Class Listing: ITWS 4600/ITWS 6600/ MATP 4450/ CSCI 4960/ MGMT 4962/ MGMT 6962/ BCBP 4960Course Numbers: 59543, 59730, 59731, 58347, 58618, 58348, 58623, 58703, 58586, 58776
Instructor: Thilanka Munasinghe - munast at rpi dot edu Teaching Assistant (TA): Shivam Sonawane - sonaws at rpi dot edu Meeting times:
Section1:Time/Location: In-person - Tues/Fri: Time: 10:00am ET - 11:50am ET. Classroom Location: VCC SOUTHSection2:Time/Location: In-person – Tues/Fri: Time: 2:00 pm ET – 3:50pm ET. Classroom Location: CII 3206
Instructor Office Hours: Tuesdays/Fridays 12:30 pm ET – 1:30 pm ET OR by appointment via emailInstructor Office Location: Lally 315.
Teaching Assitant Office Hours: 2 pm - 4 pm ET OR by appointment via email
Teaching Assitant Office Location: Lally 205
Refer to the Readings/ Assignments/ References list for each week (see below).
Reference material (available through RPI library - RCS login required):
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (online) (RECOMMENDED)
- Big data Analytics: turning big data into big money
- Big Data Analytics: Turning Big Data into Big Money (online)
- Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph (online)
- Big Data Analytics with R and Hadoop (online)
- R for Everyone: Advanced Analytics and Graphics (online)
- Introduction to Statistical Learning with R - 7th Edition
General outline of the course calendar:
Group 1 - Intro/ Setup
- Week 1 (Aug. 30/ Sept. 02): Introduction to Course, Case Studies, and Preview of Course Material/Refresher on basic statistics.
- Assignment 1
- Week 2 (Sept. 06: No classes—Follows Monday’s schedule/ Sept. 09): Introduction/ refresher on basic statistics continue / Starting with Data and Information Resources, Role of Hypothesis, Synthesis and Model Choices, R/ RStudio introduction, and Intro to Labs.
- Week 3 (Sept. 13: / Sept. 16): Introduction to Analytic Methods, Types of Data Mining for Analytics, Data filtering, hypothesis exploration, visual analysis, model consideration and assessment (lab)
- (Lab) Assignment 2
Group 2 - Patterns, relations, descriptive analytics
- Week 4 (Sept. 20 / Sept. 23): Weighted kNN, Clustering, early decision trees, Exercises for linear regression, kNN and K-means (lab), trees, plotting
- Assignment 3
- Week 5 (Sept. 27/ Sept. 30): Interpreting: Regression, Weighted kNN, Clustering, and Bayesian Inference, Exercises for clustering, plotting, bayesian inference (lab)
- Assignment 4
- Assignment 5
- Week 6 (Oct. 04/ Oct. 07): Assignment 5 presentations (Tuesday and Friday)
- Assignment 6
- Week 7 (Oct. 11 / Oct. 14): Lab weighted kNN, decision trees, random forest
Group 3 - Predictive Analytics
- Week 8 (Oct. 18/ Oct. 21): Cross-Validation Trees, Dimension Reduction, and Multi-Dimensional Scaling
- Week 10 (Oct. 25/ Oct. 28): Applications in Dimension Reduction and Labs
- Week 10 (Nov. 01/ Nov. 04): Support Vector Machines, Lab for Trees, DR, MDS, SVM
- Week 11 (Nov. 08/ Nov. 11): Factor Analysis, Factor Analysis lab
- Week 12 (Nov. 15/ Nov. 18): Interpreting PCA, MDS, DR, and FA, Boosting, Bootstrapping, Bagging, Boosting, Bootstrapping, Bagging (lab)
- Assignment 7
Group 4 - Evaluating and validating prescriptive analytics
- Week 13 (Nov. 22/ Nov. 25: No Classes – Thanksgiving Break): Cross-validation, Revisiting Regression - local methods, Lab - Cross-validation, Regression - local methods and continue on project and assignment work
- Week 14 (Nov. 29/ Dec. 02): Local Regression ctd, Mixed Models, Optimizing, Iterating, (Fischer Linear Discriminant)
- Week 15 (Dec. 06/ Dec. 09): Prior Lab Review, Hierarchical Linear and Mixed Models, Latent Class Mixed Models, Lab, Assignment 7 due
- Week 16: (TBD): Final Project and Poster Due
Reading/ Assignment/ Reference List (see above)
Class 1: Reading Assignment:
- Sports Analytics – Moneyball (http://www.imdb.com/title/tt1210166/),
- Nate Silver (http://en.wikipedia.org/wiki/Nate_Silver)
Class 2 Reading Assignment: prior to Friday's class
Class 3 Reading Assignment: prior to Tuesday's class
Classes 4-5 Reading Assignment: none
Class 6 Reading Assignment:
- http://stat-www.berkeley.edu/users/breiman/RandomForests/ Random Forests
Class 7 Reading Assignment:
- http://aquarius.tw.rpi.edu/html/DA/v15i09.pdf Karatzoglou et al. 2006
- http://aquarius.tw.rpi.edu/html/DA/svmbasic_notes.pdf Vert SVM basic
- http://aquarius.tw.rpi.edu/html/DA/svmdoc.pdf SVM documentation
- http://22.214.171.124/CRAN/web/packages/e1071/vignettes/svmdoc.pdf SVM documentation (updated 2017)
- http://www.stjuderesearch.org/site/data/ALL1/ ALL dataset
- http://www.stanford.edu/group/wonglab/RSVMpage/R-SVM.html RSVM
- http://data-informed.com/focus-predictive-analytics/ /li>
Class 8-9 Reading Assignment: None
Classes 10-13 Reading Assignment: None
- Introduce students to relevant methods to recognize and apply quantitative algorithms, techniques, and interpretation
- To develop students' strategic thinking skills, combined with a solid technical foundation in data and model-driven decision-making.
- Develop the ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems
- Students will examine real-world examples using modern cyberinfrastructure to place statistical and data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
- By the end of the course, students can effectively communicate analytic findings to non-specialists.
Course Learning Outcomes:
- Students to demonstrate knowledge of relevant analytic methods and to recognize and apply quantitative algorithms and techniques and interpret results.
- Students to demonstrate strategic thinking skills, combined with a solid technical foundation in data and model-driven decision-making.
- Students to develop the ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems.
- Students will examine real-world examples to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
- Students must effectively communicate analytic findings to non-specialists
- [6000 Levels]: Students must develop and demonstrate an ability to apply appropriate analytic techniques under conditions of uncertainty, be able to build optimization models that incorporate random parameters: static stochastic optimization, two-stage optimization with recourse, chance-constrained optimization, and sequential decision making
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Course: Data Analytics