Introduction to Python Basics for Data Science

In the first half of this section, we will introduce a lot of new ideas about what we mean by "data science". What is the process? What kinds of problems can data science solve?

By the end of the lesson you should be able to answer which technique you would use as a professional data scientist for a particular business problem.

Just as it's important to understand the kinds of problems that can be solved by data science, it's also important to have a sense of the process used to conduct data science.

Apart from being a snake, Python is your new best friend programmatically speaking.

Now that we know the importance of Python in Data Science, let's try to understand the tools that we need to use to run Python for Data Science tasks.

Comments are important not only for you but also for whoever reads your code. Let's see how to implement them.

Yeap. Python too has Maths. But it's more fun in code though a little confusing ... if you are not careful!

What can we do without them? You'll get used to them so much and forget they're called Variables.

Let's speak in code!

If you’re learning Python from multiple sources, you might encounter the terms data structures and data types being used interchangeably.

Strings are basically words or text. Let's see how to work with them in Python.

Not your normal shopping list but something similar ...

Tuple like turtle but very important in Python.

I know what you are thinking, but it's not that kind of dictionary!

Functions are super important, useful and fun to learn. Let's go!

Don't confuse functions and methods!

Creating your own functions.

All methods are functions, but not all functions are methods!

You'll need this!

Functions in action.