The Data Foundations Track provides a comprehensive introduction to the essential tools and technologies that form the backbone of modern data analytics and data science workflows. This 8-week program is designed to give students hands-on experience with industry-standard tools while building a strong foundation for advanced learning tracks.

Program Structure

This track is structured as a 2-month intensive program that covers both theoretical concepts and practical application through hands-on projects and real-world scenarios.

Month 1: SQL & Databases (BigQuery)

• Week 1-2: Relational Database Concepts & SQL Essentials

  • Database design principles and normalization
  • Basic SQL syntax and data types
  • SELECT statements and filtering data
  • Data aggregation and grouping

• Week 3: Advanced SQL Operations

  • Complex joins (INNER, LEFT, RIGHT, FULL OUTER)
  • Subqueries and correlated subqueries
  • Window functions for advanced analytics
  • Common Table Expressions (CTEs)

• Week 4: BigQuery Mastery

  • Google BigQuery platform deep dive
  • Cost optimization strategies and best practices
  • Working with public datasets
  • Query performance tuning

Month 2: Python Analytics & GitHub

• Week 5-6: Python Fundamentals for Data

  • Python syntax, data structures, and control flow
  • Data manipulation with Pandas library
  • NumPy for numerical operations
  • Data cleaning and preprocessing techniques

• Week 7: Data Visualization & APIs

  • Creating visualizations with Matplotlib and Seaborn
  • Statistical plotting and dashboard creation
  • API integration for data collection
  • BigQuery Python SDK and database connectivity

• Week 8: Version Control & Collaboration

  • Git fundamentals and GitHub workflows
  • Collaborative development practices
  • Code documentation and best practices
  • Project deployment and sharing

Technology Stack

Core Technologies

• SQL & Databases: Google BigQuery, SQL fundamentals
• Programming: Python 3.x, Jupyter Notebooks
• Data Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Tools: GitHub, Git, BigQuery Python SDK
• Cloud Platform: Google Cloud Platform basics

Development Environment

• Python development environment setup
• Jupyter Lab/Notebook for interactive development
• Google Cloud Console and BigQuery interface
• GitHub for version control and collaboration

Hands-On Projects

Project 1: SQL Analytics Dashboard

• Analyze real-world datasets using BigQuery
• Create comprehensive SQL queries with joins and aggregations
• Optimize query performance and cost
• Present findings through data visualizations

Project 2: Python Data Pipeline

• Build end-to-end data pipeline using Python
• Extract data from APIs and databases
• Clean, transform, and analyze data with Pandas
• Create interactive visualizations and reports

Capstone Project: End-to-End Data Pipeline

• Integrate SQL and Python skills in a complete project
• Extract data from multiple sources (APIs, databases)
• Perform comprehensive data analysis and visualization
• Deploy solution using GitHub and present findings
• Demonstrate professional data workflow practices

Prerequisites

No prior experience required. This track is designed for complete beginners who want to start their journey in data analytics and data science. Basic computer literacy and willingness to learn programming concepts are recommended.

Career Outcomes

Upon completion, students will be ready to pursue entry-level positions in:

• Data Analyst roles across various industries
• Business Intelligence Analyst positions
• Junior Data Scientist roles with additional training
• Database Analyst positions
• Continue to advanced specialization tracks in our program

Skills Acquired

• Professional-level SQL proficiency for data analysis
• Python programming for data manipulation and visualization
• Experience with cloud-based analytics platforms (BigQuery)
• Version control and collaborative development skills
• Business communication of data insights
• Foundation for advanced data science and engineering tracks

Next Steps

Graduates of this track are well-prepared to continue with any of our specialization tracks:

• Data Analytics & BI Track for business intelligence focus
• Data Engineering GCP Track for data infrastructure roles
• Data Science Track for machine learning and advanced analytics
• AI Foundations Track for artificial intelligence applications