Curriculum | Master of Science in Marketing Analytics and Insights

Students are required to complete a total of 30 credits to complete the MS in Marketing Analytics and Insights degree. View the suggested course sequence for a 1-year full-time student.

View Suggested Sequence

Required Courses

Students must complete all of the listed required courses (21 credits).

(22:630:586) Marketing Management - 3 Credits

The purpose of the course is to offer an understanding of the nature and role of Marketing in the firm and in society. Students will gain knowledge regarding the marketing decisions of price, place, promotion, product, develop an understanding of consumer behavior, market research, social and cultural factors affecting marketing. The course will expose students to a series of marketing principles, frameworks, and analyses. These techniques will be applied to a series of case studies to reinforce the concepts. At the end of the course the students should be able to develop effective marketing plans for products and services.

In-person/Hybrid

(22:630:604) Marketing Research - 3 Credits

Pre/corequisite: Marketing Management

Provides insight into the nature and assumptions of Marketing Research conducted by corporations and commercial research companies. Provides practical experience in planning and implementing marketing research. Covers the sale of marketing research in business management; survey research and questionnaire design; scientific marketing research design and planning; data collection; basic statistical tools for analysis; and report writing and communication of research results.

In-person/Hybrid

(22:630:679) Customer Journey Analytics - 3 Credits

Pre/corequisite: Marketing Management

This course introduces the concept of Customer Journey in the Digital world which spans digital channels (web, mobile, app) and non-digital touchpoints (1:1, call center etc.). Customer Journey Analytics is the process of tracking and analyzing the way customers use combinations of channels to interact with an organization (also known as omnichannel). The focus of the course is to understand every step of the customer journey in today’s digital world using analytics in order to give that customer a much better experience of how we market to them in a channel of their choice. The course combines practical applications and analytics platforms with an end goal of developing skills that help to derive actionable insights that will impact the organization’s acquisition, experience and retention strategies. It provides a broad overview of key digital analytics strategies, concepts, issues, challenges and tools.

In-person/Hybrid

(22:630:685) Applied AI in Marketing - 3 Credits

Pre/corequisite: Marketing Management

This course will introduce you to the industry application of the most common machine learning and artificial intelligence techniques in marketing using industrialized statistical software. By the end of this course, you would learn how to optimize marketing spend, measure customer attitudes towards a product using unsupervised learning, and predict customer purchase behavior with supervised learning. You will also learn how to choose the right method for the most frequent business problems and will obtain hands-on experience in solving these problems.

In-person/Hybrid

(22:544:641) Analytics for Business Intelligence - 3 Credits

Prerequisite: Basic Statistics

This course is intended for business students of data mining techniques with these goals: 1) To provide the key methods of classification, prediction, reduction, and exploration that are at the heart of data mining; 2) To provide business decision-making context for these methods; 3) Using real business cases, to illustrate the application and interpretation of these methods.

In-person/Hybrid

(22:544:646) Data Analysis and Visualization - 3 Credits

Data analytics and visualization is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce state-of-the-art modeling, analysis, and visualization techniques. It will emphasize practical challenges involving complex real-world data and include several case studies and hands-on work with the R/Python programming language.

In-person/Hybrid

(22:630:691) MarTech Application Landscape & Ethics - 3 Credits

Prerequisite: Marketing Management

This course scans the technology landscape that supports Data Analytics. Data Storage platforms: Cloud, Data Warehouse Data Lake; Data Fabrics, including role of tools like Domino, role of NLP (Natural Language Processing) and NLG in Analytics, COTS (commercial off the shelf). Course can get into developing ‘intelligent systems’ that take data and predictive and prescriptive analytics. And get into new emerging areas like Blockchain and NFTs. Students don’t have to understand the technology but get a good understanding of the role, application and outcome   Also, associated ethical issues, including data privacy, are discussed.

In-person/Hybrid

Electives

Students are required to take 2 courses (6 credits).

(22:630:655) Customer Relationship Management - 3 Credits

Prerequisite: Marketing Research

Customer Relationship Management (CRM) offers significant opportunities for organizations to successfully implement strategies, practices, and technologies aimed at winning, managing, and retaining customers profitably and, as a result, strengthening the organization’s competitive position. This course is designed to introduce students to both CRM fundamentals and the utilization of technology in managing customers. Through lectures, scholarly articles, case readings, and class discussions, students will gain an understanding of the key factors that impact CRM success.

