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1 L ~ ;~ NEW COURSE PROPOSAL NCP ~""t!'_ tt:jnj(ntm ~~~ - >"-=:;A usc Columbia, Lancaster, Salkehatchie, Sumter & Union campuses ~~~~;~=:~:-~~;;~~:~;~~::;~~:~~~ -~~= -~ourse to the Universfty course database. Thi~-~~~~ ~~s - ~~~~~~:~_] Date: October 9, 2014 Campus: Columbia College/School: Arts and Sciences Department (if applicable): ---=:.S.:.:::ta:.:.:ti:::.st=-=-ic::=s IZJ Undergraduate IZJ Graduate PROPOSED COURSE INFORMATION Course Designation: STAT letter Designator Prefix Course Number Suffix # Credit Hours: 3 0 Variable j}(fixed #Times Course Can Be Taken: 1 Course Title (24 character limit): Bi:J!>gc.::D:::.:a:::::t=-a _:..:A:.::n:=a=.t.IY...::ti:..::c=--s. Cross-listed with which course? CSCE letter Designator Prefix Course Number Suffix Course Description: (50-word limit) ~oundational tccfintq- u- e_s_a_n--;d-t-oo--;1-s -re_q_u..-ire-d..-fi"""o-r...,.da,t-a-sc..,. e_n_c_e-and big data analytics. Concepts, principles, and echniques applicable to any technology and industry for establishing a baseline that can be enhanced by future tudy.,.., \. ;: Course Delivery Location: t8l USC Campus 0 Off-Campus site (If off-campus delivery is being requested, attach a completed Off-Campus Delivery (OCD) form.) Course Delivery Method: ~ Traditional Delivery 0 Distance Technology Delivery (streaming Video. web-based. 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 ~ Fall D Spring D May Session D Summer I D Summer II Required Resources: Does this course require additional faculty, facilities, library resources or funding? 0 Yes 0 No (If yes, attach letters of commitment from appropriate official(s).) Grading System: ~ Standard 0 Pass/Fail Only 0 Not Auditable Rationale for grading system other than standard: I _ Enrollment Restrictions: Restricted to: Excluded: Special Permissions required? 0 Department 0 Instructor

2 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? t8l Yes C No Identify which unit(s)/campus(es) Compute~. Science and t;ng!nccring (via cross-lis!...:: acc.p.a::.p:...:ro;;..:v..=a::.~. l) _ f!f yes. please attach letters of concurrence from relevant units andlor the Office of System Affairs.) REQUIRED ATTACHMENTS (The following docum P-n~ a~ appropriate must be attached to this form before submission) P?J Course syllabus (see for syllabus component guidelines and template syllabus) R Basic bibliography (list of required texts and readings) i?s1 Justification Form (JUS) 0 Letters of concurrence (if appropriate) u Letter{s) committing resources (if appropriate) [ J Related course forms (if appropriate) All forms are available at n Distance Education Delivery (DED) Form (initial approval ~nabling course to be offered via d i~~ance technology) CONTACT INFORMATION Contact Person: =B~an ~ab~in=g. l!abing(1ilstat.sc.cdu, ~.,. Emajl Address -. Associate Professor Title ~Pro~n~e~~~~ October 9, Date Department Chair: John Grego - -- Emeii Address Academic Dean: FacSenate Cours&Curricl Dean of the Graduate School (as appropriate): Mury Ann Byrnes l>y!!}~~-.1!1.~ ~-<! ~ :s~:.~du_ Address Adtlress SigniJture Phona Number Date

3 JUSTIFICATION FORM USC Columbia, Lancaster, Salkehatchie, Sumter & Union campuses JUS INSTRUCTIONS:. Please atfach a statement explaining the justification for the piopased prograin or course action. This form.~-~ is available online at Date: October 9, 2014 Campus: -~C:..:::.o=lu:.::m=-=b::.:i=.a College/School: Arts and Sciences Department (if applicable): Statistics --~~~~ Deg~eP~g~mofappUcable~~~~~~~~~~~~~~~~~~~~~~~~ t8j Undergraduate 181 G~duate CSCE 587 was recently proposed by the Department of computer Science and Engineering, with a letter of support from Statistics, and approved by the Faculty Senate. This proposal simply creates the correspondingly numbered course in statistics to facilitate cross-listing (similar to CSCE 582 =STAT 582). Un1versity of S,;utn Carolina i Provost Office! Justification Form Oct20 IQ v3

4 CSCE 587 [=STAT 587]: Big Data Analytics CSCE 587 (=STAT 587] Big Data Analytics Spring 2015 John Rose roserw.cse.sc.edu/ Office: Swearingen 3A67 Course Description CSCE 587 [=STAT 587]- Big Data Analytics (3) Prereq: STAT 509,513, or 515. Foundational techniques and tools required for data science and big data analytics. Concepts, principles, and techniques applicable to any technology and industry for establishing a baseline that can be enhanced by future study. Course Overview This course introduces the student to concepts of big data management, database management, data mining techniques and the underlying statistics that support big data analytics. 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: I. deploy a structured lifecycle approach to data science and big data analytics projects 2. ~set visualization techniques and tools to analyze big data and create statistical models ~~~setools 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 a-55-igned- Inadaitioti; material from "Data Science and Big-Data ttnalytics StJJdentGnide" distributed by EMC Education Services will be provided to the students. Course Delivery Structure The course will be delivered in a computer-equipped classroom. Approximately 50% of the time wi II be devoted to lecture and the other 50% devoted to the supervised working through of 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.

5 ST A Course Outline/Schedule Lecture l: 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 1 0: 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 1 7: 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 Lecture-2-7-~ Suppgrt Vector- Machines-Part 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

6 Midtenn exam: Covers lectures 1-17: 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 Graduate students will be given a special project homework that involves the analysis of a sample dataset. Grading Scheme 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 for undergraduates will be calculated from homework (50%), midtenn (20%), final exam (30%). Grades for graduate students will be calculated from homework (40%), midtenn (20%), final exam (30%), graduate project (10%). Difference between Undergraduate and Graduate Work: Graduate students are assigned additional problems, of greater depth and difficulty, 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 University of South - arolina-honor eade:arrd-shonld -expect tha.re.-'ery-ii:istaage-efasus~ieteef iiefatfaiiwill 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 to make arrangements for appropriate accommodations.

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