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Graduate Course Proposal Form Submission Detail - LIS5802

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Current Status: SCNS Liaison Notified of Graduate Council Approval - 2016-05-18
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments: Required for MS in Intel Studies. To GC. Approved 5/12/16. To USF Sys 5/18/16; to SCNS after 5/25/16

  1. Department and Contact Information

    Tracking Number Date & Time Submitted
    5352 2016-01-03
    Department College Budget Account Number
    Library and Information Science AS XXXXX
    Contact Person Phone Email
    Randy Borum 43520

  2. Course Information

    Prefix Number Full Title
    LIS 5802 Information Analytics

    Is the course title variable? N
    Is a permit required for registration? N
    Are the credit hours variable? N
    Is this course repeatable? N
    If repeatable, how many times? 0

    Credit Hours Section Type Grading Option
    3 C - Class Lecture (Primarily) R - Regular
    Abbreviated Title (30 characters maximum)
    Information Analytics
    Course Online? Percentage Online
    O - Online (100% online) 100


    Any undergraduate statistics course



    Course Description

    This course teaches the basics of data science, visualization, and the use of R, a programming language and software environment for statistical computing and graphics.

  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?

    This course will be part of the core (required) for the new M.S. in Intelligence Studies. Between the MS in Intelligence Studies and the MA in Library and Information Science (which has previously drawn students), we expect enrollment of about 25 students per offering.

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

    Yes, 2 times

    D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)

    Doctorate or master's degree in information science, statistics or a related discipline or master's degree with a concentration in information science, statistics or a related discipline (a minimum of 18 graduate semester hours in the teaching discipline). Subject-specific knowledge and expertise in analytics, data/ information visualization and applications of visual communication to data science.

  4. Other Course Information

    A. Objectives

    • Understand the nature of Big Data infrastructure and its core value, possibilities and limitations

    • Review the foundations of Data Science and applied statistics

    • Discuss how different Visualization styles might help to discover or to communicate meaning and insights from data

    • Collect, structure and analyze Big Data sets and apply visualization design and evaluation principles to them.

    • Apply different advanced analytic applications/techniques, including R, and how to use a variety of analytic and visualization tools

    B. Learning Outcomes

    Students will demonstrate the ability to:

    • Outline the data design principles regarding Big Data, explain the basic structure of data, and to deploy a structured lifecycle approach to data science and big data analytics projects

    • Reframe a business challenge as an analytics challenge

    • Apply analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results

    • Select optimal visualization techniques to clearly communicate analytic insights to business sponsors and others

    • Use tools such as R and RStudio, MapReduce/Hadoop, in-database analytics, and window and MADlib functions

    C. Major Topics

    • Overview of Big Data and Introduction to Visualization and Infographics

    • Practice and Applications of Data Science

    • Introduction to R

    • Data Analysis and Exploration

    • Advanced Analytics: K-Means Clustering and Association Rules

    • Advanced Analytics: Linear and Logistic Regression

    • Advanced Analytics: Naïve Bayesian Classifiers and Decision Trees

    • Advanced Analytics: Time Series Analysis and Text Analytics

    • Advanced Analytics: Technology and Tools

    • In-database Analytics: SQL and MADlib

    • Operationalizing the Project and Creating the Deliverables

    • Visualization Tools and techniques

    D. Textbooks

    • EMC Education Services. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. John Wiley & Sons, 2015. ISBN: 978-1-118-87613-8

    • Stephen, Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Burlingame, CA: Analytics Press, 2012. ISBN 978-0970601971

    • Friedman, Alon. Statistics for Library and Information Services: A Primer for Using Open Source R Software for Accessibility and Visualization. Rowman & Littlefield, 2015.

    E. Course Readings, Online Resources, and Other Purchases


    A. Web Repositories:

    i. The White house open source website:

    ii. Library of Congress, National Digital Newspaper website:

    iii. The European statistical Commission reports—Eurostat databases:

    iv. The US Department of Energy; the Fuel Economy Data:

    B. Open Data repositories via R:

    i. AMSsurvey American Math Society Survey Data

    ii. Adler Experimenter Expectations

    iii. Angell Moral Integration of American Cities

    iv. Anscombe U. S. State Public-School Expenditures

    v. CanPop Canadian Population Data

    vi. Davis Self-Reports of Height and Weight


    Bateman, (2010). Useful junk? The effects of visual embellishment on comprehension and memorability of charts. Proceedings of CHI '10, Apr 2010, 2573-2582.

    Cleveland, W.S. Visualizing Data. ISBN 978-0963488404

    Chartera, G. and Ping Zhang. A First Course in Graph Theory. McGraw Hill Higher Education ISBN: 978-0486483684

    Cohen, J., (2009). MAD skills: new analysis practices for big data. 35th International Conference on Very large Data Bases -VLDB 2009.

    Fry, B. (2008). Visualizing data. O’Reilly Publications. ISBN: 978-059651455

    Fry, B. (2012). Show me the numbers: Designing tables and graphs to enlighten. Analytics Press, ISBN: 978-0970601971

    Heer, J. 2010. A tour through the visualization zoo. Communication of the ACM, (53,6), 59-69.

    Knell, R. J. Introductory R: A beginner's guide to data visualization and analysis using R. March 2013. ISBN: 978-0957597105

    Kumar, V. (2013). 101 Design Methods. A structured approach for driving innovation in your organization. Wiley Publication. ISBN: 978-111808348

    Maeda, J. Design by numbers. The MIT Press. ISBN: 012-026213354

    Schoberfer, V. M. and Kenneth, Cukier. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. ISBN: 978-544002692

    Maneesh, A. (2011). Design principles for visual communication. Communications of the ACM, 54(4).

    Yau, N. (2011). Visualize this: The flowing data guide to design, visualization and statistics. Wiley Publications.
Yau, N. (2013) Data points. Wiley Publications.

    Other Resources:

    F. Student Expectations/Requirements and Grading Policy

    • Homework 50%

    • Individual Project 30%

    • Final Exam 20%


    The course will use a standard grading metric based on the weighted analysis of the assignments.

    90%-100% earns an A

    80%-89% earns a B

    70%-79% earns a C

    60%-69% earns a D

    Less than 60% earns an F

    G. Assignments, Exams and Tests

    • Homework - Blog posting: 12 homework assignments will be assigned, one after each class. Homework will reflect the specific class content for that week and will be posted on your blog.

    • Student Project: There will be an individual project. This project will require you to collect or retrieve a large set of data, analyze it and present the end result in visualization form. Be sure to follow the instructor's directions for the project.

    • Final Exam: The final exam will be composed of open research questions pertaining to data design principles, application of analytic techniques and tools to analyze data, identifying insights, selecting and using visualization techniques, and using tools such as R for analysis.

    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,

    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: (

    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

    As your instructor, I would accommodate excused absences by making arrangements with students ahead of time (when possible) or by providing a reasonable amount of time to make up missed work. Arranging to make up missed work is the responsibility of the student. For graded work that requires participation in situ (e.g., discussions, group activities, and some labs), instructors will attempt to provide reasonable alternatives that accomplish the same learning outcomes. Nevertheless, an instructor may determine that missing a certain amount of participation-dependent activities (whether excused or not) precludes successful accomplishment of learning outcomes. In cases like this, instructors, academic advisors, or academic deans may advise students to withdraw from such courses. In cases where excused absences are anticipated in advance, advice on successful accomplish.

    Academic Integrity of Students


    J. Program This Course Supports

    M.S. in Intelligence Studies

  5. Course Concurrence Information

    M.A. in Library and Information Science

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