Mining Massive Datasets

Course Description

Welcome to the fascinating world of Mining Massive Datasets, an advanced-level computer science course offered by StanfordOnline. This comprehensive course is based on the groundbreaking text "Mining of Massive Datasets" by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who also happen to be your esteemed instructors. Dive deep into the realm of big data analysis and discover cutting-edge techniques to extract valuable insights from vast amounts of information.

What You'll Learn

In this course, you'll gain expertise in a wide array of data mining techniques and algorithms essential for handling massive datasets. You'll master the intricacies of MapReduce systems, explore locality-sensitive hashing, and delve into algorithms for data streams. The course will equip you with skills in web link analysis using PageRank, frequent itemset analysis, and clustering techniques. You'll also learn about computational advertising, recommendation systems, and social network graph analysis. Additionally, you'll explore dimensionality reduction and various machine learning algorithms tailored for big data.

Prerequisites

This course is designed for graduate students and advanced undergraduates in Computer Science. To succeed, you should have a strong foundation in:

• Data structures
• Algorithms
• Database systems
• Linear algebra
• Multivariable calculus
• Statistics

Course Topics

• MapReduce systems and algorithms
• Locality-sensitive hashing
• Algorithms for data streams
• PageRank and Web-link analysis
• Frequent itemset analysis
• Clustering
• Computational advertising
• Recommendation systems
• Social-network graphs
• Dimensionality reduction
• Machine-learning algorithms

Who This Course Is For

• Graduate students in Computer Science looking to specialize in big data analysis
• Advanced undergraduates in Computer Science seeking to challenge themselves
• Data scientists and engineers wanting to enhance their skills in handling massive datasets
• Professionals in tech industries aiming to stay current with cutting-edge data mining techniques

Real-World Applications

• Develop efficient algorithms for processing and analyzing big data in tech companies
• Improve search engine rankings and web analytics for online businesses
• Create sophisticated recommendation systems for e-commerce platforms
• Optimize advertising strategies using computational techniques
• Analyze social networks to gain insights into user behavior and trends
• Implement machine learning algorithms for predictive analytics in various industries
• Enhance data processing pipelines using MapReduce and other distributed computing techniques
• Improve data compression and information retrieval systems using dimensionality reduction

Syllabus Overview

While a detailed syllabus is not provided, the course closely matches the content of Stanford's CS246 course and covers the following major topics:

1. MapReduce systems and algorithms
2. Locality-sensitive hashing
3. Algorithms for data streams
4. PageRank and Web-link analysis
5. Frequent itemset analysis
6. Clustering
7. Computational advertising
8. Recommendation systems
9. Social-network graphs
10. Dimensionality reduction
11. Machine-learning algorithms