Introduction to Data Science

Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).

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