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).

Syllabus
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
Taught by Bill Howe

General
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