Learning Python for Data Science the Right Way
The new course is available now, and just like the introductory Python course, it’s completely free.
This course has been carefully designed to build and expand upon the skills you learned in the first course. Together, these two courses will give you the Python foundation you need to start diving into some serious data science, and working with time-saving packages like
In the Intermediate course, you’ll build on what you learned about strings, doing some light data cleaning and analysis, and combining columns. Then you’ll dig into Python’s object-oriented nature and learn about classes – what they are, why they’re useful, and how to create them. From there, you’ll take a closer look at dates and times in Python, which will help reinforce what you just learned about classes. Then it’s on to learning about regular expressions and applying some intermediate looping techniques before putting everything together with a project analyzing Hacker News posts to see what types are most successful.
By the end of the course, you will feel confident downloading a CSV data set, opening it, and performing some basic data cleaning and data analysis tasks in a “pure Python” style. You will also have developed a conceptual understanding of key concepts like object-oriented programming, and you’ll be comfortable checking Python documentation to find the answers to your own questions.
These new Fundamentals and Intermediate courses have been carefully designed to work together, with the idea of making data science in Python even more accessible to people with no backgrounds in programming.
For that reason, we’ve placed a strong emphasis on building conceptual understanding and improving the explanations throughout both courses. We want students to be able both understand and apply these concepts, because they’re are the fundamental building blocks that will underlie a lot of what you do in subsequent courses (and in your career as a data scientist). The new courses are designed to introduce you to the basic “what, why, and how” of each concept and then let you build and expand that understanding through practical application.
Since our previous versions of these courses were in place for a long time, we also had a lot of data about where students usually got stuck or fell off the wagon that we used to make big improvements. These new courses eliminate those sticking points with better concept explanations and a reworked sequence that flows better, so each topic builds naturally on the one that came before.
If you work through the missions, by the end of Intermediate, you’re going to feel comfortable working in Python, and you’ll be ready to dive into more advanced topics and start learning about the most commonly-used packages in Python data science in our pandas course (which comes next in our Data Analyst and Data Scientist paths).
But how can you keep yourself on track and get those most out of these courses? Here are a few tips:
Set a study schedule and stick to it. Research suggests that you’re a lot more likely to meet your goals if you make specific plans about how you’re going to achieve them. Choose a duration, time, and place you’re going to study each week, and have a back-up plan for how you’ll make it up if you have to miss a session.
Ask for help. Premium subscribers (we’re having a sale, by the way) get access to our top-notch help and support, and our Members Slack is full of peers that will help you, too. But even if you’re not a paying member, you’re welcome to reach out to support when you get stuck. Plus, there’s the Q&A feature on our site, and a whole plethora of data science communities and resources spread out all over the web (try searching code-related questions on StackOverflow, for example). Everybody gets stuck from time to time, and there’s no shame in asking for help!
Do the guided projects. If you want to make really fast progress, it can be tempting to skip these, but don’t. They’re important for helping you synthesize everything you’ve learned and put it together on your own, in the context of real-world data science problems. Plus, when you’re ready to start looking for data science jobs (sooner than you think!) you’ll want to already have some projects finished that you can consider including in your portfolio.
Remember that failure is part of the learning process. These courses are designed to be as beginner-friendly as possible, but that doesn’t mean they won’t challenge you. There will be concepts you just don’t get at first, and code screens you can’t seem to get right. It’s easy to get disheartened by this, but remember, these moments of struggle are an important part of the learning process, and everyone goes through them.
Charlie is a student of data science, and also a content marketer at Dataquest.