This repository documents my progress through the IBM Data Science Professional Certificate and serves as a portfolio of coursework, labs, projects, presentation materials, and supporting reference resources developed throughout the program.
- Program Overview
- Certificate
- Featured Projects
- Technical Stack
- Latest Additions
- Repository Organization by Branch
- Notes
| Course | Title | Status | Key Skills |
|---|---|---|---|
| C1 | What is Data Science? | ✅ | Foundations, Data Literacy |
| C2 | Tools for Data Science | ✅ | Jupyter, RStudio, GitHub |
| C3 | Data Science Methodology | ✅ | Problem Definition, Analytical Approach, Requirements |
| C4 | Python for Data Science & AI | ✅ | Python Fundamentals, APIs, Web Scraping |
| C5 | Python Project for Data Science | ✅ | Dashboards, Data Extraction, yfinance |
| C6 | Databases and SQL with Python | ✅ | SQL, Relational Databases, Python DB-API |
| C7 | Data Analysis with Python | ✅ | Pandas, NumPy, SciPy, Data Wrangling, EDA |
| C8 | Data Visualization with Python | ✅ | Matplotlib, Seaborn, Folium, Dash |
| C9 | Machine Learning with Python | ✅ | Regression, Classification, Clustering |
| C10 | Applied Data Science Capstone | ✅ | SpaceX Project, Folium, Plotly, Predictive Analysis |
| C11 | Generative AI for Data Science | ✅ | Generative AI in Data Science, Model Building, Final Project |
A capstone project focused on predicting whether the first stage of a Falcon 9 rocket will land successfully. The work includes data collection, exploratory analysis, visual analytics, and predictive modeling.
Related branches:
- 10.1 Data Collection and Wrangling
- 10.2 Exploratory Data Analysis
- 10.3 Visual Analysis
- 10.4 Predictive Analysis
- 10.5 Present Insights
A final project centered on real estate data analysis, feature exploration, and model-oriented interpretation for house pricing datasets.
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A practice project that applies data cleaning, analysis, and model development techniques to insurance cost data.
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A project covering classification-focused workflows and model evaluation through practical prediction tasks.
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A project exploring the use of generative AI concepts within data science workflows and model-building tasks.
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- Languages: Python, SQL
- Libraries: Pandas, NumPy, SciPy, Scikit-learn
- Visualization: Matplotlib, Seaborn, Folium, Plotly Dash
- Tools: Jupyter Notebooks, IBM Watson Studio, Anaconda, Git, GitHub
- Core Concepts: Data Wrangling, Exploratory Data Analysis, SQL Analysis, Predictive Modeling, Visualization, Generative AI
The default branch includes presentation-ready deliverables for communicating project findings:
- Data Findings Report Edder April 30 2025.pdf — Report version for convenient viewing and sharing.
- Data Findings Report Edder April 30 2025.pptx — Editable presentation deck with findings, charts, and visual summaries.
This repository is organized primarily by branches, where each branch represents a course module, project milestone, final project, or supporting resource.
- Main Branch — Repository landing branch with README and latest presentation materials.
- 7.1 Data Acquisition
- 7.2 Data Wrangling
- 7.3 Exploratory Data Analysis (EDA)
- 7.4 Model Development
- 7.5 Model Evaluation and Refinement
- 7.6 Project Practice: Insurance Cost Analysis
- 7.7 Final Project: Data Analytics for House Pricing Data Set
- 8.1 Introduction to Matplotlib and Line Plots
- 8.2 Basic and Specialized Visualization Tools
- 8.3 Waffle Charts, Word Clouds, and Regression Plots
- 8.4 Creating Dashboards with Plotly and Dash
- 8.5 Final Project and Exam
- 9.1 Linear Regression (Single and Multiple)
- 9.2 Classification and Regression: Trees, SVM, KNN, Random Forest
- 9.3 Unsupervised Learning: K-means, DBSCAN, HDBSCAN, Dimension Reduction, and Feature Engineering
- 9.4 Evaluating ML Models and Generalizability
- 9.5 Final Project: Titanic and Rainfall Prediction Classifier
- 10.1 Data Collection and Wrangling
- 10.2 Exploratory Data Analysis
- 10.3 Visual Analysis
- 10.4 Predictive Analysis
- 10.5 Present Insights
- 11.1 Generative AI in Data Science
- 11.2 Generative AI in Model Building
- 11.3 Generative AI Final Project
- The default branch is IBM-Data-Science-Python-(Main-Branch).
- The default branch currently contains the README and the most recent presentation/report deliverables.
- Most course content is organized in dedicated branches rather than in a single folder structure within the default branch.
- Together, these branches reflect the progression of the certificate program from SQL and data analysis to machine learning, capstone work, and generative AI applications.