Course Schedule

Week 1 (Aug 27 - Aug 29): Introduction to Data Science Systems

Aug 27 (Tues):

• Lecture: Overview of Data Science Systems
• Content: Introduction to Data Pipelines, ETL Concepts, and Data Wrangling
• Reading: Data Science Fundamentals

Aug 29 (Thurs):

• Case Study: Data Science at the Golden State Warriors
• Content: Exploring how the Warriors leverage data science for performance
• Activity: Case Study Discussion
• Reading: Harvard Case Download (PRE-READ before class)

Week 2 (Sept 3 - Sept 5): Introduction to Databases and SQL

Sept 3 (Tues):

• Lecture: Fundamentals of Relational Databases
• Content: SQL basics, database design, and normalization
• Reading: A Beginner's Guide to SQL

Sept 5 (Thurs):

• Hands-On: Creating and Querying Databases
• Activity: Using MySQL and SQLite to perform basic SQL operations
• Reading: Database Normalization Explained

Week 3 (Sept 10 - Sept 12): Python with SQL

Sept 10 (Tues):

• Lecture: Integrating Python with Databases
• Content: SQLite and MySQL, SQLAlchemy, and ORM concepts
• Reading: Using SQLite with Python

Sept 12 (Thurs):

• Hands-On: Executing SQL Queries in Python
• Activity: Building a Python application that interacts with a SQL database
• Reading: SQLAlchemy: Introduction to ORM with Python

Week 4 (Sept 17 - Sept 19): Data Handling in Python

Sept 17 (Tues):

• Lecture: Working with Data Files in Python
• Content: Handling CSV files, DataFrames, and Pandas basics
• Reading: Handling CSV Files in Python

Sept 19 (Thurs):

• Hands-On: Data Cleaning and Transformation
• Activity: Manipulating data using Pandas
• Reading: Pandas for Data Science

Week 5 (Sept 24 - Sept 26): JSON and Python

Sept 24 (Tues):

• Lecture: Working with JSON in Python
• Content: Parsing and generating JSON, integrating with APIs
• Reading: Working with JSON Data in Python

Sept 26 (Thurs):

• Hands-On: JSON and Pandas Integration
• Activity: Working with JSON data in a Pandas DataFrame
• Reading: Integrating JSON with Pandas (SAME)

Week 6 (Oct 1 - Oct 3): Calling APIs with Python

Oct 1 (Tues):

• Lecture: Introduction to APIs
• Content: RESTful services, making API calls, and handling responses
• Reading: An Introduction to APIs

Oct 3 (Thurs):

• Hands-On: Consuming Public APIs
• Activity: Practice making API requests and parsing data
• Reading: Making API Requests with Python

Week 7 (Oct 8 - Oct 10): Building Your Own ETL Tool in Python

Oct 8 (Tues):

• Lecture: ETL Process Overview
• Content: Designing an ETL pipeline, best practices
• Reading: Understanding ETL and Its Importance

Oct 10 (Thurs):

• Hands-On: Building a Custom ETL Tool
• Activity: Implementing a simple ETL tool using Python, Pandas, MySQL, and SQLite
• Reading: Building an ETL Pipeline with Python

Week 8 (Oct 17 - Oct 19): Advanced Data Handling and Analysis in Python

Oct 17 (Thur):

• Lecture: Advanced Data Aggregation Techniques
• Content: Grouping, merging, and joining DataFrames
• Reading: Advanced Pandas Techniques

Oct 19 (Thurs):

• Hands-On: Complex Data Analysis
• Activity: Perform advanced data manipulation and analysis using Pandas (Read Chapters 5-7)
• Reading: Data Aggregation and Grouping with Pandas

Week 9 (Oct 22 - Oct 24): Data Project Development

Oct 22 (Tues):

• Lecture: Planning a Data Science Project
• Content: Defining objectives, selecting datasets, and project management
• Reading: Planning a Data Science Project

Oct 24 (Thurs):

• Hands-On: Project Implementation
• Activity: Begin work on an end-to-end data science project, focusing on data ingestion, cleaning, and visualization
• Reading: End-to-End Data Science Projects

Week 10 (Oct 29 - Oct 31): Data Visualization and Reporting

Oct 29 (Tues):

• Lecture: Introduction to Data Visualization
• Content: Tools like Matplotlib, Seaborn, and creating dashboards with Plotly
• Reading: Introduction to Data Visualization with Python

Oct 31 (Thurs):

• Hands-On: Building Dashboards
• Activity: Create an interactive data dashboard and generate reports
• Reading: Creating Dashboards with Plotly

Week 11 (Nov 5 - Nov 7): No Class on Election Day / Data Project Work

Nov 5 (Tues): No Class (Election Day)

Nov 7 (Thurs):

• Workshop: Project Development Session
• Activity: Continue work on the data science project with in-class support
• Reading: Continuous Progress in Data Projects

Week 12 (Nov 12 - Nov 14): Deploying Data Applications on Google Cloud

Nov 12 (Tues):

• Lecture: Google Cloud Infrastructure Overview
• Content: Setting up MySQL on Google Cloud SQL, deploying applications
• Reading: Google Cloud for Data Science

Nov 14 (Thurs):

• Hands-On: Deploying Python Applications
• Activity: Practice deploying a data-driven Python application on Google Cloud
• Reading: Deploying MySQL on Google Cloud SQL

Week 13 (Nov 19 - Nov 21): Google Cloud Application Deployment

Nov 19 (Tues):

• Lecture: Continuous Integration and Deployment (CI/CD)
• Content: Setting up CI/CD pipelines for data projects
• Reading: Continuous Integration and Deployment with Google Cloud

Nov 21 (Thurs):

• Hands-On: Finalizing Deployment
• Activity: Deploy your project on Google Cloud with CI/CD integration
• Reading: Deploying Python Apps on Google Cloud

Week 14 (Nov 26): Project Check-In and Work Session

Nov 26 (Tues):

• Workshop: Project Check-In
• Content: Review project progress and provide feedback
• Activity: Focused project work with in-class guidance

Week 15 (Dec 3 - Dec 5): Final Project Presentations and Course Wrap-Up

Dec 3 (Tues):

• Activity: Final Project Presentations
• Content: Present and discuss the end-to-end data science projects

Dec 5 (Thurs):

• Wrap-Up: Final Feedback and Course Closure
• Activity: Reflect on learning outcomes and course experiences