Data Science Foundations Specialization Course Syllabus

Overview: This specialization provides a beginner-friendly introduction to data science, combining theory and hands-on practice in Python, R, SQL, and essential tools like Jupyter Notebooks and GitHub. With approximately 2–3 hours of study per week, learners complete the program in about 13 weeks. The course emphasizes real-world applications through labs and projects using datasets from urban mobility and rocketry, culminating in a capstone project that demonstrates core data science competencies.

Module 1: What is Data Science?

Estimated time: 6 hours

• Defining data science and its modern relevance
• Understanding key roles in data science
• Exploring real-world applications across industries
• Reflection exercises linking concepts to practical examples

Module 2: Tools for Data Science

Estimated time: 12 hours

• Introduction to Jupyter Notebooks and RStudio Cloud
• Using GitHub for version control and collaboration
• Basics of Python and R for data tasks
• Hands-on labs in cloud-based development environments

Module 3: Data Science Methodology

Estimated time: 12 hours

• The nine-step data science lifecycle
• From business understanding to model deployment
• Problem scoping and data requirements
• Applying methodology to a case study

Module 4: Python for Data Science, AI & Development

Estimated time: 12 hours

• Python fundamentals: syntax and data types
• Data structures: lists, dictionaries, arrays
• Functions and control flow
• Using Pandas and NumPy for data manipulation

Module 5: Databases and SQL for Data Science

Estimated time: 12 hours

• Relational database concepts
• Writing SQL queries for data extraction
• JOIN operations and filtering data
• Database design principles and normalization

Module 6: Final Project

Estimated time: 18 hours

• Analyze urban mobility dataset using Python and SQL
• Build a predictive model for rocketry performance
• Create interactive visualizations and dashboards

Prerequisites

• Familiarity with basic computer operations
• No prior programming experience required
• Access to a web browser and internet connection

What You'll Be Able to Do After

• Apply core data science workflows to real-world problems
• Use Python, R, and SQL to clean, analyze, and visualize data
• Write and execute SQL queries on relational databases
• Develop basic machine learning models using regression and clustering
• Create interactive dashboards and data visualizations