📊 IBM Data Science Professional Certificate Portfolio

🎯 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

Data Science Libraries

Visualization Tools

Dashboard & Web Apps

Databases & Storage

📁 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
│
└── 📜 README.md

🚀 Getting Started

Prerequisites

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

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.

📧 Contact

Sanketh Ks

• GitHub: @Sankethks27
• LinkedIn: Sanketh Ks
• Email: sankethks27@gmail.com

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