Introduction to Data Science (coursera)

(Spring 2013)

Syllabus

Part 1: Data Manipulation, at Scale

• Databases
• Traditional Relational Databases and Relational Algebra
• MapReduce
  • Hadoop, algorithms,
  • extensions (Hive, Pig);
• NoSQL
  • Document-Oriented Databases and Eventual Consistency
  • Column-Oriented Databases
  • Tradeoffs of SQL and NoSQL
• Data cleaning, entity resolution, data integration, information extraction

Part 2: Analytics

• Basic statistical modeling, experiment design
• Introduction to Machine Learning
  • Supervised Learning: decision trees/forests, simple nearest neighbor
  • Unsupervised learning: K-Means, multi-dimensional scaling

Part 3: Interpreting and Communicating Results

• Visualization, visual data analytics

Links

• Lecture Notes