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Course Duration: 2 Months (24 hours)
Course Objectives:
Develop a strong foundation in Python programming.
Learn data analysis and visualization techniques.
Apply decision-making techniques using Python.
Understand regression and classification for predictive modeling.
Solve real-world problems using Python.
Module 1: Python Fundamentals
Introduction to Python and Installation
Variables, Data Types, and Type Conversion
Operators (Arithmetic, Logical, Comparison)
Control Structures: Conditional Statements (if-else)
Loops: for and while
Functions: Defining and Calling Functions
Built-in Libraries (math, random, etc.)
Hands-on Exercises
Module 2: Data Structures and File Handling
Lists, Tuples, and Sets
Dictionaries and Dictionary Comprehension
String Manipulation and Formatting
File Handling (Text, CSV, JSON)
Exception Handling
Hands-on Exercises
Module 3: Data Analysis with Python
Introduction to NumPy: Arrays and Operations
Introduction to Pandas: Series and DataFrames
Loading and Exploring Datasets
Data Cleaning: Handling Missing Values and Duplicates
Hands-on Exercises
Module 4: Data Visualization
Introduction to Matplotlib and Seaborn
Creating Line, Bar, and Scatter Plots
Histograms, Boxplots, and Heatmaps
Customizing Charts for Insights
Hands-on Exercises
Module 5: Decision-Making with Python
Using Conditional Logic for Decision Making
Statistical Decision-Making (Mean, Median, Mode)
Regression Analysis and Classification Techniques
Rule-Based vs. Automated Decision Making
Case Study: Decision Making in Business or Healthcare
Hands-on Exercises
Module 6: Real-World Applications and Case Studies
Applying Python to Real-World Problems
Automation Tasks with Python
Case Study: Data-Driven Decision Making
Mini Project: Applying Regression/Classification for Decision Making
Interpreting Results for Insights
Hands-on Exercises
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