📁 Repository Architecture

.
├── Week_01_02_Data_Cleaning_EDA/
│   ├── data/                           # Raw & cleaned dataset storage (E-commerce, Netflix)
│   ├── notebooks/                      # Data_Cleaning_EDA_Pipeline.ipynb
│   └── docs/                           # One_Page_Summary_Report.pdf
├── Week_03_SQL_Querying/
│   ├── scripts/                        # Chinook_AdventureWorks_Queries.sql
│   └── docs/                           # Query_Insights_Document.pdf
├── Week_04_BI_Dashboarding/
│   ├── dashboards/                     # Superstore_Sales_Dashboard.pbix
│   └── presentation/                   # Strategic_KPI_Presentation.pdf
├── Week_05_Business_Case_Study/
│   ├── reports/                        # Executive_Case_Study_Report.pdf
│   └── presentation/                   # Business_Presentation_Slides.pdf
├── Week_06_Advanced_Python_TimeSeries/
│   ├── notebooks/                      # AAPL_Stock_Feature_Engineering.ipynb
│   └── reports/                        # Financial_Trend_Summary.pdf
├── Week_07_ETL_Pipelines_Automation/
│   ├── pipeline/                       # weather_etl_pipeline.py
│   └── docs/                           # README.md & Demo_Video_Link.txt
└── Week_08_Final_Capstone/
    ├── dashboards/                     # WDI_Global_Development_Dashboard.pbix
    ├── reports/                        # Final_Capstone_Analytical_Report.pdf
    └── README.md                       # Comprehensive Capstone Documentation

🛠️ Detailed Weekly Track & Technical Executions

🔍 Weeks 1 & 2: Data Cleaning & Exploratory Data Analysis (EDA)

• Objective: Ingest, clean, validate, and isolate patterns from structurally compromised raw datasets.

• Datasets Evaluated:

  1. E-commerce Retail Transactions Dataset (Kaggle)

  2. Netflix Movies & TV Shows Dataset (Kaggle)

• Core Tasks Executed:

  • Data Profile & Schema Auditing: Implemented programmatic schema exploration using Pandas (shape, dtypes, primary key constraints profiling).

  • Data Cleaning Rigor: Quantified, handled, and strategically justified missing value treatments; removed duplicate entries; standardized inconsistent dates, text housing (case uniformity), and schema names.

  • EDA & Summary Statistics: Evaluated full matrix central tendency metrics (Mean, Median, Std Dev, Min/Max). Executed $\ge 5$ targeted thematic breakdowns per dataset (e.g., Top revenue countries, monthly sales spikes, Netflix content distribution velocity).

• Deliverables Included: Cleaned .csv output matrices, annotated .ipynb pipeline, and an explicit Execution Summary Report.

🗄️ Week 3: SQL & Relational Data Querying

• Objective: Author scalable, optimized SQL architecture to extract business intelligence from complex relational databases.

• Environments & Datasets: PostgreSQL / MySQL utilizing Chinook (Music Store) and AdventureWorks Lite relational schemas.

• Core Tasks Executed:

  • Database Setup: Normalized schemas, verified primary/foreign constraints, and mapped Entity-Relationship Diagrams (ERD).

  • Advanced Query Operations: Created window operations (ROW_NUMBER(), RANK() OVER (PARTITION BY ...)), multi-tier relational joins (INNER, LEFT, RIGHT), nested subqueries, and conditional aggregate filters (GROUP BY, HAVING).

  • Optimization: Applied indexing paradigms to minimize query execution overhead on large transactional records.

• Deliverables Included: Production-ready .sql script files and a deep-dive Query Insight Document.

📊 Week 4: Data Visualization & Interactive Dashboarding

• Objective: Model and implement executive-ready business intelligence dashboards utilizing industry standard platforms.

• Tools & Datasets: Microsoft Power BI / Tableau using Global Superstore Sales or Johns Hopkins COVID-19 Trend Dataset.

• Core Tasks Executed:

  • KPI Engineering: Designed and isolated primary high-level metrics (Total Revenue, Profit Margin, Sales Growth, Case Recovery Rates).

  • Interactive Controls: Developed custom dimensional filters, slicing hierarchies, dynamic trend cross-filtering, and granular location drill-downs.

  • Data Storytelling Layout: Applied rigorous visual hierarchy layouts, maintaining desaturated, professional color palettes to optimize stakeholder comprehension.

