In this section, you'll learn about one of the core topics required for data science: coding! You'll get exposed to the fundamentals of Python as well as a brief introduction to data visualization.

We start this section by looking at the kinds of problems that data science can solve and by giving a sense of the end to end process of "doing data science". We then get you set up with a professional data science environment with Git, GitHub, the command line, and the Anaconda Python distribution so you can start using industry standard tools from day 1.

We also run you through the workflow that we'll be using for the projects so you can get comfortable with how to access and update content and get some practice using the tools we just set you up with! And then to bring it all together, we ask you to "code along" with us to complete your first (very simple) data science project.

If you're new to Python (or programming), you may be a little overwhelmed at this point. Don't worry. We'll spend all of the rest of the course practicing and deepening your understanding of the materials we introduced in the first part of the section.

Next, we introduce some of the most important elements of programming in Python. We start from the basics, but move fairly quickly. We cover variables, common data types, conditionals, lists, and dictionaries. This may not all seem immediately relevant to data science, but it's designed to give you the tools that you'll need for all of your future projects in Python.

Finally, we take a little bit of time to introduce you to Matplotlib - one of the most popular tools in Python for generating visualizations.

You will also use some simple datasets to generate bar graphs, histograms, and scatter plots.

Remember, it's OK to feel a little uncomfortable. For some students, this section will be the most difficult as it introduces so many new concepts at once, but you'll continue to practice these day after day, until they become second nature. For those of you who already know some of these contents, we've introduced additional challenges for students who are finding the labs easy, so look out for the "Level Up (optional)" section in some of the labs to get some deeper practice with Python and visualization.