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📊 IBM Data Science Professional Certificate Portfolio

IBM Data Science

IBM Data Science Python Jupyter Pandas R NumPy Scikit-learn Plotly SQL Matplotlib Seaborn Anaconda

🎯 Overview

Welcome to my comprehensive portfolio documenting the completion of the IBM Data Science Professional Certificate! This repository showcases hands-on projects, labs, and assignments covering the complete data science workflow from data collection to predictive modeling and interactive visualization.

🏆 Certificate Details

  • Certificate: IBM Data Science Professional Certificate
  • Issued By: IBM via Coursera
  • Duration: 9 comprehensive courses + Capstone Project
  • Skills Acquired: Data Analysis, Machine Learning, Data Visualization, SQL, Python, Statistical Analysis, Dashboard Development
  • Tools Mastered: Python, Jupyter, SQL, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Folium, Scikit-learn, Dash

📚 Course Structure & Portfolio Contents

1. 🐍 Python for Data Science, AI & Development

  • Topics Covered: Python fundamentals, data structures, functions, classes, file I/O, APIs, NumPy, Pandas
  • Key Files:
    • PY0101EN-*.ipynb - Comprehensive Python notebooks
    • Pandas_Practice.ipynb - Pandas data manipulation
    • practice_project.ipynb - Final integration project

2. 📊 Data Analysis with Python

  • Topics Covered: Data wrangling, exploratory data analysis, model development, evaluation, regression
  • Key Projects:
    • Final Project: House Sales Analysis in King County USA
    • Exploratory_data_analysis_cars.ipynb - Automotive data analysis
    • Model_Evaluation_and_Refinement_cars.ipynb - Model tuning
    • Cheatsheets: Complete module summaries and reference guides

3. 📈 Data Visualization with Python

  • Topics Covered: Matplotlib, Seaborn, Folium, Plotly, Dash, interactive dashboards
  • Key Projects:
    • Airline Performance Dashboard - Interactive flight analytics
    • Australia Wildfire Dashboard - Geospatial visualization
    • Automobile Sales Dashboard - Business intelligence
    • Multiple visualization labs with various chart types

4. 🗄️ Databases and SQL for Data Science with Python

  • Topics Covered: SQL queries, joins, views, stored procedures, transactions, database design
  • Key Projects:
    • Final Assignment: Database querying with SQLite
    • Real-world socioeconomic data analysis
    • Comprehensive practice exercises with screenshots
    • Cheatsheets: SQL reference guides for all operations

5. 🤖 Machine Learning with Python

  • Topics Covered: Supervised/unsupervised learning, regression, classification, clustering, evaluation
  • Key Projects:
    • Final Project: Rainfall Prediction Classifier for Australia
    • Practice Project: Titanic Survival Prediction
    • Credit Card Fraud Detection with Decision Trees & SVM
    • Customer segmentation with K-Means clustering
    • Multiple regression and classification models

6. 🚀 Applied Data Science Capstone

  • Topics Covered: End-to-end data science project, SpaceX launch analysis, presentation skills
  • Key Components:
    • Data Collection: API integration and web scraping
    • Data Wrangling: Data cleaning and preparation
    • EDA: SQL-based and visualization-based analysis
    • Predictive Analysis: Machine learning classification
    • Dashboard: Interactive SpaceX launch dashboard
    • Presentation: Professional report and presentation

7. 📋 Data Science Methodology

  • Topics Covered: CRISP-DM framework, business understanding, data preparation, modeling, deployment
  • Key Files:
    • Process flow exercises and templates
    • Methodology cheatsheets
    • Project planning frameworks

8. 🔧 Tools for Data Science

  • Topics Covered: Jupyter Notebooks, GitHub, RStudio, Anaconda, open-source tools
  • Key Labs:
    • GitHub branching and merging
    • Jupyter notebook creation
    • Open source dataset exploration
    • R basics and visualization

9. 💡 What is Data Science

  • Topics Covered: Data science concepts, career paths, real-world applications
  • Key Materials:
    • Career roadmap and guidance
    • Case studies and applications
    • Data science ethics and best practices

10. 🤖 Generative AI - Elevate Your Data Science Career

  • Topics Covered: AI-assisted data science, data generation, model development, visualization
  • Key Projects:
    • Final Project: Generative AI for Data Science
    • Data preparation and augmentation with AI
    • Database querying with natural language
    • Ethical considerations in AI

