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University of Washington vía Coursera
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Introduction to Data Science

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Part 0: Introduction 
  • Examples, data science articulated, history and context, technology landscape
Part 1: Data Manipulation at Scale
  • Databases and the relational algebra 
  • Parallel databases, parallel query processing, in-database analytics 
  • MapReduce, Hadoop, relationship to databases, algorithms, extensions, languages  
  • Key-value stores and NoSQL; tradeoffs of SQL and NoSQL
Part 2: Analytics
  • Topics in statistical modeling: basic concepts, experiment design, pitfalls
  • Topics in machine learning: supervised learning (rules, trees, forests, nearest neighbor, regression), optimization (gradient descent and variants), unsupervised learning
Part 3: Communicating Results
  • Visualization, data products, visual data analytics 
  • Provenance, privacy, ethics, governance 
Part 4: Special Topics
  • Graph Analytics: structure, traversals, analytics, PageRank, community detection, recursive queries, semantic web
  • Guest Lectures