📊 Data Science & Big Data

95-885, Fall 2025 – Carnegie Mellon University

This repository contains comprehensive coursework and hands-on implementations for 95-885 Data Science & Big Data. It includes Python notebooks, code files, assignments, and practice exercises covering a wide range of topics across:

• Probability & statistical modeling

• Algorithms & optimization

• Applied Machine Learning

• Big Data processing & distributed systems

• Applied computing & data engineering

🎯 Key Learning Outcomes

1. Designing end-to-end data science and machine learning solutions, from data ingestion and preprocessing to modeling, evaluation, and deployment. Projects reflect real-world use cases—suitable for solving both industry problems and academic research challenges.

2. Hands-on practice with Big Data tools, including:

   • Apache Spark for distributed data processing

   • Hadoop ecosystem tools

   • Cloud data handling

3. Building production-ready pipelines using tools like Pandas, Scikit-learn, PySpark, and Hadoop streaming, and integrating them with machine learning models.

🧠 Contents

Class-Labs/: Jupyter Notebooks used in class labs and practical projects

Assignments/: Clean, tested Python scripts and reports

Documents/: Sample Research Papers

projects/: Capstone or course mini-projects on real datasets

🚀 Skills Gained

By the end of this course, learners will be able to:

1. Handle massive datasets and perform distributed computation

2. Apply statistical methods and ML models to large-scale problems

3. Understand performance bottlenecks in data pipelines

4. Translate academic theory into practical, scalable solutions