, there were three times as many open positions for data scientists as the year before.
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
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 3
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. One of the best ways to accelerate your education is through hands-on learning. 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: This article also has tons of other Python project ideas for beginners: 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. The four most-important Python libraries are NumPy, Pandas, Matplotlib, and Scikit-learn. 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. 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: 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.) 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. 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. 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. 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. 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). Jupyter Notebook is an incredibly important tool, which you should start learning right away. It comes prepackaged with Python libraries, which is helpful. 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! 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. 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.) 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.Step 2: Practice with hands-on learning
Practice with Python projects
Alternative ways to practice and learn
Step 3: Learn Python data science libraries
Step 4: Build a data science portfolio as you learn Python
Present your portfolio effectively
Does your portfolio need a theme?
Step 5: Apply advanced data science techniques
Helpful Python Learning Tips for Beginners
Ask questions
Use Git for version control
Learn beginner and intermediate statistics
Start learning Jupyter Notebook
Python for Data Science FAQs
How long will it take to learn Python?
Where can I learn Python for data science?
Is Python Necessary in the data science field?
Is Python better than R for data science?