IDC 6145 Big Data for Data Science

• Course Number: IDC 6145
• Credit hours: 3
• Prerequisites: Basic programming knowledge, Statistics fundamentals

Course Description

This course provides a comprehensive introduction to Big Data concepts and technologies in the context of Data Science. Students will learn about distributed computing, parallel processing, and how to handle data at scale using modern Big Data frameworks.

Learning Objectives

After completing this course, students will be able to:

• Understand core Big Data concepts and the Big Data lifecycle
• Implement MapReduce algorithms using Hadoop
• Develop data processing applications using Apache Spark
• Apply distributed computing principles to solve real-world problems
• Design and implement scalable data processing pipelines
• Evaluate and optimize Big Data solutions

Chapters

• Chapter 1: Introduction To Big Data
  • Introduction To Big Data
• Chapter 2: Big Data Lifecycle
  • Big Data Life Cycle
• Chapter 3: Data Storage For Big Data
  • Taxonomy of Data Storage: From Local to Cloud
  • Informatics
• Chapter 4: Data Processing For Big Data
  • Big Data Processing
• Chapter 5: PySpark Programming
  • PySpark Environment For Learning
  • PySpark Programming for Big Data
  • PySpark Programming: RDD
  • PySpark Programming: Spark SQL and DataFrame API
  • Notebooks for practice
• Chapter 6: Data Analysis for Big Data
  • Data Analysis for Big Data: Overview
  • Machine Learning And Deep Learning For Data Analysis
• Chapter 7: Other Topics in Big Data
  • Streaming for Big Data
  • Cloud Computing For Big Data
  • Security, Privacy, and Compliance for Big Data

Summary

• Course Content Summary
• Big Data Glossary

Assignment Instructions

• Project