We will prepare you to use Python for Data Science.
We start by illustrating Python programming fundamentals. You will learn about variables, data types, data structures (lists, sets, tuples, dictionaries), decision and looping structures, and functions.
Next, I have a whole section on how to work with nested data, nested iteration and list comprehension. These are more advanced topics that build on the fundamentals.
We then turn to working with libraries that are built on top of 'pure' or 'base' Python and are used for data analysis, data manipulation, and data science. These libraries were designed to help make data science work easier and more flexible. You will work with Numpy and Pandas.
Who this course is for:
- You want to learn from the very beginning. You want to first understand Python (standard/base) and then build from there by learning the libraries relevant for Data Science.
- Beginners to Python
- Beginners to using Python for Data Science
- How to create and work with variables, data structures, looping structures, decision structures, and functions.
- How to work with nested data, iterate through nested data, and utilize list comprehension for transformation and filtration.
- Numpy: create and work with arrays.
- Pandas: create and work with the two main data structures: Series and DataFrame. You will focus on the second and see how to use it for data manipulation and data science.
- We will start at the very beginning for your Python learning journey.. You will first learn Python (standard) and then we will look at libraries that are relevant for data science.