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, 66% of data scientists reported using Python daily, making it the number one language for analytics professionals.
- In 2021, it overtook Java on the TIOBE index and is now the most popular programming language.
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 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:
— 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.
— A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.
— A Python library created specifically to facilitate working with data. This is the bread and butter of a lot of Python data science work.
— A visualization library that makes it quick and easy to generate charts from your data.
— 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
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).