CSCE 587 Home Page Syllabus Homework/Projects

CSCE 587 Big Data Analytics

HW2 has been assigned: see Homework/Projects page.

Lecture Materials

Aug. 24 Introduction and Introduction to R

Aug. 29 VM Terminal Sessions (or how to change your password on the virtual machine)
Introduction to R and some wave data. Instructions on how to transfer data sets to the virtual machine.

Aug. 31 Continuation of the introduction to R and the wave data we expect to use. We will continue with Intermediate R

Sept. 5 Intermediate R and the CSV data that we expect to use starting from slide 34. Note: this csv file has contents that are different from the example in the slides.

Sept. 7 Conclusion of Intermediate R
Note: The first homework has been assigned. See the Homework/Projects page. If time allows, we will start a discussion on K-means clustering along with ome illustrative Data and a second data set: the iris data set.

Sept. 12 K-means clustering. The VMs were brought down in preparation for the storm and have not been brought back up, so we will postpone the lab until Thursday. Instead we will gain a better understanding of cluster by looking at the first 20 slides of this generic presentation on clustering.

Sept. 14 CSE/STAT Section 002: Introduction to clustering. Followed by K-means clustering.

Sept. 14 CSE/STAT Section 001: K-means lab along with some illustrative Data. Here is the iris data set.
Followed by lecture on association analysis.

Sept. 19 CSE/STAT Section 002: K-means lab along with some illustrative Data. Here is the iris data set.
Followed by lecture on association analysis.

Sept. 19 CSE/STAT Section 001: Continuation of association analysis, followed by Speeding up association rule discovery

Sept. 21 CSE/STAT Section 002: Continuation of association analysis, followed by Speeding up association rule discovery

Sept. 21 CSE/STAT Section 001: We will start with a lab on association rules. (If you have problems with dependencies, here are Alistair's hints for installing arules and arulesviz).
Time permitting, we will continue with Simple linear regression

Sept. 26 CSE/STAT Section 002: We will start with a lab on association rules. (If you have problems with dependencies, here are Alistair's hints for installing arules and arulesviz).
Time permitting, we will continue with Simple linear regression

Sept. 26 CSE/STAT Section 001: Continuation of Simple linear regression. This will be followed by the linear regression lab and some real estate data, time permitting.

Class Meeting Times

Section Days Time Room
Lecture/Lab 002 T, Th 8:30am - 9:45am 1D29
Lecture/Lab 001 T, Th 10:05am - 11:20am 1D29

Instructor

Prof. John Rose
Office:Swearingen 3A67
E-mail:rose@cse.sc.edu
Office Phone:777-2405
Office Hours:TW 3:30pm-5pm and by appointment

Teaching Assistant

Ms. Xianshan Qu
Office:Swearingen 3D47
E-mail:xqu@email.sc.edu
Office Phone:777-XXXX
Office Hours:MW 2:00pm-3:30pm

Text

No textbook will be required. Required readings will come from the big data/data mining/data analytics literature.

Prerequisites

Ability to think

Grade breakdown

Homework & Projects30%
In-Class Labs20%
Midterm20%
Final30%

Grade ranges

A90 - 100B+86 - 89B80 - 85
C+76 - 79C70 - 75D+66 - 69
D60-65Fbelow 60
Grades will not be curved. You will receive the grade that you have earned. N.B. If you want to receive a passing grade, then you must earn it during the semester.

Resources you may find useful

R download for Linux, Mac OS, and Windows
R Studio download for linux, Mac OS, and Windows

Computer Science and Engineering University of South Carolina

If you have any questions or comments, please send me e-mail at: rose@cse.sc.edu

CSCE 587 Home Page Syllabus Homework/Projects