Graduate Studies Reports Access
Graduate Course Proposal Form Submission Detail - ESI6608
Tracking Number - 5286
Current Status:
-
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments:
Detail Information
- Date & Time Submitted: 2015-10-09
- Department: Industrial and Management Systems Engineering
- College: EN
- Budget Account Number: TPA1000021030000000000000
- Contact Person: Mingyang Li
- Phone: 8139745579
- Email: mingyangli@usf.edu
- Prefix: ESI
- Number: 6608
- Full Title: Engineering Analytics I
- Credit Hours: 3
- Section Type: C -
Class Lecture (Primarily)
- Is the course title variable?: N
- Is a permit required for registration?: N
- Are the credit hours variable?: N
- Is this course repeatable?: Y
- If repeatable, how many times?: 1
- Abbreviated Title (30 characters maximum): Engineering Analytics I
- Course Online?: B -
Face-to-face and online (separate sections)
- Percentage Online: 0
- Grading Option:
R - Regular
- Prerequisites: EGN 3443 Probability and Statistics (or equivalent with instructor permission)
- Corequisites: none
- Course Description: Introductory course to statistical learning for data science and analytics. The course develops skills and knowledge to analyze and interpret large amounts of data in order to extract patterns and gain insights for problem solving and decision making.
- Please briefly explain why it is necessary and/or desirable to add this course: Replacing Selected Topics with Permanent number; already listed in program
- What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service? Current Fall 2015 semester graduate enrollment is approximately 22 students. This graduate enrollment (20-25 students) is also typical of previous Fall and Spring offerings of this course in the past.
- Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Yes, 3 or more times
- What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) PhD in Industrial Engineering or related discipline such as Statistics
- Objectives: The objective is to provide the student with a variety of data mining and statistical learning techniques, with emphasis on concepts and applications.These techniques include regression, classification, clustering, dimension reduction and high dimensional analysis.
- Learning Outcomes: At the end of the course, students will be able to: 1. have a basic understanding of the principles and methods for statistical learning, 2. identify the appropriate methods, perform the correct analysis and make the right interpretation, 3. utilize the statistical software R to implement different methods for real data analysis.
- Major Topics: Basic Concepts and Preliminaries
Introduction and Overview
R tutorial and Data Visualization
Descriptive Statistics, Inferential Statistics
Regression I: Linear Regression
Simple Linear Regression
Multiple Linear Regression
Classification I
Logistic Regression
Discriminant Analysis: LDA, QDA
K-Nearest Neighbors Classification
Model Selection and High Dimensional Data Analysis
Subset Selection: best subset selection, forward selection, backward selection
Model Selection Criterion: Cp, AIC, BIC, adjusted R2
Validation and Cross-validation
High Dimensional Data Analysis: Shrinkage Methods, Dimension Reduction Methods
Regression II
K-Nearest Neighbors Regression
Polynomial Regression and Basis Expansion
Splines
Generalized Additive Models
Classification II: Support Vector Machine
Tree-based Methods and Ensemble Learning
Decision Trees
Ensemble Learning: Bagging, Random Forests, Boosting
Clustering
K-means Clustering
Hierarchical Clustering
- Textbooks: James, G., Witten, D., Hastie, T. and Tibshirani, R., An Introduction to Statistical Learning: with Applications in R, Springer Texts in Statistics, 2013. The e-book is available from the USF Library. ISBN No. for the hard copy: 9781461471387
- Course Readings, Online Resources, and Other Purchases: All computational problems are to be completed using the R programming language.
The software can be downloaded at http://www.r-project.org/.
- Student Expectations/Requirements and Grading Policy: Grading: Homework & Quiz 40%, Exam I Midterm 25%, Exam II Final 35%
Homework will be assigned on a regular basis throughout the semester. Many homework problems also require programming in R. Homework should be submitted by the assigned date through CANVAS. No late homework is acceptable. Students will be expected to attend class. Quizzes will be given from time to time.
There will be a midterm exam and a final exam. Both will be in class. Make-up exams must be requested at least one week prior to the date the exam is held.
- Assignments, Exams and Tests: Homework will be assigned on a regular basis throughout the semester. Many homework problems also require programming in R. Homework should be submitted by the assigned date through CANVAS. No late homework is acceptable. Students will be expected to attend class. Quizzes will be given from time to time.
There will be a midterm exam and a final exam. Both will be in class.
- Attendance Policy: Course Attendance at First Class Meeting – Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation – Registration - 4.0101,
http://usfweb2.usf.edu/usfgc/ogc%20web/currentreg.htm)
Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)
In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. It’s the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.
- Policy on Make-up Work: Make-up exams must be requested at least one week prior to the date the exam is held. Make-up exams will not be given without a valid medical (or other formal) excuse.
Academic Integrity: Academic honesty is fundamental to the activities and principles of a university. All members of the academic community must be confident that each person’s work has been responsibly and honorably acquired, developed and presented. Any effort to gain an advantage not given to all students is dishonest whether or not the effort is successful.
The academic community regards academic dishonesty as an extremely serious matter, with serious consequences that range from probation to expulsion. When in doubt about plagiarism, paraphrasing, quoting, or collaboration on assignments, consult the instructor. In this course dishonesty in any form will not be tolerated. If any student is caught cheating, plagiarizing, copying, or in any other way attempting to represent the work of others as his/her
own, the student will receive a failing grade in the course. There will be no exceptions to this rule. A few extra points in an examination are not worth the risk of receiving a grade of F for the course.
- Program This Course Supports: MSEM, MSIE
- Course Concurrence Information: other graduate programs in engineering or math
- if you have questions about any of these fields, please contact chinescobb@grad.usf.edu or joe@grad.usf.edu.