Python with Data Science and Machine Learning

Module 1: Introduction to Python

• What is Python?
• Installing Python & IDEs (IDLE, VS Code, PyCharm)
• Writing and running Python programs
• Understanding the Python interpreter
• Comments and basic syntax

Module 2: Python Basics

• Variables & Data Types (int, float, string, bool)
• Input and output (input(), print())
• Type conversion
• Basic operators (Arithmetic, comparison, logical, assignment)

Module 3: Control Flow

• Conditional statements (if, elif, else)
• Looping (for loop, while loop)
• break, continue, and pass

Module 4: Data Structures

• Strings (Slicing, methods)
• Lists (Indexing, methods, list comprehension)
• Tuples (Immutable sequences)
• Dictionaries (Key–value pairs)
• Sets (Unique elements, operations)

Module 5: Functions

• Defining functions (def)
• Parameters & return values
• Default & keyword arguments
• Variable scope
• Lambda functions

Module 6: Modules and Packages

• Importing modules
• Creating your own module
• Standard libraries (math, random, datetime, os)

Module 7: File Handling

• Reading files
• Writing files
• Working with text files
• Exception handling basics

Module 8: Object-Oriented Programming (Basic)

• Classes and objects
• Attributes and methods
• Constructors (__init__)
• Inheritance (intro level)

Module 9: Error & Exception Handling

• Types of errors
• try, except, finally
• Raising exceptions

Module 10:Python for Data Science-Foundations

• What is Data Science?
• Data Science workflow
• Using Jupyter Notebook for DS (Installing Jupyter, Running cells & Markdown)
• Core Python libraries (NumPy, Pandas, Matplotlib, Seaborn)
• Importing datasets (CSV, Excel, JSON)

Module 11:Data Analysis with Numpy & Pandas

NumPy

• Arrays, shapes, dimensions
• Indexing, slicing, reshaping
• Array calculations & broadcasting
• Random data generation

Pandas

• Series & DataFrames
• Reading/writing CSV, Excel, JSON
• Data cleaning (Missing values, Duplicates, Type conversion)
• Sorting, filtering, grouping, merging
• Data exploration (info, describe, etc.)

 

 Module 12:Data visualization & Exploratory Data Analysis (EDA) 

 

Visualization

• Matplotlib basics
• Seaborn plots (Histograms, Bar charts, Scatter plots, Boxplots, Heatmaps)
• Customizing charts (labels, colors, legends)

Exploratory Data Analysis

• Understanding distribution
• Correlation analysis
• Outlier detection
• Visualizing patterns & trends
• Mini EDA project on a real dataset

• What is Machine Learning?
• Types of ML (Supervised,Unsupervised)
• Scikit-learn workflow (Importing models,Splitting data (train_test_split))
• Data preprocessing (Scaling (StandardScaler),Encoding categorical data)
• Key ML Algorithms (Linear Regression,Logistic Regression,Decision Trees,KNN)
• Model evaluation (Accuracy,Confusion matrix,Precision, recall, F1-score)
• Mini ML Project (Train model,Test model,Visualize results)

Module 14: Mini projects (anyone from below)

• Movie Recommendation System (Content-Based)
• Customer Churn Prediction
• House Price Prediction (Regression)
• Fake News Detection
• Handwritten Digit Recognition (MNIST)