Introduction to Data Science on Coursera

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

Readings

• 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

Readings for step 3-4-5

• 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

(NOT COVERED IN LECTURES)Readings / Talks

Readings for step 3-4-5

• 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

(NOT COVERED IN LECTURES)Readings / Talks

Readings for step 3-4-5

• 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

(NOT COVERED IN LECTURES)Readings / Talks

Readings

• Chapter 3 of A Handbook of Statistical Analyses Using R
• 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)

1. Ssupervised learning (rules, trees, forests, nearest neighbor, regression),
2. Optimization (gradient descent and variants),
3. Unsupervised learning

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)
• 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)

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

• structure
• traversals
• analytics
• PageRank
• community detection
• recursive queries
• semantic web

Readings