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Data Analytics Fall 2022

Class Listing: ITWS 4600/ITWS 6600/ MATP 4450/ CSCI 4960/ MGMT 4962/ MGMT 6962/ BCBP 4960 Course 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 SOUTH Section2: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 email Instructor Office Location: Lally 315. 

Teaching Assitant Office Hours: 2 pm - 4 pm ET OR by appointment via email

Teaching Assitant Office Location: Lally 205

Syllabus/ Calendar

Refer to the Readings/ Assignments/ References list for each week (see below).

Reference material (available through RPI library - RCS login required):

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:

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:

Class 7 Reading Assignment:

Class 8-9 Reading Assignment: None

Classes 10-13 Reading Assignment: None

Course goals:

  • 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

Academic Integrity:

Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts that violate this trust undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities and the Graduate Student Supplement (For 6000 level and above courses) define various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. In cases where help was received, or teamwork was allowed, a notation on the assignment should indicate your collaboration. Submission of any assignment that is in violation of this policy will result in (1) an academic (grade) penalty and (2) reporting to Associate Dean of Academic Affairs and either the Dean of Students (for Undergraduates) or the Dean of Graduate Education (for Graduate students). In this course, the academic penalty for a first offense is zero grade for the relevant portion of the grade. A second offense will result in failure of the course. If you have any questions concerning this policy before submitting an assignment, please ask for clarification.

Academic Accommodations:

Rensselaer Polytechnic Institute strives to make all learning experiences as accessible as possible. If you anticipate or experience academic barriers based on a disability, please let me know immediately so that we can discuss your options. To establish reasonable accommodations, please register with The Office of Disability Services for Students (mailto:dss@rpi.edu; 518-276-8197; 4226 Academy Hall). After registration, make arrangements with me as soon as possible to discuss your accommodations so that they may be implemented in a timely fashion.”

COVID-19 code of conduct : This code will apply to any class that meets fully or partially in an on-campus physical classroom for in-person instruction. Violations: Refusal to comply with the COVID-19 code of conduct will be treated just as any classroom disruption, which will receive a request for immediate compliance, failing which the student will be asked to leave the classroom. Any further noncompliance will result in the dismissal of the entire class. All Covid-19 related violations will be reported by the instructor to the Compliance Officer at Lally School, and the Dean of Students. A student found to be in violation of the code, or required repeated reminders for compliance, will be asked to participate in all classes remotely. This is to protect their health and safety as well as the health and safety of their classmates, instructor, and the university community.

Traffic Flow and Social Distancing: Students and faculty will respect the need for social distancing. They are required to follow the traffic flow arrows posted in all rooms and buildings, including bathrooms and common areas.

In-Class Seating: Students should sit in the appropriate designated seating in the classroom. Students are not allowed to move furniture or sit in seats not designated by the Institute.

Cleaning of Spaces: Students are encouraged to clean the surfaces of the chairs/tables/desks they occupy before they sit down and as they prepare to leave. Cleaning and sanitizing solutions will be provided in the classroom.

Students who are ill, under quarantine for COVID-19, or suspect they are ill should not come to class. All faculty will make every reasonable effort to accommodate the student’s absence and will communicate that accommodation directly to the student. Students who need to report an illness should contact the Student Health Center via email or call 518-276-6287. For students seen off campus, a student may request an excused absence via www.bit.ly/rpiabsence with an uploaded doctor’s note that excuses them.

ON-CAMPUS HEALTH & WELLNESS SUPPORT

Student Health Center*

Mon-Fri, 8:30 am – 5:00 pm EST

The mission of the Student Health Center (SHC) is to keep students healthy so that they may achieve their academic, personal, and athletic goals. The SHC provides confidential, accessible, cost-effective, current evidence-based treatment for acute and chronic physical health problems. At this time, appointments are being offered virtually (phone and video). Call 518-276-6287 to schedule an appointment, or schedule one through your Student Health portal. There are no walk-in appointments available at the Student Health Center during this time.

*information subject to change based on pandemic conditions

Counseling Center*

Mon-Fri, 8:30 am – 5:00 pm EST (some weekday evening hours available by appointment)

The goal of the Counseling Center is to help students maximize their sense of well-being as well as their academic, personal, and social growth. Appointments are free and confidential, and in-person at the Counseling Center, 4th Floor of Academy Hall. Some WebEx and phone appointments will be offered as needed. Please contact the Counseling Center for this service. Appointments can be made by calling 518-276-6479 or email: counseling@rpi.edu Counseling Center staff are available in case of a crisis on evenings and weekends (call Public Safety at 518-276-6611 and ask to speak with the on-call counselor).

*information subject to change based on pandemic conditions

Office of Health Promotion

Health promotion initiatives at Rensselaer are evidence-based and comprehensive efforts to improve the health knowledge, behaviors, and skills of Rensselaer students. Health Educators provide campus programming on a variety of health topics, and are available for one-on-one consultations around issues including, but not limited to: sleep hygiene, mental health, sexual health, alcohol and other drugs, LGBTQIA+ topics, sexual assault prevention, and more. All appointments are free and confidential and take place via WebEx. To schedule an appointment, email: healthed@rpi.edu  Follow us on social media for daily health tips and event information!  Instagram: rpi.studenthealth |Twitter: @RPIhealth |

Facebook: RPI Student Health Services | Discord: https://discord.gg/8DZJJ38zWj

 


Course: Data Analytics

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