NEW COURSE PROPOSAL NCP USC Columbia, Lancaster, Salkehatchie, Sumter & Union campuses

INSTRUCTIONS: This form is used to add a new course to the University course database. This form is available online at www.sc.edu/provosVacadQI'Q9.

Date: 8118/2014 Campus: _C..::;...=.:ol=u=m=b=ia=------------------College/School: College of Engineering & Computing

Department (if applicable): Computer Science and Engineering

181 Undergraduate 0 Graduate

PROPOSED COURSE INFORMATION

Course Designation: CSCE 587 4-letter Designator Prefix Course Number Suffix

# Credit Hours: 3 0 Variable 181 Fixed #Times Course Can Be Taken: 1 -~-

Cross-listed with which course? 4-letter Designator Prefix Course Number Suffix

Course Description: (50-word limit) This course covers foundational techniques and tools required for data science and big data analytics. The course focuses on concepts, principles, and techniques applicable to any technology environment and industry and establishes a baseline that can be enhanced by further formal training and additional real-world experience.

Course Prerequisites/Corequisites: I Prereq: STAT 509 or STAT 515

~-----------------------~

Course Delivery Location: [81 USC Campus 0 Off-Campus site (If off-campus delivery is being requested, attach a completed Off-Campus Delivery (OCD) form.)

Course Delivery Method: [81 Traditional Delivery 0 Distance Technology Delivery (streaming video, welrbased, CD/DVD)

(If distance technology delivery is being requested for the first time, attach a completed Distance Education Delivery (OED) form.)

Proposed Effective Term -Change to database/bulletin effective no sooner than:

Year: 2015 [81 Fall 0 Spring 0 May Session 0 Summer I 0 Summerll RECEIVED Required Resources: Does this course require additional faculty, facilities, library resources or funding? SEP Dl

2014 0 Yes [81 No (If yes, attach letters of commitment from appropriate offlcial(s).)

Grading System: [81 Standard 0 Pass/Fail Only 0 Not Auditable USC Graduate School Rationale for grading system other than standard:

Enrollment Restrictions: Restricted to: ----------------------------------------Excluded:

Special Permissions required? 0 Department 0 Instructor

NCP-Page 2 IMPACT ON OTHER ACADEMIC UNITS & CAMPUSES Does the proposed course affect the curriculum, students or academic interest of any other unit at USC Columbia

or on a USC Regional Campus? DYes ONo

Identify which unit(s)/campus(es)

(If yes, please attach letters of concurrence from relevant units and/or the Office of System Affairs.)

REQUIRED ATTACHMENTS (The following documents as appropriate must be attached to this form before submission)

t8l Course syllabus (see http://www.sc.edu/provost/acadprog/courseslindex.shtml for syllabus component guidelines and template syllabus)

0 Basic bibliography (list of required texts and readings)

[81 Justification Form (JUS)

0 Letters of concurrence (if appropriate)

0 Letter(s) committing resources (if appropriate)

0 Related course forms (if appropriate) All forms are available at www.sc.edu/provost/acadprog

0 Distance Education Delivery (OED) Form (initial approval enabling course to be offered via distance technology)

CONTACT INFORMATION Contact Person:

REQUIRED APPROVALS

Department Chair:

Academic Dean:

FacSenate Cours&Curric/ Dean of the Graduate School (as appropriate):

Manton Matthews Print name

mm@sc.edu Email Address

To Y'.Y Ambler Print name

a.rohl<=r~ cee .~ct.~ Email Address

Print name

Email Address

Date of Faculty Governance Approval (if appropriate)

Professor Title

777-0928 Phone Number

8/ 18/14 Date

Phone Number

Signature

Phone Number

0 Graduate Council 0 Faculty Senate

slzz/zot4 IDBte I

1>/zg}ZPIV Date

Date

Umversity of South Carolina I Provost Office I NCP Form Oct2010 v4

JUSTIFICATION FORM JUS USC Columbia, Lancaster, Salkehatchle, Sumter & Union campuses

INSTRUCTIONS: Pfease attach a statement explaining the justification for the proposed program or course action. This form is available online at www.sc.edu'provosVacadprog. ..

Date: 8/18/14 Campus: _..:::C~ol:.::u:.::::m::.::b::..:::ia=-------------------College/School: College of Engineering & Computing

Department (if applicable): Computer Science & Engineering

Degree Program (if applicable): ______________________ _

181 Undergraduate 0 Graduate

A "Big Data Analytics" class been taught for the last few years as a Special Topics (CSCE 590) class. It has been very popular with our students. There is a growing demand for experts in data analysis, databases, and machine learning echniques. CSCE 587 will help satisfy that demand.

