Mastering Python: 0/1 -> 1/0 | Beginner to Advanced

Course Curriculum

Chapter 1: Course Introduction and Installation

Lecture 1: Course Glimpse

Lecture 2: Installing Python

Chapter 2: Basics of Programming

Lecture 1: Introduction to Python Programming

Lecture 2: Recap – Basics of Programming

Chapter 3: Lists and Tuples

Lecture 1: Introduction to Lists and Indexing

Lecture 2: List Functions

Lecture 3: List Cloning and Nested Lists

Lecture 4: Tuples

Lecture 5: Recap – Lists and Tuples

Chapter 4: Dictionaries and Sets

Lecture 1: Dictionary and its Functions

Lecture 2: Dictionaries and Sets

Lecture 3: Comparison and Logical Operators

Lecture 4: Recap – Dictionaries and Sets

Chapter 5: Conditional Statements

Lecture 1: If-Else Block Statements

Lecture 2: If-Else – Business Case – ATM Machine

Lecture 3: Recap – Conditional Statements

Chapter 6: Looping in Python – Basics to Advanced

Lecture 1: For Loop – Direct and Indirect Referencing

Lecture 2: For Loop – Break and Continue Keywords

Lecture 3: For Loop – Problem Solving – I

Lecture 4: For Loop – Problem Solving – II

Lecture 5: Data Structure Comprehensions

Lecture 6: While Loop – Introduction & Problem Solving

Lecture 7: Recap – Looping in Python – Basics to Advanced

Chapter 7: Creating User Defined Functions

Lecture 1: User-Defined Functions

Lecture 2: Functions – Problem Solving Exercise

Lecture 3: Functions – *Args and **Kwargs

Lecture 4: Anonymous Functions

Lecture 5: Recap – Creating User Defined Functions

Chapter 8: Numpy Arrays – I

Lecture 1: Introduction to Numpy Arrays

Lecture 2: Numpy Array Functions – I

Lecture 3: Numpy Array Functions – II

Lecture 4: Indexing and Broadcasting of Arrays

Lecture 5: Recap – Numpy Arrays – I

Chapter 9: Numpy Arrays – II

Lecture 1: Mathematical Operations Using Arrays

Lecture 2: Numpy – Random Module

Lecture 3: Arrays vs Lists – Optimization

Lecture 4: Introduction to Linalg Module

Lecture 5: Recap – Numpy Arrays – II

Chapter 10: Pandas – Series

Lecture 1: Series – Introduction and Indexing

Lecture 2: Recap – Pandas – Series

Chapter 11: Pandas – Data Frames – I

Lecture 1: Data Frames – Introduction and Manipulation

Lecture 2: Indexing of Data Frames – I

Lecture 3: Indexing of Data Frames – II

Lecture 4: Filtering of Data Frames

Lecture 5: Recap – Pandas – Data Frames – I

Chapter 12: Pandas – Data Frames – II

Lecture 1: Functions Applied on Data Frames

Lecture 2: Importing Files and Exploratory Data Analysis (EDA)

Lecture 3: Identifying and Dealing with Missing Values

Lecture 4: Merging of Data Frames

Lecture 5: Concatenation of Data Frames

Lecture 6: Data Frames – Exporting Files and Miscellaneous Functions

Lecture 7: Recap – Pandas – Data Frames – II

Chapter 13: Handling Strings

Lecture 1: Introduction to Strings and its Functions – I

Lecture 2: String Functions and Conversion – II

Lecture 3: Recap – Handling Strings

Chapter 14: Dealing with Dates and Time

Lecture 1: Dates in Python

Lecture 2: Creating Date-time Objects and Conversion

Lecture 3: Time Package

Lecture 4: Date-time Objects Using Pandas

Lecture 5: Recap – Dealing with Dates and Time

Chapter 15: Data Visualization Using Matplotlib and Seaborn

Lecture 1: Introduction to Matplotlib

Lecture 2: Sin-Cosine Curves and Scatter Plot

Lecture 3: Subplots Using Pyplot

Lecture 4: Subplots Using Figure Objects

Lecture 5: Adding Axes to Figure Objects and Bar Charts

Lecture 6: Histogram and Box Plots

Lecture 7: Data Visualization with Seaborn

Lecture 8: Recap – Data Visualization Using Matplotlib and Seaborn

Lecture 9: End of the Course!

Lecture 10: End of Course! (Optional)