In-person/Hybrid

or (53:630:509) Customer Analytics - 3 Credits

Course information coming soon

Online (Camden)

(22:630:677) Advanced Marketing Analytics - 3 Credits

Prerequisite: Marketing Research

Today’s managers typically have access to large quantities of data. Careful analyses of such data lead to an improved understanding of the marketplace and, in turn, improve the quality of marketing decisions. This course will cover statistical models and techniques that can be effectively used by managers on marketing data sets. This course emphasizes data situations that students are likely to face in marketing and consulting jobs. The main topics covered in this course are customer value measurement, segmentation & targeting analysis, positioning analysis, new product design decisions, and new product forecasting models. Students will learn to use several statistics software packages such as MEXL, SPSS, and Number Analytics.

In-person/ Hybrid

(22:630:689) Marketing Mix Models - 3 Credits

Prerequisite: Marketing Research, Customer Journey Analytics

At the heart of great business organizations are great business decisions. Most modern business organizations use data to reliably make good decisions. In order to make a specific business decision, businesses typically rely on metrics generated from data. This course focuses on evaluating the performance of Marketing through attribution models. We will implement analysis and dashboards using Excel and Tableau.

In-person/ Hybrid

(22:544:603) Business Data Management - 3 Credits

The purpose of this course is to provide students with an understanding of database technology and its application in managing data resources. The conceptual, logical, and physical design of databases will be analyzed. A database management system will be used as a vehicle for illustrating some of the concepts discussed in the course.

In-person/ Hybrid

or (53:623:517) Data Management and Business Intel - 3 Credits

Course information coming soon

Online (Camden)

(22:544:635) Neural Networks and Deep Learning - 3 Credits

Prerequisite: Basic R, Python

This course introduces modern techniques of neural networks and deep learning, which have revolutionized machine learning and artificial intelligence practice to graduate students. The course heavily relies on software and libraries for deep learning, including Tensorflow, Keras, PyTorch, and similar tools, and students are required to conduct extensive projects. An end of term team project and presentation in class is also required. Students are exposed to extremely large data with a complex feature set. Pattern and image recognition, speech and sentiment analysis, and generating new images, texts, painting, and sounds from a given set of are among some application areas explored.

In-person/ Hybrid

or (53:716:545) Machine Learning Applications - 3 Credits

Course information coming soon

Online (Camden)

(22:544:660) Business Analytics Programming - 3 Credits

The goal of this course is to learn the principles of programming for business analytics using the Python and R programming languages. Programming is the fundamental background skill based on which all Information Systems are built. Even if it is not your goal to become a software developer, it is essential for an MBA graduate with concentration in Analytics and Information Management to possess a working knowledge of programming and fundamental insights into what a programmer does. This course provides you with this essential knowledge.

In-person/ Hybrid

(53:716:535) Big Data Analytics - 3 Credits

Course information coming soon

Online (Camden)

Capstone Courses

Students must complete one capstone course (3 credits).

(22:630:678) Marketing Insights - 3 Credits

Prerequisite: Marketing Research, Customer Journey Analytics

This is a Capstone course for Marketing Research which combines all aspects of the marketing research process in cased-based projects.  As future Marketing Researchers, students will be trained to integrate results from exploratory, descriptive, and causal research processes and combine both qualitative and quantitative results to make persuasive presentation of the finding.  In addition, the course will cover issues of client-vendor communication during the research process.  The course will be based on textbooks, assigned readings, case analyses, and student projects.

In-person/ Hybrid

(22:135:634) Industry Client Project/Internship/Coop - 3 Credits

Prerequisite: Marketing Research, Customer Journey Analytics

This is an experiential course to be arranged.

In-person/ Hybrid

Suggested Course Sequence (1 Year Full-Time)

Fall
Marketing Management
Marketing Research
Applied AI in Marketing
Analytics for Business Intelligence
MarTech Application Landscape & Ethics

 

Spring
Customer Journey Analytics
Data Analysis and Visualization

 

Track 1 (Insights Focused): Choose 2 Track 2 (Analytics Focused): Choose 2
Customer Relationship Management Business Data Management
Advanced Marketing Analytics Neural Networks and Deep Learning
Marketing Mix Models Business Analytics Programming
  Big Data Analytics

 

Capstone: Choose 1
Marketing Insights
Industry Client Project/Internship/Coop