Program Structure & Dates

During your Professional Master’s program, you will be required to attend one 3-day weekend on-campus session every trimester of your program. In between all on-campus weekends, your coursework will be completed online. Summer term is optional for continuing students. Only new students beginning in the Summer Trimester must attend the in-person 3-day weekend.

3-Day Weekend Schedule

Summer Trimester (5/20/24-8/8/24)
Saturday, July 13 9:00 am – 5:00 pm
Sunday, July 14 9:00 am – 5:00 pm
Monday, July 15 9:00 am – 2:00 pm

Fall Trimester 2024 (9/3/24-11/18/24)
Friday, November 1st 1:00 pm – 7:00 pm
Saturday, November 2nd 9:00 am – 6:00 pm
Sunday, November 3rd 9:00 am – 3:00 pm

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M.S. Business Analytics Professional Program Courses

All students are required to register for at least 6 credits per trimester. In order to remain in compliance with the program requirements, students must register for one PHYW (online) course and one PHYF (3-day weekend) course.

Section	Trimester	Type	Course Name	Credits
DS-510	Trimester 1	PHYW	Introduction to Data Science	3
DS-520		PHYF	Data Analysis and Decision Modeling	3
DS-650	Trimester 2	PHYW	Data Law, Ethics and Privacy	3
DS-542		PHYF	Python in Data Science	3
GB-622	Trimester 3	PHYW	Managerial Economics	3
DS-660		PHYF	Business Analytics	3
GB-530	Trimester 4	PHYW	Managerial Finance and Decision Making	3
DS-621		PHYF	Data Visualization w/ Power BI	3
DS-680	Trimester 5	PHYW	Marketing Analytics and Operations Research	3
DS-671		PHYF	Capstone: Business Analytics	3

Curricular Practical Training (CPT) Courses

All students need to take either DS-598 if they plan to use CPT or DS-599 if they do not plan to use CPT every trimester during the length of their program.

Section	Trimester	Type	Course Name	Credits
DS-598	Every Term	DLSO	Applied Industry Experience	0
DS-599	Every Term	DLSO	Research Practicum	0

* Course schedule and sequencing subject to change.

Course Descriptions

DS-510. Introduction to Data Science. 3 Credits.
Data Science is a set of fundamental principles that guide the extraction of valuable information and knowledge from data. This course provides an overview and develops student’s understanding of the data science and analytics landscape in the context of business examples and other emerging fields. It also provides students with an understanding of the most common methods used in data science. Topics covered include introduction to predictive modeling, data visualization, probability distributions, Bayes’ theorem, statistical inference, clustering analysis, decision analytic thinking, data and business strategy, cloud storage, and big data analytics.

DS-520. Data Analysis and Decision Modeling. 3 Credits.
This course will provide students with an understanding of common statistical techniques and methods used to analyze data in business. Topics covered include probability, sampling, estimation, hypothesis testing, linear regression, multivariate regression, logistic regression, analysis of variance, categorical data analysis, Bootstrap, permutation tests and nonparametric statistics. Students will learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines.

DS-650. Data Law, Ethics and Business Intelligence. 3 Credits.
The increasing use of big data in our society raises legal and ethical questions. Business intelligence is the process of collecting and transforming raw data into meaningful and useful information for business purposes. This course explores the issues of privacy, data protection, non-discrimination, equality of opportunities and due process in the context of data-rich environments. It analyzes ethical and intellectual property issues related to data analytics and the use of business intelligence. Students will also learn the legal obligations in collecting, sharing and using data, as well as the impact of algorithmic profiling, industrial personalization and government. This course also provides an understanding of the important capabilities of business intelligence, the technologies that enable them and the management of business intelligence.

DS-542. Python in Data Science. 3 Credits.
The course gives an introduction to Python programming for statistical analyses and managing, analyzing and visualizing data. Topics include numeric and non-numeric values, arithmetic and assignment operations, arrays and data frames, special values, classes and coercion. Students will learn to write functions, read/write files, use exceptions, measure execution times, perform sampling and confidence analyses, plot a linear regression. Students will explore tools for statistical simulation, large data analysis and data visualization, including interactive 3D plots.

DS-598. Applied Industry Experience. 0 Credits.
The Applied Work Experience/Curricular Practical Training course is an academic component that accompanies students’ industry work experience and Curricular Practical Training. Students whose current work role has been approved by the Program Director as directly related to their program of study should register for this non-credit course which they are eligible for after their fourth trimester.

DS-599. Research Practicum. 3 Credits.
The Research Practicum is a learning experience that gives the students the opportunity to conduct real-world consulting projects with businesses that build upon the science, research and application of data and analysis, extending to strategic planning and identifying relevant tactics to carry out strategies. For Professional Hybrid programs.

DS-660. Business Analytics. 3 Credits.
Business analytics is the process of generating and delivering the information acquired that enables and supports an improved and timely decision process. The aim of this course is to provide the student with an understanding of a broad range of decision analysis techniques and tools and facilitate the application of these methodologies to analyze real-world business problems and arrive at a rational solution. Topics covered include foundations of business analytics, descriptive analytics, predictive analytics, prescriptive analytics, and the use of computer software for statistical applications. The course work will provide case studies in Business Analytics and present real applications of business analytics. Students will work in groups to develop analytic solutions to these problems.

DS-621. Data Visualization w/Power BI.
Data Visualization with Power BI is a comprehensive course designed to equip participants with the knowledge and skills required to create compelling visualizations and interactive dashboards using Microsoft Power BI. This course will delve into the key principles of data visualization and advanced analytics and provide hands-on training in utilizing Power BI’s robust features and functionalities. students will gain a solid foundation in data visualization best practices and the ability to effectively communicate insights through captivating visuals.

GB-622 Managerial Economics
This course examines the foundation concepts for how organizations allocate resources for the production, distribution, and consumption of goods and services. Economic decisions are linked to the organization, management, and strategy involved with the conduct of operations. This course focuses on how managers can improve their understanding of the economic environment and its impact on the business firm.

GB-530 Managerial Finance and Decision Making
A study of the problems associated with the financial management of business organizations. Topics include the analysis of types of firms and markets, review of accounting, time value of money, valuation, and short-term financing.

DS-680. Marketing Analytics and Operations Research. 3 Credits.
Organizations need to interpret data about consumer choices, their browsing and buying patterns and to match supply with demand in various business settings. This course examines the best practices for using data to prescribe more effective business strategies. Topics covered include marketing resource allocation, metrics for measuring brand assets, customer lifetime value, and using data analytics to evaluate and optimize marketing campaigns. Students learn how data is used to describe, explain, and predict customer behavior, and meet customer needs. Students also learn to model future demand uncertainties, predict the outcomes of competing policy choices and take optimal operation decisions in high and low risk scenarios.

DS-671. Capstone: Business Analytics. 3 Credits.
This course is structured as a capstone research practicum where students have an opportunity to apply the knowledge acquired in business analytics to interdisciplinary problems from a variety of industry sectors. Students work in teams to define and carry out an analytics project from data collection, processing and visualization to designing the best method for solving the problem. The problems and datasets used in this practicum will be selected from real world industry or government settings. At the end of the class students will write a report that presents their project, the approach and techniques used to design a solution, followed by results and conclusion. Students are encouraged to present their capstone research at conferences.