Download CS 561

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.