Python for Data Analysis

Course Overview

Week 1: Introduction to Python and Basics

Getting Started with Python:

1. Python installation and setup
2. Introduction to Python variables
3. Python basic operators and understanding blocks
4. Python keywords and identifiers
5. Comments and multiline comments in Python
6. Python indentation rules

Operators in Python:

1. Arithmetic operators
2. Relational operators
3. Logical operators
4. Assignment operators
5. Membership operators
6. Identity operators

Introduction to Variables and Data Types:

1. Variables, expressions, and conditions
2. Global and local variables in Python
3. Packing and unpacking arguments
4. Type casting in Python
5. Byte objects vs. strings in Python
6. Variable scope and lifetime

Python Data Types:

1. Numeric data types
2. String data types and operations
3. Non-numeric data types (Boolean, None)
4. Data structures: Strings, Lists, Tuples, Dictionaries, Sets

Week 2: Control Structures and Loops

Control Structures:

1. Conditional statements: if, else, elif
2. Nested if and else statements
3. Using in and not in keywords

Loops in Python:

1. for and while loops
2. Nested loops and looping techniques
3. Range function usage in loops
4. Control statements: break, continue, pass
5. Printing patterns using loops
6. Generators and Python iteration methods

Week 3: Functions, Modules, and Packages

Python Functions:

1. Function syntax and calls
2. Arguments: Required, default, positional, and variable-length
3. Writing empty functions using pass
4. Lambda functions (anonymous functions)
5. *args and **kwargs usage
6. Scope and lifetime of variables in functions
7. Recursive functions: Advantages and disadvantages

Modules and Packages:

1. Organizing Python projects with modules
2. Importing own and external modules
3. Understanding and creating packages
4. Random functions and their applications

Advanced Functional Programming:

1. Map, filter, and reduce functions with lambda
2. Practical examples of Python functions and modules

Week 4: Advanced Data Structures

Tuples:

1. Accessing elements, modifying, and deleting tuples
2. Built-in functions: Length, sort, count, index, membership

Dictionaries:

1. Creating and accessing dictionary values
2. Dictionary methods: get, add, copy, fromkeys, items, keys, values
3. Dictionary comprehensions and advanced operations
4. Default and ordered dictionaries

Sets:

1. Creating and modifying sets
2. Set operations: Union, intersection, difference
3. Frozen sets and their usage

Strings:

1. String operations and manipulation

Week 5: Data Analysis and Visualization

Data Analysis with Pandas:

1. Introduction to Pandas
2. Reading and writing Excel files using Pandas
3. Handling missing values and data cleaning
4. Exploring data with plotting, correlations, and histograms
5. Statistical concepts: Mean, median, mode, variance, standard deviation

Data Visualization with Matplotlib:

1. Creating bar, column, pie, area, and scatter plots
2. Customizing chart properties and labels
3. Understanding plt.subplots() and legends
4. Exporting charts as images

Week 6: Data Visualization with Seaborn

Introduction to Seaborn:

1. Creating scatter and count plots
2. Using Pandas with Seaborn for data visualization
3. Tidy vs. untidy data
4. Adding variables with hue
5. Visualizing relationships between quantitative variables