Python Data Science Basics With Numpy, Pandas And Matplotlib

Description:

Welcome to my new course Python Essentials with Pandas and Numpy for Data Science

In this course, we will learn the basics of Python Data Structures and the most important Data Science libraries like Numpy and Pandas with step by step examples! The first session will be a theory session in which, we will have an introduction to python, its applications and the libraries.

In the next session, we will proceed with installing python in your computer. We will install and configure anaconda which is a platform you can use for quick and easy installation of python and its libraries. We will get ourselves familiar with Jupiter notebook, which is the IDE that we are using throughout this course for python coding. Then we will go ahead with the basic python data types like strings, numbers and its operations. We will deal with different types of ways to assign and access strings, string slicing, replacement, concatenation, formatting and f strings. Dealing with numbers, we will discuss the assignment, accessing and different operations with integers and floats. The operations include basic ones and also advanced ones like exponents. Also we will check the order of operations, increments and decrements, rounding values and type casting.

And after learning all of these basic data types and data structures, its time for us to proceed with the popular libraries for data-science in python. We will start with the NumPy library. We will check different ways to create a new NumPy array, reshaping , transforming list to arrays, zero arrays and one arrays, different array operations, array indexing, slicing, copying. we will also deal with creating and reshaping multi dimensional NumPy arrays, array transpose, and statistical operations like mean variance etc using NumPy

Most times, external data will be coming in either a CSV file or a JSON file. We will check how we can import CSV and JSON file data as a dataframe so that we can do the operations and later convert this data frame to either CSV and json objects and write it into the respective files.

Also we will see how we can concatenate, join and merge two pandas data frames. Then we will deal with data stacking and pivoting using the data frame and also to deal with duplicate values within the data-frame and to remove them selectively.

We can group data within a data-frame using group by methods for pandas data frame. We will check the steps we need to follow for grouping. Similarly we can do aggregation of data in the data-frame using different methods available and also using custom functions. We will also see other grouping techniques like Binning and bucketing based on data in the data-frame

Overall this course is a perfect starter pack for your long journey ahead with big data and machine learning. You will also be getting an experience certificate after the completion of the course(only if your learning platform supports)

So lets start with the lessons. See you soon in the class room.

Basic knowledge:

A decent configuration computer and the willingness to lay the corner stone for your big data journey

Data science enthusiasts who want to begin their career.

Course Completion Certificate

What will you learn:

• Essential Python data types and data structure basics with Libraries like NumPy and Pandas for Data Science or Machine Learning Beginner

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