• Deliverables Included: Interactive .pbix / .twb files and a 5–10 slide strategic presentation.

📈 Week 5: Business Analytics Case Study

• Objective: Apply rigorous diagnostic and prescriptive analytics workflows to unpack real-world enterprise operational bottlenecks.

• Case Selected: Telco Customer Churn OR Bank Direct Marketing Campaign Optimization.

• Core Tasks Executed:

  • Operational Diagnostic Modeling: Outlined underlying business problem statements and assessed demographic, behavioral, and operational variables via correlation matrices and distribution box-plots.

  • Driver Identification: Isolated key customer churn/subscription indicators (e.g., contract types, charges, historic campaign touchpoints).

  • Prescriptive Recommendations: Designed 3–5 highly specific, data-validated actionable strategies (retention pathways, targeted loyalty frameworks, tailored campaign profiles).

• Deliverables Included: Formal 3–5 Page Management Case Report and a streamlined Stakeholder Slide Deck.

🐍 Week 6: Advanced Python Analysis & Feature Engineering

• Objective: Execute complex time-series transformations and programmatically engineer features on volatile financial market streams.

• Dataset: Apple Inc. (AAPL) Historical Stock Data extracted via yfinance.

• Core Tasks Executed:

  • Time-Series Transformation: Managed complex date/time indexes, implemented rolling statistical boundaries (7-day, 30-day moving averages), and performed multi-interval percentage change calculations.

  • Feature Engineering Execution: Programmatically generated new analytical features including Daily Price Deltas, Rolling Volatility Indices, and Normalized Monthly Returns.

  • Visualizations: Created interactive multi-axis time plots tracking Close Price vs. Volume and Bollinger-band style moving average channels.

• Deliverables Included: Structured .ipynb research notebook and a concise Financial Trend Summary.

⚙️ Week 7: Data Pipelines & Automated ETL Architectures

• Objective: Construct a fully automated end-to-end Extract, Transform, Load (ETL) pipeline harvesting live streaming data.

• Data Source & Core Tools: Python (requests, pandas), OpenWeather API, and local database storage (SQLite/CSV).

• Core Tasks Executed:

  • Automated Extraction: Constructed an authenticated programmatic interface connecting to the OpenWeather API to parse real-time environmental metrics for multi-city vectors.

  • Structural Transformation: Standardized JSON objects into normalized Pandas DataFrames, handling type casting, field remapping, and metric standardization.

  • Target Load Execution: Configured automated loading routines into local SQLite databases and structured CSV formats.

• Deliverables Included: Automated ETL .py scripts, GitHub codebase architecture, a comprehensive README.md, and an organic project demo video walking through execution steps.

🌍 Week 8: Data Analytics Capstone Project (Final)

• Objective: Orchestrate the complete end-to-end data analytics workflow on a multi-dimensional macro-level public policy framework.

• Dataset: World Bank's World Development Indicators (WDI) — encompassing global economic, environmental, and infrastructure telemetry.

• Core Tasks Executed:

  • Thematic Specialization: Formulated a specific developmental hypothesis tracking multi-decade socio-economic indicators across nations.

  • Analytical Rigor & BI Modeling: Engineered a complex corporate-grade Power BI multi-page dashboard, mapping core thematic KPIs, time-series projections, and regional statistical comparisons.

  • Comprehensive C-Suite Communication: Structured a formal academic-grade PDF research paper documenting the core methodology, data transformation protocols, and actionable, systemic global recommendations.

• Deliverables Included: Technical .pbix dashboard file, peer-reviewed Final Project Report (PDF), modular GitHub ecosystem, and an end-to-end recorded 5-10 minute executive presentation.

🚀 Key Certifications & Technical Proficiencies Gained

• Languages & Libraries: Python (Pandas, NumPy, Requests, Matplotlib, Seaborn, yfinance), SQL (PostgreSQL, MySQL).

• BI & Workflow Automation: Microsoft Power BI, Tableau, Git/GitHub, ETL Pipeline Architecture.

• Domain Expertise: Exploratory Data Analysis (EDA), Statistical Profiling, Time-Series Forecasting, Feature Engineering, Metric/KPI Modeling, Strategic Reporting.

🤝 Acknowledgments

Special thanks to the instructors, mentors, and program coordinators at AnalystLab Africa for providing a world-class, rigorous framework to develop professional data analytics capabilities during Batch B.

Note: To review specific project code, dashboards, or executive reports, browse into the corresponding directory tracks listed above.