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. ]

Sum1 2017
Section Type Instructor Location Days Times
SA1 IND Ritt FLR 266 MW 6:00 pm – 9:30 pm
SO1 IND Rabinovich ARR
Fall 2017
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. ]

Sum1 2017
Section Type Instructor Location Days Times
SO1 IND Maleyeff ARR
Fall 2017
Section Type Instructor Location Days Times
O1 IND Maleyeff 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. ]

Sum1 2017
Section Type Instructor Location Days Times
SA1 IND Grosz COM 213 MW 6:00 pm – 9:30 pm
Fall 2017
Section Type Instructor Location Days Times
C1 IND Ellis 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

Data Analytics

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