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Graduate Course Proposal Form Submission Detail - ESI6608

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Current Status: -
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments:


  1. Department and Contact Information

    Tracking Number Date & Time Submitted
    5286 2015-10-09
     
    Department College Budget Account Number
    Industrial and Management Systems Engineering EN TPA1000021030000000000000
     
    Contact Person Phone Email
    Mingyang Li 8139745579 mingyangli@usf.edu

  2. Course Information

    Prefix Number Full Title
    ESI 6608 Engineering Analytics I

    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

    Credit Hours Section Type Grading Option
    3 C - Class Lecture (Primarily) R - Regular
     
    Abbreviated Title (30 characters maximum)
    Engineering Analytics I
     
    Course Online? Percentage Online
    B - Face-to-face and online (separate sections) 0

    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.


  3. Justification

    A. Please briefly explain why it is necessary and/or desirable to add this course.

    Replacing Selected Topics with Permanent number; already listed in program

    B. 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.

    C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?

    Yes, 3 or more times

    D. 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


  4. Other Course Information

    A. 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.

    B. 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.

    C. 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

    D. 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

    E. 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/.

    F. 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.

    G. 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.

    H. 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.

    I. 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.

    J. Program This Course Supports

    MSEM, MSIE


  5. 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.