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Data Analytics Fall 2026

Class Listing: ITWS 4600 / ITWS 6600 / CSCI 4600 / MGMT 4600/ MGMT 6600/ BCBP 4600  

Course Numbers: 78426, 78427, 78953, 78864, 78852, 78809   

Instructor: Ahmed Eleish - eleisa2 at rpi dot edu  

TA: TBA  

Meeting times:

Section 01: Time/Location: In-person - Tue/Fri: Time: 10:00am ET - 11:50am ET ; Location: Lally 104

Section 02: Time/Location: In-person – Tue/Fri: Time: 2:00 pm ET – 3:50pm ET ; Location: Pittsburgh 5216

Instructor Office Hours: Thursday 2:00 - 4:00 or by appointment via email  

Instructor Office Location:  Lally 315 / Amos Eaton 134
 

Syllabus/ Calendar

Outline of the Course Calendar:

Assignment Schedule

Current assignment structure - no final exam! :-)

• Assignment 1: Review of a DA Case Study. / 5% (written) / Aug. 28 - Sep. 04
• Assignment 2: Distributions, Linear Models, Classification & Clustering / 10% (written + figures) / Sep. 29 - Oct. 16
• Assignment 3: Term project proposal / 5% (oral/written) / Sep. 29 - Oct. 13
• Assignment 4: Term project / 30% (25% written, 5% presentation - oral) / Oct. 13 - Dec. 11
• Assignment 5: Regression & Classification with Housing Data / 15% (written + figures) / Oct. 20 - Nov. 06
• Assignment 6: Patterns, trends, relations: model development and evaluation / 15% (written + figures) / Nov. 06 - Dec. 08
• 15% labs
• 5% participation (attendance)
 

Group 1 - Intro / Setup

  • Week 1 (Aug. 28 / Sep. 01 / Sep. 04): Introduction to Course & Preview of Course Materials + Refresher on basic statistics + R/ RStudio introduction and Intro to Labs
  • Week 2 (Sep. 08 / Sep. 11): Refresher on basic statistics continued + Distributions, Probabilities, Role of Hypothesis + lab 1
  • Week 3 (Sep. 15 / Sep. 18): Introduction to Analytic Methods, Types of Data Mining for Analytics, Regression Analysis, Supervised & Unsupervised Machine Learning.

Group 2 - Patterns, relations, descriptive, and predictive analytics

  • Week 4 (Sep. 22 / Sep. 25): Linear models, kNN, Decision Trees, Naive Bayes + lab 2
  • Week 5 (Sep. 29 / Oct. 02): k-Means, PAM, DBSCAN, Hierarchical Clustering + lab 3
  • Week 6 (Oct. 06 / Oct. 09): Evaluating Regression Models / Evaluation Metrics for Classification & Clustering Models
  • Week 7 (Oct. 13): Project Proposal presentations

Group 3 - Model evaluation, dimensionality reduction, more supervised, and unsupervised learning methods

  • Week 8 (Oct. 20 / Oct. 23): Dimensionality Reduction, PCA + lab 4
  • Week 9 (Oct. 27 / Oct. 30): Support Vector Machines + lab 5
  • Week 10 (Nov. 03 / Nov. 06): More Supervised and Unsupervised learning Methods
  • Week 11 (Nov. 10 / Nov. 13): Intro to Network Analysis + lab 6
  • Week 12 (Nov. 17 / Nov. 20): Guest Lecture

Group 4 - Reviewing methods, project advising, and peripheral topics

  • Week 13 (Nov. 24 / Nov. 27): Thanksgiving - No Classes
  • Week 14 (Dec. 01 / Dec. 04): Project Advising
  • Week 15 (Dec. 8 / Dec. 11):  Final project presentations + written reports due Dec. 11

 

Reading/ Reference List

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 the ability to apply appropriate analytic techniques under conditions of uncertainty, and to optimize models that incorporate parameters.

     

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.”


Course: Data Analytics

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