Hi, dear learning aspirants welcome to “Ultimate Python Bootcamp For Data Science & Machine Learning ” from beginner to advanced level. We love programming. Python is one of the most popular programming languages in today’s technical world. Python offers both object-oriented and structural programming features. Hence, we are interested in data analysis with Pandas in this course.

This course is for those who are ready to take their data analysis skill to the next higher level with the Python data analysis toolkit, i.e. "Pandas".

This tutorial is designed for beginners and intermediates but that doesn't mean that we will not talk about the advanced stuff as well. Our approach of teaching in this tutorial is simple and straightforward, no complications are included to make bored Or lose concentration.

In this tutorial, I will be covering all the basic things you'll need to know about the 'Pandas' to become a data analyst or data scientist.

We are adopting a hands-on approach to learn things easily and comfortably. You will enjoy learning as well as the exercises to practice along with the real-life projects (The projects included are the part of large size research-oriented industry projects).

I think it is a wonderful platform and I got a wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasts.

What you will learn:

You will become a specialist in the following things while learning via this course

“Data Analysis With Pandas”.

• You will be able to analyze a large file

• Build a Solid Foundation in Data Analysis with Python

After completing the course you will have professional experience on;

• Pandas Data Structures: Series, DataFrame and Index Objects

• Essential Functionalities

• Data Handling

• Data Pre-processing

• Data Wrangling

• Data Grouping

• Data Aggregation

• Pivoting

• Working With Hierarchical Indexing

• Converting Data Types

• Time Series Analysis

• Advanced Pandas Features and much more with hands-on exercises and practice works.