Graduate Course Proposal Form Submission Detail - LIS5802
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SCNS Liaison Notified of Graduate Council Approval
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
- 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 Randy Borum 43520 firstname.lastname@example.org
- 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
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.
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.
- Other Course Information
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
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: http://www.data.gov
ii. Library of Congress, National Digital Newspaper website: http://www.loc.gov/chroniclingamerica
iii. The European statistical Commission reportsEurostat databases: http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database
iv. The US Department of Energy; the Fuel Economy Data: http://www.fueleconomy.gov/feg/download.shtm
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, et.al. (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., et.al. (2009). MAD skills: new analysis practices for big data. 35th International Conference on Very large Data Bases -VLDB 2009.
Fry, B. (2008). Visualizing data. OReilly Publications. ISBN: 978-059651455
Fry, B. (2012). Show me the numbers: Designing tables and graphs to enlighten. Analytics Press, ISBN: 978-0970601971
Heer, J. et.al. 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. et.al. (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.
F. Student Expectations/Requirements and Grading Policy
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: (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. Its 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
- Course Concurrence Information
M.A. in Library and Information Science