Skill Level Intermediate
- [Narrator] Over the past two decades, Python has emerged as an indispensable tool for scientific computing tasks. It is one of the easiest and most versatile programming languages in use today. The usefulness and credibility of Python for data science, stems primarily from the large inactive ecosystem of third party packages. Hi, I'm Harshit Tyagi, and in this course, we are going to learn tips and techniques for using Python in our Data Science workflow. We'll start by learning to work with iPads and notebooks, including accessing documentation, debugging errors, and profiling code. Then we'll learn to use scientific computing package, NumPy, to create and manipulate arrays. Then we'll dig into working with the data using the data manipulation package, Pandas. Will subsequently look at how to make insightful data visualizations using Matplotlib. Lastly, we'll dive into machine learning best practices with the psychic learn package. Note that you will need to have some familiarity with Jupiter notebooks to follow along with me in this course. But of course, the Python code can be executed in other dev environments as well. Alright, let's get right to it.