The Data Analytics & Business Intelligence track is designed for professionals who want to become expert data analysts and business intelligence specialists. This comprehensive 12-week program focuses on advanced analytical techniques, professional visualization tools, and business intelligence best practices.

Program Structure

This track builds upon data foundations with three months of intensive, hands-on training in advanced analytics, business intelligence tools, and data storytelling techniques.

Month 1: Advanced SQL & BigQuery Mastery

• Week 1-2: Advanced SQL Fundamentals

  • Data Definition Language (DDL) and Data Manipulation Language (DML)
  • Complex joins and advanced subquery techniques
  • Advanced aggregate functions and analytical queries
  • Query optimization and performance tuning

• Week 3: Window Functions & Advanced Analytics

  • Row number, rank, and dense rank functions
  • Lead, lag, and running totals for time series analysis
  • Complex Common Table Expressions (CTEs)
  • Advanced window frame specifications

• Week 4: BigQuery Enterprise Features

  • Partitioned tables and clustering strategies
  • Cost optimization and query performance monitoring
  • Advanced BigQuery features: arrays, structs, and nested data
  • Working with public datasets and external data sources

Month 2: Python for Advanced Analytics

• Week 5-6: Advanced Data Manipulation

  • Master-level Pandas techniques for complex data transformations
  • NumPy for high-performance numerical computing
  • Advanced data cleaning and preprocessing pipelines
  • Working with multiple data formats and sources

• Week 7: Exploratory Data Analysis & Statistics

  • Comprehensive EDA methodologies and best practices
  • Statistical analysis and hypothesis testing
  • Correlation analysis and feature relationships
  • Advanced data profiling and quality assessment

• Week 8: API Integration & Automation

  • REST API integration for real-time data collection
  • BigQuery Python SDK for programmatic data access
  • Automated reporting and data pipeline creation
  • Advanced visualization with Matplotlib, Seaborn, and Plotly

Month 3: Business Intelligence & Visualization

• Week 9: BI Fundamentals & Data Modeling

  • Business intelligence concepts and KPI frameworks
  • Dimensional modeling and star schema design
  • Data warehouse concepts and best practices
  • ETL processes for BI systems

• Week 10: Power BI Mastery

  • Power BI Desktop development and DAX formulas
  • Interactive dashboard creation and data storytelling
  • Power BI Service deployment and sharing
  • Advanced Power BI features and custom visuals

• Week 11: Looker & Advanced BI Tools

  • LookML development: dimensions, measures, and explores
  • Looker dashboard creation and data governance
  • Alternative BI tools: Tableau fundamentals
  • Comparative analysis of BI platforms

• Week 12: Professional Reporting & Business Communication

  • Professional report design and presentation techniques
  • Data storytelling and business insight communication
  • Executive dashboard creation and KPI monitoring
  • Project deployment and stakeholder management

Technology Stack

Database & Analytics Platforms

• Primary: Google BigQuery, SQL Server, PostgreSQL
• Cloud: Google Cloud Platform, Microsoft Azure
• Data Warehousing: BigQuery, Snowflake, Azure Synapse
• Query Optimization: Advanced SQL, performance tuning

Programming & Analysis

• Languages: Python 3.x, SQL, DAX, LookML
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly
• APIs: REST API integration, BigQuery Python SDK
• Development: Jupyter Notebooks, VS Code, Git

Business Intelligence Tools

• Primary BI: Power BI Desktop & Service, Looker
• Secondary: Tableau Desktop, Google Data Studio
• Data Modeling: Star schema, dimensional modeling
• Visualization: Advanced charting, interactive dashboards

Data Sources & Integration

• Databases: Relational and NoSQL databases
• APIs: RESTful services, web data extraction
• Files: CSV, JSON, Excel, Parquet formats
• Streaming: Real-time data integration concepts

Hands-On Projects

Project 1: Enterprise SQL Analytics

• Build complex analytical queries on enterprise datasets
• Implement advanced window functions for business metrics
• Optimize query performance and cost in BigQuery
• Create automated reporting solutions

Project 2: Python Analytics Pipeline

• Develop end-to-end analytics pipeline using Python
• Integrate multiple data sources through APIs
• Perform advanced statistical analysis and EDA
• Create automated visualization and reporting system

Project 3: Multi-Platform BI Solution

• Design dimensional data model for business intelligence
• Build comprehensive dashboards in both Power BI and Looker
• Implement DAX calculations and LookML measures
• Create executive reporting and KPI monitoring system

Capstone Project: Enterprise Analytics Solution

• Complete business intelligence solution for real-world scenario
• Integrate data from multiple sources (databases, APIs, files)
• Build scalable analytics pipeline with automated refresh
• Create professional dashboard suite with interactive features
• Present business insights and recommendations to stakeholders

Prerequisites

Required: Completion of Data Foundations track or equivalent experience including:

• Proficiency in SQL fundamentals and basic Python
• Experience with data manipulation and basic visualization
• Understanding of relational database concepts
• Basic familiarity with data analysis workflows

Recommended:

• Some exposure to business environments and KPIs
• Basic understanding of statistics and data analysis
• Familiarity with cloud platforms (preferred but not required)

Career Outcomes

Graduates will be ready for senior analyst, BI developer, and data consultant roles across industries, with expertise in modern analytics platforms and business intelligence tools.

Target Roles & Compensation

• Senior Data Analyst: $75,000 - $120,000+ annually
• Business Intelligence Developer: $80,000 - $130,000+ annually
• Analytics Consultant: $85,000 - $140,000+ annually
• BI Architect: $95,000 - $150,000+ annually
• Data Analytics Manager: $100,000 - $160,000+ annually

Industry Applications

• Finance: Risk analysis, fraud detection, investment analytics
• Healthcare: Operational analytics, patient outcomes analysis
• Retail: Customer analytics, inventory optimization, sales forecasting
• Technology: Product analytics, user behavior analysis, growth metrics
• Manufacturing: Supply chain analytics, quality control, operational efficiency
• Marketing: Campaign analysis, customer segmentation, ROI measurement

Skills Mastery

• Expert-level SQL: Complex queries, optimization, advanced analytics
• Advanced Python: Data manipulation, statistical analysis, automation
• BI Platform Mastery: Power BI, Looker, dashboard development
• Business Acumen: KPI development, business insight communication
• Data Storytelling: Professional presentation, executive reporting
• Technical Leadership: Project management, stakeholder communication

Professional Development

Certification Preparation

• Microsoft Power BI Data Analyst Associate
• Google Cloud Professional Data Engineer (partial preparation)
• Looker LookML Developer certification
• Tableau Desktop Specialist

Industry Recognition

• Portfolio of professional analytics projects
• GitHub repository with analytics solutions
• Case studies demonstrating business impact
• Professional network in analytics community

Next Steps

Advanced Specialization Options

• Data Engineering GCP Track: For building data infrastructure
• Data Science Track: For machine learning and predictive analytics
• Machine Learning Track: For AI-driven analytics solutions

Career Acceleration

• Lead analytics teams and mentor junior analysts
• Transition to data engineering or data science roles
• Consultant or freelance analytics specialist
• Product manager for analytics products
• Chief Data Officer track in smaller organizations