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Python for Data Analytics

Automate tasks and analyze data using Python's powerful libraries. Learn Pandas, NumPy, and data processing for real-world analytics projects.

50+ Hours
15+ Projects
Beginner to Advanced
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Complete Curriculum

Course Syllabus

Comprehensive module-wise breakdown of everything you'll learn

01

Introduction to Python

Python fundamentals and setup

1.1 What is Python?

  • High-level, interpreted programming language
  • History and evolution (Python 2 vs 3)
  • Features: Simple, readable, open-source
  • Applications: Web, Data Science, ML, Automation

1.2 Python Installation & Setup

  • Installing Python on Windows/Mac/Linux
  • Setting up environment variables
  • IDE options: VS Code, PyCharm, Jupyter
  • First Python program
02

Python Basics

Syntax, variables, and data types

2.1 Python Syntax

  • Program structure and indentation
  • Comments (single and multi-line)
  • Keywords and identifiers
  • Pass statement

2.2 Variables & Data Types

  • Variable assignment and naming rules
  • Numeric types: int, float, complex
  • Strings and string operations
  • Boolean and None types

2.3 Type Conversion

  • Implicit vs explicit conversion
  • Type casting functions
  • Mutable vs immutable types
03

Operators & Control Flow

Operators, conditionals, and loops

3.1 Operators

  • Arithmetic operators (+, -, *, /, //, %, **)
  • Comparison operators (==, !=, >, <)
  • Logical operators (and, or, not)
  • Membership and identity operators

3.2 Conditional Statements

  • if, elif, else statements
  • Nested conditionals
  • Ternary operator

3.3 Loops

  • for loop and range function
  • while loop
  • break, continue, pass
  • Nested loops
04

Data Structures

Lists, tuples, dictionaries, and sets

4.1 Lists

  • Creating and accessing lists
  • List methods: append, insert, remove, pop
  • List slicing and comprehension
  • Sorting and reversing

4.2 Tuples & Sets

  • Tuple creation and immutability
  • Tuple unpacking
  • Set operations: union, intersection, difference

4.3 Dictionaries

  • Creating and accessing dictionaries
  • Dictionary methods: keys, values, items
  • Nested dictionaries
  • Dictionary comprehension
05

Functions & Modules

Defining functions and using modules

5.1 Functions

  • Defining and calling functions
  • Parameters and arguments
  • Return values
  • Lambda functions
  • *args and **kwargs

5.2 Modules & Packages

  • Importing modules
  • Creating custom modules
  • pip and package installation
06

Data Analytics with Python

Pandas, NumPy, and data manipulation

6.1 NumPy

  • Introduction to NumPy arrays
  • Array operations and broadcasting
  • Mathematical functions
  • Array manipulation

6.2 Pandas Basics

  • Series and DataFrames
  • Reading data from CSV, Excel
  • Data selection and filtering
  • Handling missing values

6.3 Data Manipulation

  • Sorting and grouping data
  • Merging and joining DataFrames
  • Pivot tables and aggregation
  • Data cleaning techniques

6.4 Data Visualization

  • Matplotlib basics
  • Seaborn for statistical plots
  • Creating charts: bar, line, scatter

6.5 Real-World Projects

  • Data cleaning automation
  • Sales data analysis project
  • Report automation scripts
Skills You'll Gain

What You'll Learn

Python Basics

Master Python syntax, data types, and control flow

Data Structures

Work with lists, dictionaries, and sets effectively

Pandas & NumPy

Analyze and manipulate data with powerful libraries

Data Visualization

Create charts with Matplotlib and Seaborn

Automation

Automate repetitive data processing tasks

Job Ready

Build portfolio projects for data analyst roles

Start Your Journey

Ready to Learn Python?

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