Focus: Shifting from local spreadsheets to modern, cloud-first infrastructure.

Topics: The Modern Data Stack; Cloud Warehouses (Big Query/Snowflake) vs. Legacy Databases How Generative AI changes the analyst workflow (Prompt Engineering for data syntax).

Hands-On Lab: Initializing the cloud environment. Navigating cloud web consoles and CLI endpoints. Querying massive, multi-terabyte public datasets natively in the cloud. Parsing complex data types at rest.

AI Integration: Engineering structural prompt templates to reverse-engineer unknown, poorly documented database schemas and generate data dictionary documentation.

Weekly Assignment: ‘’The Architecture Mapping.’’ Document the end-to-end data lifecycle of a global transaction, detailing data ingestion from an OLTP production database into an OLAP cloud data warehouse, explicitly defining storage optimizations.

Platform: Google Big Query (Console Overview).

Focus: Retrieving data efficiently from cloud warehouses. SELECT, WHERE, ORDER BY, LIMIT; Logical operators and basic aggregation (COUNT, SUM, AVG).

FinOps Focus: Computational complexity of queries. Indexing, partitioning, and clustering strategies in the cloud. Quantifying scanned bytes per query and calculating real-world infrastructure billing impacts of inefficient syntax (e.g., eliminating SELECT * anti-patterns).

Hands-On Lab: Writing optimized filtering logic, complex multi-conditional statements, and database aggregations using real enterprise transaction logs.

AI Integration: Utilizing LLMs to interpret raw compiler error logs, trace execution bottlenecks, and inject optimization hints.

Weekly Assignment: ‘’The Cost-Conscious Data Retrieval.’’ Refactor a series of poorly written legacy queries. Students must reduce total bytes processed by a minimum of 60% while maintaining identical analytical outputs.

Platform: Google Big Query using public e-commerce datasets.

Focus: Combining and restructuring complex datasets. Relational database joins (INNER, LEFT, RIGHT); Multi-step query logic using Common Table Expressions (CTEs) and subqueries; Conditional logic via CASE WHEN.

Hands-On Lab: Compounding multi-step database queries. Merging detached customer dimension tables with transactional fact tables to compute deep behavioural data.

AI Integration: Prompting AI engines to refactor heavily nested legacy subqueries into highly readable, optimized, sequential CTE structures.

Weekly Assignment: ‘’The Multi-Table Relational Challenge.’’ Write a production-ready, multi-table analytical script using multiple CTEs to calculate complex retention and inventory turnover performance.

Platform: Google BigQuery.

Focus: Rapidly cleaning and preparing data using Python.

Topics: Execution of complex cleanup workflows in Pandas; Handling missing data, structural duplicates, and converting broken or unformatted data types.

Weekly Assignment: ‘’The Algorithmic Data Cleaning Pipeline.’’ Build an automated, end-to-end Python sanitization script that accepts a corrupted data file and outputs a pristine, structural Parquet dataset.

Platform: Google Colab.

Focus: Rapidly cleaning and preparing data using Python.

Topics: Execution of complex cleanup workflows in Pandas; Handling missing data, structural duplicates, and converting broken or unformatted data types.

Weekly Assignment: ‘’The Algorithmic Data Cleaning Pipeline.’’ Build an automated, end-to-end Python sanitization script that accepts a corrupted data file and outputs a pristine, structural Parquet dataset.

Platform: Google Colab.

Focus: Finding hidden patterns in data before building final dashboards.

Topics: Statistical analysis with Pandas; Visualizing trends and outliers using Matplotlib and Seaborn; Slicing data by dimensions.

Hands-On Lab: Executing comprehensive exploratory data analysis (EDA) to locate patterns, collinearity, and anomalies within high-dimensional business data.

AI Integration: Prompting generative models to write advanced data-profiling loops that automatically generate multi-dimensional statistical summaries and correlation matrices.

Weekly Assignment: ‘’The Diagnostic Data Profile.’’  Create a comprehensive exploratory data notebook mapping the statistical distribution of an unfamiliar dataset, isolating 3 distinct systemic anomalies using programmatic visualization

Platform: Google Colab.

Focus: Building reliable data pipelines into BI tools.

Topics: Extracting data from cloud databases (BigQuery) and Web APIs; Transforming dirty data via Power Query; Automation frameworks and refresh schedules.

Platform: Power BI Desktop & Power Query.

Core Concepts: Dimensional modeling theory. Star Schema design principles vs. Snowflake Schemas. Fact tables (additive, semi-additive, non- additive) vs. Dimension tables (SCD Type 1 and Type 2). Relationship cardinality, cross-filtering directions, and data granularity alignment.

Hands-On Lab: Developing robust, enterprise-grade semantic layers. Constructing high-performance table relationships inside the data model layout.

Platform: Power BI Desktop

Core Concepts: Writing advanced analytical metrics using DAX (Data Analysis Expressions), optimizing calculation contexts via CALCULATE, filter manipulation, and time-intelligence logic.

AI Integration: Using Copilot in Power BI to draft complex DAX time- intelligence formulas.

Weekly Assignment: ‘’The Semantic Schema Build.’’ Construct a fully normalized Star Schema out of unorganized flat tables and program 5 advanced DAX measures tracking rolling year-over-year operational trends.

Platform: Power BI Desktop.

Focus: Building enterprise dashboards that executives can easily read.

Topics: Layout grids and cognitive load design; Dynamic filtering, drill-downs, and bookmarks; Brief comparative session on translating these skills into Tableau.

Hands-On Lab: Developing production-ready executive visual interfaces. Designing interactive dashboards. Translating these semantic data concepts directly into worksheets and geographic storyboards within alternative enterprise tools like Tableau.

Weekly Assignment: ‘’The Corporate BI Implementation.’’ Deliver a highly polished, interactive dashboard matching rigorous enterprise UI/UX specifications, featuring dynamic user-navigation flows.

Platform: Power BI Desktop (with Tableau Web Sandbox).

Core Concepts: FinOps frameworks (Inform, Optimize, Operate). Auditing enterprise data lake access and storage tiers. Designing automated data quality testing frameworks.  uantifying the financial return on investment (ROI) of automated intelligence infrastructures.

Hands-On Lab: Reviewing structural cloud compute and infrastructure billing logs. Parsing complex cloud usage reports to build automated tracking mechanisms for system resource overruns.

AI Integration: Utilizing AI to write complex cost-allocation logic and build comprehensive automation test suites to ensure enterprise data compliance.

Weekly Assignment: ‘’The Infrastructure Cost Optimization Audit.’’ Analyze an active cloud computing invoice log, isolate programmatic cost anomalies, trace the specific query operations causing compute bloat, and engineer an automated billing tracking model.

Platform: BigQuery Billing Data / Power BI.

Core Concepts: Analytical translation and technical narrative frameworks. Mapping technical metrics directly onto corporate financial metrics (EBITDA, operational cost reductions, customer lifetime value expansion). System architecture defense strategies.

Hands-On Lab: Reviewing deployment strategies and code promotion across development, staging, and production environments (CI/CD basics for data).

Technical Capstone Defense: Live technical presentation and review of the comprehensive enterprise data product built during the program.

Platform: Google Big Query Sandbox, Google Colab, and Power BI Desktop (for the complete technical pipeline presentation).