I hope you are excited to start this journey of learning python for data analysis.
By the end of this course, you be able to comfortably manipulate and visualize your data.
I just want a commitment from you that you will attempt all the 13 quizzes that are distributed across each of the modules and also solve the Final assignment honestly. Why ? Because the more you practice the better you code.
Why you should take this course :
It’s Memorable: You’ll learn the “why” behind everything you do, so you remember the concepts and can use them on your own later.
It’s the Perfect Length: The course is just 6.5 hours long, so you’ll actually be able to finish it and get your certificate.
It Goes at the Perfect Pace: You will learn the Python fundamentals at a pace tailored to beginners. This means you won’t get left behind, and won’t waste time on irrelevant filler.
It’s Practical: You actually use Pandas to manipulate data. It’s not just dry theory. You can see you’ve understood by solving Quizzes at the end of each section. There is a mega coding assignment to give you a hands-on flavour and make you more confident in this skill. Over time i will keep on adding more coding assignment for your practice.
Now, let's have a look at the course outline.
We will start by laying some foundation with the below lectures :
* Basic Introduction to Python.
* Numpy Package, which forms the foundation of Pandas Package.
Then, we will learn about :
* Data Types in Python to store collections of data.
Then we will start with the following :
* Create a Dataframe in Pandas from different file formats.
* Data Selection and Filtering.
* Merging and aggregations which form the backbone of the data frame analysis.
* Working with Datetime using pandas.
* Advance Data Manipulation.
* Loops and Functions.
* Data Visualization.
* Assignment - Work Fitbit User Activity data.
Who this course is for:
* Anyone who is looking to learn python for data analysis
1. Getting Started
2. Introduction To Python
4. Data Types In Python
5. Creating Dataframes In Pandas
6. Data Selection And Manipulation
7. Merging Dataframes In Pandas
9. Working With Datetime In Pandas
10. Advance Data Manipulated
11. Iterating Over Pandas Dataframes
12. User Defined Functions
13. Working with String Columns
14. Coding Assignment
15. Data visualizations Using Matplotlib
I am a Data Scientist with over 7 years of experience in python, data analysis, machine learning, and more. I have worked with various fortune 500 clients in various domains such as retail, insurance, and banking and helped them take data-driven decisions.