The following topics will be covered as part of this series. Each topic is described in detail with hands-on exercises done on Jupyter Notebook to help students learn with ease. We will cover all the nitty-gritty that you need to know to get started with Python along with the correction and handling of errors as and when they pop-up. The program builds a solid foundation by covering the most popular and widely used data science technologies and its applications.
Introduction to Python
Data Structures and Conditional Executions in Python
Conditions and Loops in Python
Working with Pandas in Python
Plotting in Python
Statistical Analysis and Application in Python (part I)
Statistical Analysis and Application in Python (part II)
Theory of Factor and Cluster Analysis in Python
Building a Predictive Model (Linear Regression) in Python
Building a Predictive Model (Logistic Regression) in Python
Time Series theory and its application in Python.
From this course students will have a clear understanding about the data science theory, techniques that are applied in analytics and also its application in Jupyter notebook.
For better understanding knowledge on Python Programming is recommended.