MS in Business Analytics Curriculum
The program balances courses in business processes with courses in exploratory and predictive analytics and covers everything a business decision-maker needs to know – from using R/Python, JSON/ XML and SQL, to examining business processes through data models, to extracting meaning from big, unstructured data.
| Course Code | Course Title | Credits | Offered | Delivery Model |
| BSAN 7001 | Intro to Data Analytics and Business Intelligence | 3 | Fall | In-person and Asynch. online |
| BSAN 7031 | Databases and SQL | 3 | Fall | In-person and Asynch. online |
| BSAN 7041 | Business Processes and Data Models | 3 | Spring | Hybrid |
| BSAN 7011 | Exploratory Data Analysis and Data Visualization | 3 | Spring | In-person and Asynch. online |
| BSAN 7021 | Predictive Analytics | 3 | Fall | Online |
| BSAN 7051 | Big Data Analytics | 3 | Spring | Online |
| BSAN 9000 | Capstone Project | 3 | Fall, Spring | Online |
| Course Code | Course Title | Credits | Offered | Delivery Model |
| BSAN 7045 | Project Management | 3 | Spring | Online |
| BSAN 7046 | Deep Learning | 3 | Periodically | Asynch. online |
| DASC 7111 | Text Mining | 3 | Periodically | Asynch. online |
| BQUA 7813 | Management Science | 3 | Summer | Asynch. online |
| BQUA 7825 | Introduction to Supply Chain Management | 3 | Fall | Hybrid |
| DASC 8211 | Machine Learning | 3 | Periodically | Online |
| PSYC7214 | Cognition for Visualization | 3 | Summer | Online |
Total Credits: 30
Course Descriptions
BSAN 7001 Introduction to Data Analytics and Business Intelligence
Business decision-making should, when possible, rely on data and the conclusions that
can be drawn from that data. This course is an introduction to business data analytics;
it covers descriptive statistics, data visualization, probability basics, and relationships
between two or more variables. One focus is on learning and contrasting traditional
statistical approaches (inference) and "big data" approaches. Much of the course will
entail the use of Excel, as spreadsheet software is arguably the most commonly available
and most frequently used tool for analyzing business data. We will equally be using
R – a popular, open-source, statistical package. 3 credits
BSAN 7031 Databases and SQL
Relational database technology revealed the power of a simple data model coupled with
the nonprocedural Structured Query Language (SQL) that enabled data independence and
unleashed the power of computing applications. Despite the growing importance of other
data models, like schema-free and distributed-data models, the relational data model
still reigns supreme in many application domains. The overwhelming majority of business
data is still stored in relational databases, and any business analyst needs to understand
how to extract data from them. This course provides thorough coverage of SQL. The
course also covers data warehousing concepts, as analysts will need to design data
warehouses for end users to perform their data analysis. Another important topic in
the course is database design. While business analysts might not design databases
for mission critical processes, they might be called upon to design departmental databases.
This will require an understanding of database design diagrams. With this in mind,
the course also covers the use of Entity Relationship Models for database design.
3 credits
BSAN 7041 Business Processes and Data Models
In order to support business effectively, analysts need to understand business processes.
This course will cover business processes by examining their corresponding data models,
as analysts will mostly be looking at business from a data-oriented lens. The course
covers data models for products, orders, shipments, work effort, invoicing, accounting,
budgeting and human resources management. The course will also look at several star-schema
for different functional areas. 3 credits
BSAN 7011 Exploratory Data Analysis and Data Visualization
In the initial stages of a data analysis project, analysts must often deal with large
and unfamiliar data sets. By asking good questions and finding answers in the data,
they arrive at useful insights – and this captures the core of exploratory data analysis
(EDA). EDA often serves as a precursor to the process of building predictive models.
Equally often, EDA yields significant insights that prove to be very useful in themselves.
This course covers the art and science of EDA. Through numerous examples, the course
will develop participants' ability to formulate interesting and important questions.
Answering these questions generally involves significant slicing, dicing, aggregating
and reshaping of the data; this course will equip participants with the requisite
skills. EDA relies heavily on data visualization and the course will equip participants
with the skills to generate, and effectively present, evocative graphs that tell stories.
The course will equip participants with a framework to enable them to ask the right
questions and with the skills to explore and find answers. Prerequisite: BSAN 7001. 3 credits
BSAN 7021 Predictive Analytics
In most business situations, being able to determine, with reasonable accuracy, the
value of some unknown can be beneficial. For example, it would be useful for a company
to know if a prospective customer would default on payments (classification), or to
know the number of units of a product that it might be able to sell during the next
quarter at a given store (regression). Quite often, even seemingly inaccurate estimates
of such unknowns can lead to large monetary gains for a company if the new knowledge
can lead to a discernable difference in performance. This is the domain of predictive
modeling – using historical data to determine the value of an unknown. The course
covers both classification and regression techniques. The course will equip participants
with the ability to identify situations that could benefit from predictive models,
to identify the data requirements and work with others to obtain the data, to manipulate
the data into a form usable for predictive analysis, and to build, evaluate, present
and deploy the models. Prerequisite: BSAN 7001. 3 credits
BSAN 7051 Big Data Analytics
Unstructured data abounds in today's environment. These arise in the form of tweets,
Facebook and Instagram posts, photographs, videos, speech recordings and the like.
IT tools have made impressive progress in being able to analyze and extract meaning
from this sort of unstructured data. This course will help students understand the
key technologies used in storing, processing and analyzing these forms of unstructured,
big data. Students will use R, Python, Cassandra, Spark and Tensor Flow. Students
will learn how to conceive of big data applications and design scalable systems. Prerequisite:
BSAN 7001, BSAN 7011, BSAN 7021. 3 credits
BSAN 9000 Capstone Project
All students will complete a rigorous, semester-long Capstone project that will rely
on and integrate the knowledge gained over the duration of the program. The Capstone
experience will give students an immersive experience where they will take charge
of a project and see it through from start to end. During the individual courses,
students learn everything in a carefully scaffolded and structured environment. In
contrast, in this course, students will face a more unstructured, realistic scenario,
similar to those that data analysts encounter in their work. This unstructured environment
will help students integrate their structured learnings and mature in the process.
Their learning is also enhanced because of the sense of ownership that they feel in
doing projects of their choice. Students will be expected to come up with their own
projects (with some guidance) and then to execute the entire process. Project teams
will identify a problem of significance and use the techniques learned in the MS in
Business Analytics to suggest solutions. Students will go through several iterations
of work within each phase, based on discussions with the instructors. Prerequisite:
BSAN 7001, BSAN 7011, BSAN 7021, BSAN 7031, BSAN 7041, BSAN 7051. 3 credits