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Data Analytics Fall 2024

Class Listing: ITWS 4600/ITWS 6600/ MATP 4450/ CSCI 4600/ MGMT 4962/ MGMT 6600/ BCBP 4960  

Course Numbers: 77982, 78861, 79589, 78862, 79427,  80323, 80328,  80380   

Instructor: Ahmed Eleish - eleisa2 at rpi dot edu  

TA: Alyssa Bigness - bignea2 at rpi dot edu  

Meeting times:

Section1:Time/Location: In-person - Tue/Fri: Time: 10:00am ET - 11:50am ET ; Location: Troy 2018

Section2:Time/Location: In-person – Tue/Fri: Time: 2:00 pm ET – 3:50pm ET ; Location: Troy 2018

Instructor Office Hours: Wednesday from 1:30 pm ET – 3:30 pm ET/Thursday 2:30 - 4:00 or by appointment via email  

Instructor Office Location: Amos Eaton 134 

TA Office Hours: Mon 12-2pm ET / Wed 2-3pm   

TA Office Hours Location: Lally 205

Syllabus/ Calendar

Refer to the Reading/ Assignment/ Reference list for each week (see below).

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

General Outline of the Course Calendar:

Assignment Schedule

Current assignment structure - no final exam! :-)

• Assignment 1: Review of a DA Case Study. - 5% (written)
• Assignment 2: Lab03 - DIstributions, LInear Models, Classification & Clustering – 10% (written + figures) - due Oct 18
• Assignment 3: Preliminary and Statistical Analysis. - 15% (written + figures) - due Oct 22
• Assignment 4: Term project proposal. - due Oct 15/18 - 5% (oral/written)
• Assignment 5: Patterns, trends, relations: model development and evaluation. - 15% (written + figures) - posted Nov 8 - due Nov 29
• Assignment 6: Term project - 30% (25% written, 5% presentation - oral/poster) - due Dec 10
• Assignment 7: Predictive and Prescriptive Analytics - 15% (15% written + figures) - posted Nov 18 - due Dec 06
• 5% participation (labs)

Group 1 - Intro/ Setup

Group 2 - Patterns, relations, descriptive analytics

Group 3 - Predictive Analytics

Group 4 - Evaluating and validating, prescriptive analytics

  • Week 13 (Nov. 19/ Nov. 22): Cross-validation, Revisiting Regression - local methods, Lab - Cross-validation, Regression - local methods and continue project and assignment work.
  • Week 14 (Nov. 26/ Nov. 29: No classes (Thanksgiving Break)/ Dec. 03): Local Regression ctd, Mixed Models, Optimizing, Iterating, (Fischer Linear Discriminant) Prior Lab Review, Hierarchical Linear and Mixed Models, Assignment 7 due.
  • Week 15 (Dec. 06/ Dec. 10): Last Day of Data Analytics Classes: 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, 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.”

Rensselaer Polytechnic Institute On- and Off-Campus Support Resources: Fall 2024 
Remember, seeking help is a strength, not a weakness


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 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 
    
Disability Services for Students 
The Office of Disability Services for Students (DSS) assists Rensselaer students with disabilities in gaining equal access to academic programs, extracurricular activities, and physical facilities on campus. DSS is the designated office at Rensselaer that obtains and files disability-related documentation, assesses for eligibility of services, and determines reasonable accommodations in consultation with students. Call 518-276-8197 or email dss@rpi.edu for more information. 


Course: Data Analytics

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