🛠️ Technical Skills Demonstrated

Programming & Analysis

Python SQL R

Data Science Libraries

Pandas NumPy Scikit-learn

Visualization Tools

Matplotlib Seaborn Plotly Folium

Dashboard & Web Apps

Dash Jupyter

Databases & Storage

SQLite MySQL

📁 Repository Structure

IBM-Data-Science-Portfolio/
│
├── 📁 Applied Data Science Capstone/
│   ├── 🚀 Introduction/           # Data collection (API & web scraping)
│   ├── 🧹 Data Wrangling/        # Data cleaning and preparation
│   ├── 🔍 Exploratory Data Analysis (EDA)/
│   │   ├── 📊 EDA with SQL/
│   │   └── 📈 EDA with Visualization/
│   ├── 📊 Interactive Visual Analytics and Dashboard/
│   │   ├── 📱 Plotly Dash Dashboard/
│   │   └── 🗺️ Folium Interactive Maps/
│   ├── 🤖 Predictive Analysis/   # Machine learning classification
│   └── 🎤 Presentation/          # Final report and presentation
│
├── 📁 Data Analysis with Python/
│   ├── 📚 Labs/                  # Practice exercises
│   ├── 🏆 Final Project/         # House sales analysis
│   └── 📋 Cheatsheets/           # Module summaries
│
├── 📁 Data Visualization with Python/
│   ├── 📊 Labs/                  # Visualization exercises
│   ├── 📈 Project/               # Advanced visualization projects
│   ├── 🎛️ Dashboard Projects/    # Interactive dashboards
│   └── 📋 Cheatsheets/           # Visualization references
│
├── 📁 Databases and SQL for Data Science with Python/
│   ├── 📚 Labs/                  # SQL practice exercises
│   ├── 🏆 Final Assignment/      # Database querying project
│   ├── 📸 Screenshots/           # Query results and database states
│   └── 📋 Cheatsheets/           # SQL reference guides
│
├── 📁 Machine Learning with Python/
│   ├── 🤖 Labs/                  # ML algorithm implementations
│   ├── 🏆 Final Project/         # Rainfall prediction classifier
│   └── 📋 Cheatsheets/           # ML algorithm references
│
├── 📁 Python for Data Science, AI & Development/
│   └── 🐍 Labs/                  # Python programming exercises
│
├── 📁 Data Science Methodology/
│   └── 📋 Process Frameworks/    # CRISP-DM methodology exercises
│
├── 📁 Tools for Data Science/
│   └── 🔧 Labs/                  # Tool setup and usage
│
├── 📁 What is Data Science/
│   └── 📚 Learning Materials/    # Foundational concepts
│
└── 📁 Generative AI - Elevate Your Data Science Career/
    └── 🤖 Labs & Projects/       # AI-assisted data science

🚀 Getting Started

Prerequisites

  • Python 3.7+
  • Jupyter Notebook
  • SQLite/MySQL
  • Required Python packages (install via requirements.txt)

Setup Instructions

  1. Clone the repository: 
    git clone https://github.com/yourusername/IBM-Data-Science-Portfolio.git
  2. Navigate to the project directory: 
    cd IBM-Data-Science-Portfolio
  3. Install required packages: 
    pip install -r requirements.txt
  4. Launch Jupyter Notebook: 
    jupyter notebook

Requirements

Key packages include:

  • pandas, numpy
  • matplotlib, seaborn, plotly, folium
  • scikit-learn, xgboost
  • dash, jupyter-dash
  • sqlalchemy, pymysql

📈 Key Projects Showcase

🚀 SpaceX Launch Analysis Capstone

  • Objective: Predict SpaceX launch success and analyze launch patterns
  • Technologies: Python, SQL, Plotly Dash, Folium, Scikit-learn
  • Features:
    • Interactive dashboard with launch statistics
    • Geospatial launch site visualization
    • Machine learning prediction model
    • Comprehensive EDA with SQL and Python

🏠 House Sales Analysis in King County

  • Objective: Analyze housing market trends and predict prices
  • Technologies: Python, Pandas, Matplotlib, Seaborn
  • Features:
    • Comprehensive exploratory data analysis
    • Multiple regression models
    • Model evaluation and refinement
    • Feature importance analysis

✈️ Airline Performance Dashboard

  • Objective: Visualize airline on-time performance and flight patterns
  • Technologies: Plotly Dash, Pandas, Interactive widgets
  • Features:
    • Real-time flight statistics
    • Interactive filters and controls
    • Geographical flight distribution
    • Performance metrics by airline

🌧️ Rainfall Prediction in Australia

  • Objective: Predict rainfall using historical weather data
  • Technologies: Scikit-learn, Classification algorithms, Feature engineering
  • Features:
    • Multiple classification models compared
    • Feature importance analysis
    • Model evaluation metrics
    • Cross-validation techniques

🎯 Learning Outcomes

  • End-to-end data science project execution from problem definition to deployment
  • Statistical analysis and hypothesis testing for data-driven insights
  • Machine learning model development for classification and regression tasks
  • Interactive dashboard creation for business intelligence
  • Database management and SQL querying for data extraction
  • Data visualization techniques for effective storytelling
  • Professional presentation skills for technical and non-technical audiences

📊 Skills Gained

✅ Data Collection: API integration, web scraping, database querying
✅ Data Cleaning: Missing value handling, outlier detection, data transformation
✅ Exploratory Analysis: Statistical testing, correlation analysis, pattern recognition
✅ Machine Learning: Supervised/unsupervised learning, model evaluation, hyperparameter tuning
✅ Data Visualization: Static plots, interactive charts, geospatial mapping, dashboards
✅ SQL Proficiency: Complex queries, joins, aggregations, database design
✅ Python Programming: Object-oriented programming, library usage, debugging
✅ Business Communication: Report writing, presentation design, stakeholder management

🏆 Achievements

  • ✅ Completed 9-course professional certificate
  • ✅ Built 20+ comprehensive data science projects
  • ✅ Mastered full data science workflow (CRISP-DM)
  • ✅ Developed interactive dashboards for real-world data
  • ✅ Implemented predictive models with 85%+ accuracy
  • ✅ Created professional data science portfolio
  • ✅ Gained hands-on experience with industry-standard tools

🤝🏿 Contributing

This portfolio represents my personal learning journey through the IBM Data Science Professional Certificate. While this is primarily a showcase of my work, I welcome discussions, feedback, and collaborations on data science projects.

📄 License

This project is for portfolio purposes and contains educational materials from the IBM Data Science Professional Certificate. The code implementations are my own work.

📧 Contact

Willie Conway

⭐ If you find this portfolio helpful or inspiring, please give it a star! ⭐

Last Updated: January 2026
Status: 🟢 Portfolio Complete | 🔄 Continuously Updated with New Projects
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