Coursera - Introduction to Data Science (2013)

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
Entity resolution, record linkage, data cleaning

Part 2: Analytics

Basic statistical modeling, experiment design, introduction to machine learning, overfitting
Supervised learning: overview, simple nearest neighbor, decision trees/forests, regression
Unsupervised learning: k-means, multi-dimensional scaling
Graph Analytics: PageRank, community detection, recursive queries, iterative processing
Text Analytics: latent semantic analysis
Collaborative Filtering: slope-one

Part 3: Communicating Results

Visualization, data products, visual data analytics
Provenance, privacy, ethics, governance

Part 4: Guest Lectures

Guest Lectures: AMPLab, Datameer, SciDB, more

General
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