Python for Data Analytics

Instructor - Durgesh Yadav

Starting Date - 25 June 2025

Schedule:

• Friday: 9:45 PM – 10:45 PM
• Saturday & Sunday: 10:30 PM – 11:30 PM
• (Each session is 1.5 hours)

Session 1: Introduction to Python for Data Analytics

• What is Python?
• Why is Python popular in Data Analytics?
• Installing Anaconda, Jupyter Notebook, and VS Code
• Understanding IDEs and virtual environments
• Role of Python in the Data Analytics pipeline
• Hands-on: Writing your first Python program

Session 2: Python Basics – Variables, Data Types & Operators

• Variable declaration and naming conventions
• Data types: Integer, Float, String, Boolean
• Type conversion and casting
• Operators: Arithmetic, Comparison, Logical
• Input and Output functions

Session 3: Control Flow in Python

• Conditional statements: if, elif, else
• Looping constructs: for, while
• Loop control: break, continue, pass
• Real-life examples for logic building

Session 4: Functions & Built-in Methods

• Defining and invoking functions
• Parameters, arguments, and return statements
• Variable scope: Local vs Global
• Useful built-in functions: len(), type(), range(), enumerate()
• Introduction to lambda functions and map(), filter()

Session 5: Data Structures – Lists and Strings

• Working with lists: indexing, slicing, appending, inserting, deleting
• List comprehensions
• String operations: slicing, methods, and formatting (f-strings)
• Iterating over lists and strings

Session 6: Data Structures – Dictionaries, Tuples, and Sets

• Dictionaries: creation, access, update, and iteration
• Tuples: characteristics and use cases
• Sets: uniqueness and set operations
• Comparison and selection of appropriate data structures

Session 7: Introduction to NumPy – Arrays & Operations

• What is NumPy and why use it?
• Creating 1D and 2D arrays
• Indexing, slicing, and array operations
• Broadcasting and vectorized operations
• Basic statistical functions with NumPy

Session 8: Advanced NumPy – Array Manipulation & Functions

• Reshaping, transposing, flattening, and concatenating arrays
• Aggregation functions: sum(), mean(), std(), etc.
• Boolean indexing and filtering arrays
• Hands-on Mini Project with NumPy

Session 9: Pandas Basics – Series & DataFrame

• Introduction to Pandas and its core data structures
• Series vs DataFrame
• Reading data from CSV and Excel files
• Exploring data: head(), info(), describe()
• Data selection and filtering

Session 10: Data Manipulation with Pandas – Cleaning & Aggregation

• Handling missing data
• Filtering, sorting, renaming columns
• Aggregation using groupby()
• Merging and joining DataFrames
• Practice with real-world datasets