Analytics

Click on any course title below to read its description. Courses offered in the upcoming semester include a schedule, and are indicated by a label to the right of the title.

Business Analytics

Prereq: AD100 Pre-Analytics Laboratory
This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidation, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). Students will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data-inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise.   [ 4 cr. ]

Section Type Instructor Location Days Times
E1 IND Ritt FLR 266 M 6:00 pm – 8:45 pm
O1 IND Rabinovich ARR

Prereq: METAD571
The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value. The major risk categories of the enterprise risk management such as financial risk, strategic risk and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked data-inputs, analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise.  [ 4 cr. ]

Section Type Instructor Location Days Times
O1 IND Arslan ARR

Prereq: METAD571
Become familiar with the foundations of modern marketing analytics and develop your ability to select, apply, and interpret readily available data on customer purchase behavior, new customer acquisition, current customer retention, and marketing mix optimization. This course explores approaches and techniques to support the managerial decision-making process and skills in using state-of-the-art statistical and analytics tools. Students will have an opportunity to gain basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive models, and quantify the incremental impact of specific marketing actions.   [ 4 cr. ]

Section Type Instructor Location Days Times
C1 IND Lee CAS 229 W 6:00 pm – 8:45 pm

Prereq: METAD571
Explore web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of websites, their content, user behavior, and reporting. The Google analytics tool is used for collection of website data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module presents how web crawlers process and index the content of web sites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated.   [ 4 cr. ]

Section Type Instructor Location Days Times
D1 IND Kanabar CAS 426 R 6:00 pm – 8:45 pm
O1 IND Phillips ARR
O1 Phillips ARR

Enterprises, organizations and individuals are creating, collecting, and using massive amount of structured and unstructured data with the goal to convert the information into knowledge, to improve the quality and the efficiency of their decision-making process, and to better position themselves to the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of the business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal to understand (i) the business context where hidden internal and external value is to be identified and captured, and (ii) of exactly what the selected data mining method do.   [ 4 cr. ]

Data Analytics

The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Starting with an introduction to probability and statistics, the R tool is introduced for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: MET CS 546 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Section Type Instructor Location Days Times
A1 IND Kalathur COM 217 M 6:00 pm – 8:45 pm
O1 IND Kalathur ARR

This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Section Type Instructor Location Days Times
B1 IND Teymourian CAS 323A T 6:00 pm – 8:45 pm
D1 IND Zhang CAS 324 R 6:00 pm – 8:45 pm

The Web Analytics and Mining course covers the areas of web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of web sites, their content, user behavior, and reporting. Google analytics tool is used for collection of web site data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module studies how web crawlers process and index the content of web sites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Section Type Instructor Location Days Times
C1 IND Vasilkoski SHA 202 W 6:00 pm – 8:45 pm
O1 IND Vasilkoski ARR

The goal of this course is to study basic concepts and techniques of data mining. The topics include data preparation, classification, performance evaluation, association rule mining, and clustering. We will discuss basic data mining algorithms in the class and students will practice data mining techniques using data mining software. Students will use Weka and SQL Server or Oracle. Prereq: MS CS Prerequisites: MET CS 579; or instructor's consent. MS CIS Prerequisites: MET CS 669 and MET CS 546; or instructor's consent.  [ 4 cr. ]

Section Type Instructor Location Days Times
D1 IND Lee FLR 267 R 6:00 pm – 8:45 pm
E1 IND Lee FLR 267 R 6:00 pm – 8:45 pm
O1 IND Lee ARR