The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.
1.00 - 6.00 credits | May be repeated for a total of 18 credits.
Grading Basis: Graded
Introduces many of the most exciting concepts emerging in the field of consumer oriented Internet-working, including high speed access [cable modem, satellites and digital subscriber lines (DSL)] and infrastructure developments such as gigabyte networking with asynchronous transfer mode (ATM). Evaluates the emerging directions in EC that are expected to shape both consumer and business applications in the coming decade. A "macro perspective" is used to examine the technical and managerial aspects of electronic commerce. Focus is on questions such as: What are or will be the key attributes of current and future digital products, payment systems, online retailing, and banking? How are these systems designed and implemented? What are the different mercantile processes and tradeoffs associated with these processes? What impact has global connectivity made on traditional supply-chain(s)?
Last Refreshed: 17-SEP-19 05.20.16.405317 AM
|Term||Class Number||Campus||Instruction Mode||Instructor||Section||Session||Schedule||Enrollment||Location||Grading Basis||Notes|
|1198 16668 1 M20||Fall 2019||16668||Stamford||Hybrid/Blended||Tung, Yung-Chin||M20||Reg||27/35||Graded|
|1203 14170 1 B12||Spring 2020||14170||Hartford||In Person||B12||Reg||0/65||Graded|
|1203 14509 1 B14||Spring 2020||14509||Hartford||In Person||B14||Reg||0/46||Graded||Health analytics is one of the fastest growing areas in the field of analytics. Unlike many traditional analytics positions, health analytics, especially in a clinical or medical research environment, requires the analyst to have knowledge of evidence based practice, research techniques, health data collection devices (EHR/EMR), legislation and regulation of health data, ethical use of health data, and reporting tools. This course will blend all of the aforementioned knowledge areas so that upon completion of the course, the student will be well prepared to blend these new skills with their core analytics skills to succeed in an analytics role in the clinical health or medical research discipline.|
|1203 14673 1 B15||Spring 2020||14673||Hartford||In Person||B15||Reg||0/46||Graded||Course Description The Internet has become an imperative portal for companies to reach and interact with consumers. It is important for companies to understand and leverage the data available online to devise optimal competitive strategies and achieve their business objectives. This course introduces the key concepts, techniques, and tools for analyzing web data to derive actionable customer intelligence, develop digital marketing strategies and evaluate their impacts. It covers topics including clickstream tracking, search engine analytics, digital experiments, and social analytics. It combines lecture, cases, class discussion, presentations, and hands-on computer work. Upon completion of this course, students should be able to: ? Understand the key concepts of clickstream tracking and search engine analytics ? Analyze web data to derive actionable custom intelligence ? Understand the key concepts of social analytics ? Design digital experiments to evaluate the impacts of digital marketing strategies ? Conduct social network analysis Components: Lecture PRE-REQUISITES: Three credits. Prerequisite: OPIM 5604 (Predictive Modeling). Open only to MBA and MSBAPM, others with consent.|
|1203 14681 1 B16||Spring 2020||14681||Hartford||In Person||Wanik, David||B16||Reg||Mo 6:00pm‑9:00pm
||0/46||Graded||PRE-REQ: OPIM5604 The purpose of this class is to introduce students to topics related to deep learning and will build on your previous experience in predictive analytics. Students will learn how to use neural networks for a host of data and applications ? including time series data, text data, geospatial data, and image data.|
|1203 14722 1 B17||Spring 2020||14722||Hartford||In Person||B17||Reg||0/25||Graded||OPIM 5894, Global Technology Management course will cover aspects of project management and data analytics in multi-national environments. Topics will include the challenges of project management in multi-cultural organizations, data quality in emerging markets, as well as regulations such as Right to be Forgotten and General Data Protection Regulation (GDPR) in today?s market. Up to 25 students will have the opportunity to travel and experience such topics first hand with our international corporate partners in Munich. The trip will include site visits to companies and cultural centers. Required course participation: Pre-trip required courses: Tuesdays (2/19, 3/5 and 3/12) from 6-9pm at the GBLC, Hartford, CT. Required International Trip: Friday, 3/15?Saturday, 3/23 to Munich, Germany. Post-trip required courses: Tuesday (4/2 and 4/9) from 6-9pm at the GBLC, Hartford, CT|
|1203 14968 1 M20||Spring 2020||14968||Stamford||Hybrid/Blended||M20||Reg||0/0||Graded||1/31/2019 and 3/7/2019 from 5-6:30. These classes will be held in both Hartford GBLC and Stamford. Rooms TBD|
|1203 15155 1 MS40||Spring 2020||15155||Stamford||In Person||MS40||Reg||0/44||Graded||PRE-REQ: OPIM5604 The purpose of this class is to introduce students to topics related to deep learning and will build on your previous experience in predictive analytics. Students will learn how to use neural networks for a host of data and applications ? including time series data, text data, geospatial data, and image data.|