• What is Python?
• What is Python Used for?

• Downloading and Installing Jupyter Notebooks
• Jupyter Notebook UI Walkthrough

• Variables Introduction
• Variables + Expressions
• Assigning Multiple Values and Concatenation
• Variable Naming Best Practices

Slicing Variables

Data Types Introduction

• Numbers
• Boolean
• Strings
• Lists
• Tuples
• Sets
• Dictionaries
• Converting Data Types

• String Slicing
• Manipulating string by string functions
• Modifying numbers by number function

• Operators Introduction
• Comparison Operators
• Arithmetic Operators
• Logical Operators
• Writing the conditional statement using
• • IF….ELIF…ELSE
  • Nested If Else Statements

• For Loops
• Nested for Loops
• While Loops
• Break, Continue, Else, Pass
• Nested While Loops

• Functions Introduction
• First Function + Passing Arguments
• Default and Arbitrary Arguments
• Keyword and Arbitrary Keyword Arguments
• Lambda Functions
• Print vs Return in Functions

• Creating a File
• Reading, Writing, and Closing Files
• Writing and Appending in a File
• Creating a Folder
• Copying and Moving Files

• MS Excel Cells , rows, columns
• Data Calculations using Excel
• Using Formula, Functions & Similarity

• What is Pandas?
• What can Pandas be used for?

• Series and DataFrames Introduction
• Numpy Arrays and Pandas Series
• Pandas DataFrames

• Importing CSV Files
• Importing Different File Types
• Connecting to a Database
• Exporting Data

• Filtering on Columns
• Filtering on Rows
• Filtering using String Methods
• Filtering using Date Functions
• Ordering in Data Frames

• Indexing Introduction
• Creating an Index
• Multi-Level Indexing
• Reordering and Sorting Indexes
• Reshaping and Pivoting Indexes

• Group by and Aggregations Introduction
• Basics of Group By
• Average Revenue
• Aggregations with Group By
• Average Gaming Session
• Group By on Multiple Columns
• Transform with Group By

• Joins Introduction
• Basics of Merge and Join
• Merge Operations
• Merging Multiple Data Frames
• Concatenation

• Data Visualization Introduction
• Visualizing Data with Pandas
• Advanced Data Visualization

• Data Cleaning Introduction
• Dealing with Duplicates
• Duplicate Emails
• Standardizing Data
• TMI (Too Much Information)
• Splitting and Combining Columns
• Breaking Out Column
• Working with Null Values
• Dropping Columns and Rows

• Basic Project
• Advance Project