University of South Carolina I Provost Office I Justification Form Oct 2010 v3

CSCE 587 Big Data Analytics Spring 2015 Swearingen 3D22 Tfh 10:05-11 :20am

Course Description

Big Data Analytics

John Rose rose@cse.sc.edu/7-2405 Office: Swearingen 3A67 Office hours: Tfh 3:30-5pm NoTA

CSCE 590- Big Data Analytics (3) Prereq: STAT 509 or STAT 515. This course covers foundational techniques and tools required for data science and big data analytics. The course focuses on concepts, principles, and techniques applicable to any technology environment and industry and establishes a baseline that can be enhanced by further formal training and additional real-world experience.

Course Overview This course introduces the student to concepts of big data management, data mining techniques and the underlying statistics that support bid data analytics. Since this is a 500-level course, relevant 500 level courses such as Database (CSCE 520) and Statistics for Engineers (STAT 509) cannot be listed as prerequisites. Consequently, lecture time will be devoted to addresses necessary topics from these courses. In this course we will use the programming language R as the primary tool for analysis.

Learning Outcomes By the end of the course the student will be able to:

1. deploy a structured lifecycle approach to data science and big data analytics projects 2. select visualization techniques and tools to analyze big data and create statistical models 3. use tools such as R and RStudio, and MapReduce/Hadoop.

Required Texts, Other Materials, Suggested Readings This course does not have a required text. However, ad hoc readings from the field will be assigned. In addition, material from "Data Science and Big Data Analytics Student Guide" distributed by EMC Education Services will be provided to the students.

Course Delivery Structure The course will be delivered in a computer lab with 50% of the time devoted to lectures and the other 50% devoted to hands-on lab exercises.

Course Requirements Readings: Students will read lecture material assigned for each class prior to the class. Homework: Students will complete assignments demonstrating mastery of material. These will be due at the beginning of class.

Course Outline/Schedule Lecture 1: Introduction to Big Data Analytics Lecture 2: DBMS Overview

Lecture 3: Introduction to R and RStudio Lecture 4: Basic analysis in R Lecture 5: Intermediate R Lecture 6: Intermediate analysis in R Lecture 7: Visualization and Data Exploration Lecture 8: K-means Clustering. Lecture 9: Independent Sample Tests Lecture 10: Basic Association Analysis Lecture 11: Association Rule Speedup Lecture 12: Linear regression part 1 Lecture 13: Linear regression part 2 Lecture 14: Logistic regression Lecture 15: Naive Bayes Lecture 16: Decision trees part 1 Lecture 17: Decision trees part 2 Lecture 18: Review for Midterm Exam Lecture 19: Midterm Exam Lecture 20: Introduction to Hadoop and HDFS Lecture 21: Using R with Hadoop Lecture 22: First R!Hadoop program Lecture 23: Intermediate R!Hadoop programming Lecture 24: Pig, Hive, and HBase Lecture 25: Discussion of rmr2 Project Lecture 26: Support Vector Machines Part 1 Lecture 27: Support Vector Machines Part 2 Lecture 28: Review for Final Exam

Assignments Readings: Students will read lecture material assigned for each class prior to the class. Readings will be assigned at the end of the preceding class.

Homework: Students will complete assignments demonstrating mastery of material. These will be due at the beginning of class. Graded written evaluations will be returned one week after submission.

HW 1: R Homework assignment

HW 2: K-means homework assignment

HW 3: Association Analysis homework assignment

HW 4: Linear and logistic regression homework assignment

HW 5: Naive Bayes homework assignment

HW 6: Decision tree homework assignment.

HW 7: rmr2 Project

Midterm exam: Covers lectures l - 17: In-class exam as well as take-home applied-exam

Final exam: Covers entire semester: In-class exam as well as take-home applied-exam

Grading S~heme Final grade: 90 <=A, 87 <= B+ <90, 80<= B < 87,77 <= C+ < 80, 65<= C < 77,60 <= D+ <65, 50 <= D < 60, F < 50 Grades will be calculated from homework (50%), midterm (20%), Final Exam (30%).

Differen~e between Undergraduate and Graduate Work: Graduate students are assigned additional problems in both homework and exams.

Course Policies Attendance: Attendance is mandatory. Students will be expected to have read the material for each lecture prior to the lecture and to be able to actively participate in discussions during class.

Tardiness, late assignments: homework is due at the beginning of class. Late assignments will be charged 20% per day.

Violations of academic honesty: Assignments and examination work are expected to be the sole effort of the student submitting the work. Students are expected to follow the Universiry of South Carolina Honor Code and should expect that every instance of a suspected violation will be reported. Students found responsible for violations of the Code will be subject to academic penalty under the Code in addition to whatever disciplinary sanctions are applied.

Policy on disabilities or special needs: Any student with a documented disability should contact the Office of Student Disability Services at 803-777-6142 to make arrangements for appropriate accommodations.