Python for Data Science and Machine Learning course Syllabus

A rigorous and career-focused program that builds strong Python and machine learning expertise for data-driven careers. This course spans approximately 18–24 weeks with a weekly commitment of 6–10 hours, combining hands-on coding, data analysis, and machine learning fundamentals. Learners progress from Python basics to building and evaluating predictive models, culminating in a real-world capstone project.

Module 1: Python Programming for Data Analysis

Estimated time: 60 hours

• Introduction to Python syntax and programming logic
• Working with core data structures: lists, dictionaries, and tuples
• NumPy for numerical computing and array operations
• Pandas for data cleaning, transformation, and manipulation

Module 2: Data Visualization and Exploration

Estimated time: 60 hours

• Creating visualizations with Matplotlib and Seaborn
• Principles of exploratory data analysis (EDA)
• Identifying patterns, trends, and outliers in datasets
• Communicating insights through effective data storytelling

Module 3: Machine Learning Foundations

Estimated time: 60 hours

• Introduction to supervised learning concepts
• Building regression and classification models
• Training, validation, and testing workflows
• Applying cross-validation techniques for model evaluation

Module 4: Model Evaluation and Performance Optimization

Estimated time: 40 hours

• Assessing model accuracy and error metrics
• Tuning hyperparameters for improved performance
• Addressing overfitting and underfitting

Module 5: Capstone Project

Estimated time: 40 hours

• Analysis of a real-world dataset using Python tools
• Building and evaluating predictive machine learning models
• Optimizing model performance and presenting structured results

Module 6: Final Project

Estimated time: 40 hours

• Deliverable 1: Complete exploratory data analysis report
• Deliverable 2: Trained and evaluated machine learning model
• Deliverable 3: Final presentation of findings and model performance

Prerequisites

• Basic understanding of high school-level mathematics
• Familiarity with fundamental programming concepts (helpful but not required)
• Access to a computer with internet and Python environment setup support provided

What You'll Be Able to Do After

• Write efficient Python code for data analysis tasks
• Manipulate and clean real-world datasets using Pandas and NumPy
• Visualize data and communicate insights effectively using Matplotlib and Seaborn
• Build and evaluate supervised machine learning models
• Complete a full data science workflow from data exploration to model deployment