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ABET Course Syllabus Course Title Topics in Data Science Course Number Total Credit CS 561 4 Coordinator Russ Abbott Contact Hours 4 hours/week Course Information This course is a core elective in the MS program. a) Catalog Description An examination of the primary data science algorithms and tools that implement them. Lecture 3 hours, recitation/activity 1 hour. b) Prerequisite: CS 461. Course Goals At the end of the course, students are able to Understand, explain, and implement the primary data science algorithms. Major Topics Covered in the Course: C4.5: Decision tree k-means SVM: Support Vector Machines Apriori: frequent itemsets EM: Expedctation-Maximization PageRank AdaBoost kNN: k-Nearest Neighbor Naïve Bayes CART: Classification and Regression Trees Recitation sections Hands-on activities are critical components of computer science courses that have significant programming components. Each week students do a project related to the week’s material. During the recitation section, students describe and explain their work. Explaining what one has done helps develop a deeper understanding of it. Besides pushing them to deepen their understanding, the explanation requirement helps students develop presentation skills they will need after graduation. Textbook Wu, Xingdong, et. al. (2008) “Top 10 algorithms in data mining.” Knowledge Information Systems, 14:1-37, Springer. (Also Chapman and Hall/CRC, 2009.) References Bishop, Christopher M. (2007) Pattern Recognition and Machine Learning. Springer. Conway, Drew (2012) Machine Learning for Hackers. O’Reilly Media. Downey, Allen B. (2011) Think Stats. O’Reilly Media. Hastie, Trevor, Robert Tibshirani, and Jerome Friedman (2011) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. Gelman, Andres, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtan, and Donald B. Rubin (2013) Baysian Data Analysis. Chapman & Hall/CRC. Hofmann, Markus and Ralf Klinkenberg (2013) RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman & Hall/CRC. James, Gareth, Daniella Witton, Trevor Hastie, and Robert Tibshirani (2013) An Introduction to Statistical Learning: with Applications in R. Springer. Janert, Philipp K. (2010) Data Analysis with Open Source Tools. O’Reilly Media. Kruschke, John K. (2010) Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Academic Press. Kuhn, Max and Kjell Johnson (2013) Applied Predictive Modeling. Springer. McKinney, Wes (2012) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media. Miller, Thomas W. (2013) Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R. FT Press. North, Matthew A. (2012) Data Mining for the Masses. Global Text Project. Stone, James V (2013) Bayes’ Rule: A Tutorial Introduction to Bayesian Analysis. Sebtel Press. Assessment [(i) Chengyu and I will send you the list of courses that this section is applicable. (ii) We will include the necessary assignments/projects/rubrics that will be applied in this course that gives the data for direct measures described in the assessment plan] Academic Integrity Cheating will not be tolerated. Anyone cheating or helping someone else cheat will receive a grade of F for the course and will be reported to the proper authorities. ADA Statement Reasonable accommodation will be provided to any student who is registered with the Office of Students with Disabilities and requests needed accommodation.