Data Science Portfolio — IBM Data Science Professional Certificate (IBM/Coursera)

This repository showcases my hands-on data science work from the IBM Data Science Professional Certificate, organized by module. The strongest end-to-end projects are highlighted first.

Featured projects

1) SpaceX Falcon 9 Launch Outcome — Analysis & Prediction (Capstone)

Folder: 9. Applied Data Science Capstone/
Goal: analyze and predict Falcon 9 first-stage landing outcome using historical launch data.
What I did:

• Performed EDA using SQL + Python
• Built visualizations to uncover patterns and drivers of success
• Ran geospatial analysis with Folium (launch sites and outcome patterns)
• Built an interactive dashboard with Plotly Dash
• Trained and evaluated models to predict mission outcome Skills/tools: Python, SQL, EDA, data visualization, Folium, Dash, ML classification, model evaluation

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2) Tesla vs GameStop — Revenue & Stock Price Dashboard

Folder: 4. Python Project for Data Science/
Goal: extract and compare company revenue against stock price for Tesla and GameStop.
What I did:

• Extracted revenue data (API-based data acquisition)
• Cleaned and prepared time-series style data for comparison
• Built a dashboard-style visualization comparing stock price vs revenue Skills/tools: Python, data extraction (API), data cleaning, visualization/dashboarding

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3) Real Estate Data Analytics — Wrangling, Outliers, Pipeline (EDA)

Folder: 6. Data Analysis with Python/
Goal: perform structured EDA on real estate data and build a reusable analysis workflow.
What I did:

• Data wrangling and preparation for analysis
• Outlier identification and descriptive statistics
• Built and evaluated a pipeline (preprocessing + modeling workflow) Skills/tools: Python, pandas, EDA, statistics, preprocessing pipelines, evaluation

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4) Rainfall Prediction — Classifier Pipeline with Tuning

Folder: 8. Machine Learning with Python/
Goal: build a machine learning classifier to predict rainfall.
What I did:

• Feature engineering and dataset preparation
• Built an end-to-end classification pipeline
• Optimized the model using GridSearchCV (cross-validation)
• Evaluated, refined, and compared model performance Skills/tools: scikit-learn, pipelines, GridSearchCV, cross-validation, classification metrics, feature engineering

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Skills demonstrated

• Python for data work: wrangling, EDA, feature prep, pipelines
• SQL analytics: querying, joins, aggregation for exploration and insight
• Visualization & dashboards: charts, geospatial mapping (Folium), interactive dashboards (Dash)
• Machine learning: classification workflows, tuning, evaluation, iteration
• End-to-end delivery: problem framing → data acquisition → analysis/modeling → communication

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Repository map (by course/module)

• 1. Data Science Methodology/ — problem framing and analytic approach
• 2. Tools for Data Science/ — DS tooling and workflow foundations
• 3. Python for Data Science, AI & Development/ — Python foundations applied to DS
• 4. Python Project for Data Science/ — Tesla vs GameStop revenue dashboard project
• 5. Databases and SQL for Data Science with Python/ — SQL + Python for analytics
• 6. Data Analysis with Python/ — EDA + pipeline-based analysis (real estate analytics)
• 7. Python for Data Visualization/ — visualization techniques and storytelling
• 8. Machine Learning with Python/ — rainfall prediction classifier + tuning/evaluation
• 9. Applied Data Science Capstone/ — SpaceX Falcon 9 outcome analysis + prediction
• 10. Generative AI. Elevate your Data Science career/ — GenAI concepts for DS workflows
• 11. Data Scientist career guide and interview preparation/ — career/interview materials

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Quick reference resources (repo root)

• GitGuideCommands.pdf — Git command guide
• Python Cheat Sheet - The Basics Coursera.pdf
• Python cheat sheet working with data.pdf
• Python_reference_sheet.pdf

License / attribution

Portfolio/educational use. Please attribute appropriately and respect any upstream course/lab licensing where applicable.