Syllabus Help Center
Part 0: Introduction
Data science articulated, data science examples, history and context, technology landscape
Readings
- (example) Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow, Albert-László Barabási, Flavor network and the principles of food pairing, Scientific Reports 1, Article number: 196 doi:10.1038/srep00196
- (example) Acerbi A, Lampos V, Garnett P, Bentley RA (2013) The Expression of Emotions in 20th Century Books. PLoS ONE 8(3): e59030. doi:10.1371/journal.pone.0059030
- (example) Google Flu Trends
- Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, Larry Brilliant, Detecting influenza epidemics using search engine query data, Nature 457, 1012-1014 (19 February 2009) (paywalled)
- David Lazer, Ryan Kennedy, Gary King, Alessandro Vespignani, The Parable of Google Flu Trends: Traps in Big Data Analysis, Science 14 March 2014: Vol. 343 no. 6176 pp. 1203-1205 (paywalled)
- Steve Lohr, Google Flu Trends: The Limits of Big Data, NYTimes, March 28, 2014
- (example) Eigenfactor, and publications
- (example) L'Aquila quake: Italy scientists guilty of manslaughter, BBC
- Discussion of data science and data scientists
- Drew Conway's Venn Diagram
- Mike Loukides, What is data science?, O'Reilly Radar, 2010
- Mike Driscoll, "The Seven Secrets of Successful Data Scientists"
- Origins of "Volume, Velocity, Variety"
- eScience: The Fourth Paradigm (Foreward and Introduction, pages xi - xxxi; Gray's Laws, pages 5-12)
- Chris Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete” , Wired magazine, 2008
- Responses to Chris Anderson, 2008
Part 1: Data Manipulation, at Scale
Databases and the relational algebra
Readings
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How Vertica Was the Star of the Obama Campaign, and Other Revelations
- E. F. Codd, 1981 Turing Award Lecture, " Relational Database: A Practical Foundation for Productivity", 1981 (Think about which arguments from this short piece are still relevant today.)
- [Advanced] Cohen et al.“MAD Skills: New Analysis Practices for Big Data”, 2009
- [Advanced] Erik Meijer, Gavin Bierma co-Relational Model of Large Shared Data Banks, Communications of the ACM, 2011
MapReduce, Hadoop, relationship to databases, algorithms, extensions, language; key-value stores and NoSQL; tradeoffs of SQL and NoSQL
Readings
- Ullman, Rajaraman, Mining of Massive Datasets, Chapter 2
- Stonebraker et al., “MapReduce and Parallel DBMS’s: Friends or Foes?”, Communications of the ACM, January 2010.
- Dean and Ghemawat, “MapReduce: A Flexible Data Processing Tool”, Communications of the ACM, January 2010.
- Rick Cattell, “Scalable SQL and NoSQL Data Stores”, SIGMOD Record, December 2010 (39:4)
- Optional Technical Background: The Hadoop Distributed File System
Data cleaning, entity resolution, data integration, information extraction
Topics in statistical modeling and experiment design
Readings
Introduction to Machine Learning, supervised learning, decision trees/forests, simple nearest neighbor
(NOT COVERED IN LECTURES)
Readings / Talks
Part 2: AnalyticsReadings / Talks
- Elmagarmid, et. al. Duplicate Record Detection: A Survey,
- Koudas, et. al. Record Linkage: Similarity Measures and Algorithms
Topics in statistical modeling and experiment design
Readings
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Chapter 3 of A Handbook of Statistical Analyses Using R
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Gregory Park on overfitting to the leaderboard in a Kaggle Competition
- John P. A. Ioannidis, Why Most Published Research Findings Are False, PLOS One, August 30, 2005
- Benford's Law (wikipedia)
- Frequentist vs. Bayesian Perspectives explained, Jake Vanderplas, UW eScience Institute
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http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/
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http://jakevdp.github.io/blog/2014/06/06/frequentism-and-bayesianism-2-when-results-differ/
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http://jakevdp.github.io/blog/2014/06/12/frequentism-and-bayesianism-3-confidence-credibility/
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http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
Introduction to Machine Learning, supervised learning, decision trees/forests, simple nearest neighbor
Readings
- Xindong Wu et al., Top 10 Algorithms in Data Mining, Knowledge and Information Systems, 14(2008), 1: 1-37. (read section on C4.5)
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Ullman, Rajaraman, Mining of Massive Datasets , Chapter 1
- Pedro Domingos, A Few Useful Things to Know about Machine Learning, CACM 55(10), 2012
Unsupervised learning: k-means, multi-dimensional scaling
Readings
- Xindong Wu et al., Top 10 Algorithms in Data Mining, Knowledge and Information Systems, 14(2008), 1: 1-37. (read section on k-means)
Part 3: Interpreting and Communicating Results
Visualization, visual data analytics
Readings (well, watchings)
- Hans Rosling, The Joy of Stats
- Pat Hanaran, Tools for Data Enthusiasts
- Jeffrey Heer, Michael Bostock, Vadim Ogievetsky, A Tour through the Visualization Zoo, Communications of the ACM, Volume 53 Issue 6, June 2010
Backlash: Ethics, privacy, unreliable methods, irreproducible results
(NOT COVERED IN LECTURES)
- Howard Wen, "Big Ethics for Big Data", O'Reilly Media
- John Markoff, New York Times, Unreported Side Effects of Drugs Are Found Using Internet Search Data, March 13, 2013
- Mike Loukides, Data Skepticism, O'Reilly Media, April 2013
- Gary Marcus and Ernest Davis, Eight (No, Nine!) Problems With Big Data, New York Times, April 6, 2014
- Tim Harford, Big data: are we making a big mistake?, March 28, 2014
- K.N.C., The backlash against big data, The Economist, Apr 20th 2014 (very short)
- See also: Gartner Hype cycle
- George Johnson, New Truths That Only One Can See, New York Times, January 20, 2014
- John P. A. Ioannidis, Why Most Published Research Findings Are False, PLOS One, August 30, 2005
- Dan Mckinley, Whom the Gods Would Destroy, they First Give Real-Time Analytics
Part 4: Graph Analytics
Readings
- Sherif Sakr, Processing large-scale graph data: A guide to current technology, June 2013
Created Mon 25 Jun 2012 7:48 PM CEST
Last Modified Mon 21 Jul 2014 1:51 AM CEST
Last Modified Mon 21 Jul 2014 1:51 AM CEST