Learning Python for Data Science course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This Professional Certificate offers a practical, beginner-friendly introduction to Python programming for data science. The program is structured into five core modules and a final capstone project, spanning approximately 16–24 weeks with a total time commitment of 80–120 hours. Each module combines video lectures, hands-on coding exercises, and quizzes to build foundational skills in Python and data analysis. Learners will gain experience with real-world datasets, data manipulation, visualization, and exploratory analysis, culminating in a comprehensive project that demonstrates their proficiency. Lifetime access ensures flexibility for self-paced learning.
Module 1: Python Programming Foundations
Estimated time: 20 hours
- Variables and data types
- Control structures: conditionals and loops
- Functions and code reuse
- Basic data structures: lists and dictionaries
Module 2: Data Types and Structures in Python
Estimated time: 15 hours
- Working with strings and numeric types
- Advanced use of lists and tuples
- Dictionaries and sets for data organization
- Introduction to problem-solving with Python
Module 3: Data Wrangling with Python
Estimated time: 25 hours
- Introduction to Pandas for data manipulation
- Cleaning and transforming datasets
- Handling missing data and inconsistencies
- Using NumPy for numerical operations
Module 4: Exploratory Data Analysis
Estimated time: 20 hours
- Techniques for exploratory data analysis (EDA)
- Identifying trends and patterns in data
- Data cleaning strategies for analysis readiness
- Generating actionable insights from datasets
Module 5: Data Visualization and Exploration
Estimated time: 20 hours
- Creating visualizations with Matplotlib
- Enhancing plots using Seaborn
- Communicating findings effectively through charts
Module 6: Final Project
Estimated time: 30 hours
- Analyze a real-world dataset using Python
- Apply data cleaning, transformation, and visualization techniques
- Publish a structured report presenting key insights
Prerequisites
- Basic computer literacy
- Familiarity with high school-level mathematics
- No prior programming experience required
What You'll Be Able to Do After
- Write Python scripts to automate simple tasks
- Manipulate and clean real-world datasets using Pandas and NumPy
- Perform exploratory data analysis to uncover patterns
- Create informative data visualizations using Matplotlib and Seaborn
- Present data-driven insights in a professional report format