Faster computing powers have made it possible for organizations to transform their
massive amounts of data into information and apply advanced analytics methods to problems
that were impossible to solve even ten years ago. Business analytics uses data, statistical
and quantitative analysis, programming, predictive modeling, and optimization to make
businesses work better and provide them with competitive advantage.
A concentration in Business Analytics will prepare you with the skills to use data
and models that organizations are looking for in order to help them make better decisions
in diverse areas like marketing, supply chain, operations and healthcare.
Coursework
The business analytics concentration is fulfilled by three required course and one
elective course. The current schedule for these courses is available in the Schedule
of Classes on the BU Brain. Descriptions of courses are listed in the Binghamton University Bulletin and course descriptions for the upcoming semester are available on the BU Brain.
Contact Academic Advising for the most updated list of core courses and electives.
Required coursework
A GPA of 3.5 and a grade of A- in CQS 311 is required to declare this concentration.
(Completion of S-Core coursework required for all courses, MIS 325 can replaced with
an elective below if approved)
-
SCM 360 - SpreadsheetModeling&Dec Making
Supplements other courses in the business curriculum by discussing decision making
and risk management using spreadsheet analysis. Improves students' decision-making
ability in an uncertain and complex environment. Teaches techniques widely used to
assess and manage risk, structure problems, determine the optimal decision and estimate
the impact of a decision on performance measures of interest. Through cases, lectures
and exercises, sharpens students' problem-solving skills and analytical and logical
thinking ability. Greatly enhances students' proficiency in spreadsheet analysis
and modeling, an invaluable skill in today's business environment in which spreadsheets,
with 40 million users, have become the primary platform for business analysis. For
students interested in a career in general consulting or as analysts in the areas
of finance, marketing or MIS. Pre-Requisites: CQS, OPM and MIS 311. Usually offered
in Fall only. Credits: 4
Levels: Undergraduate
-
MIS 325 - Essentials of Programming
Given the highly digitized environment we live in, organizations are increasingly
recognizing the need for programming literacy to leverage computing devices and data-processing
applications to improve their performance. Therefore, programming and data analysis are becoming the necessities of today's
highly competitive business environment. This course focuses on the essentials of
programming in Python, a powerful and general-purpose programming language that many
businesses expect their recruits to be competent in. The course will cover a variety
of general programming topics such as data structures, decision controls, loops, and
functions. It will also cover popular Python libraries such as numpy, pandas, and
matplotlib. The course will prepare students to develop programs that solve real-world
problems. Students registering for this course should be prepared for an intense
but manageable workload.
Levels: Undergraduate
-
SCM 460 - Bus Intelligence & Analytics
This course covers fundamental concepts and techniques in data mining with a focus
on business application. The goal is to understand how to build and use appropriate
data mining models to analyze business-related data and obtain useful information
to advance the business and make managerial decisions. The course covers various predictive
regression and classification models, such as Multiple Linear Regression, Regularization
methods, K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Classification
and Regression Trees, Neural Networks, clustering methods, and dimension reduction
techniques. Python will be used to implement such models. Hence, proficiency in programming
with Python is critical in succeeding in this course. Prerequisites are Python programming,
statistics and OPM 311 Credits: 4
Levels: Undergraduate
Electives
Select one or more of the following: (students also have the option to select MIS 480J: Introduction to Machine Learning and MKTG 480R: Data Mining Techniques in Marketing)
-
MKTG 441 - Customer Analytics
Provides hands-on skills in using advanced computer-based tools that help in marketing
decisions. Topics include sales-call planning, segmentation using cluster analysis,
positioning using MDS, new-product design using conjoint analysis, pricing using yield
management, etc. Emphasis is placed on conceptualizing the problems as well as their
practical solutions. Particularly relevant for the data-rich e-commerce environment
(e.g., data mining techniques for marketing decisions). Students gain valuable spreadsheet
skills and learn to integrate analysis with marketing intuition. Formerly called ADVANCED
TOOLS FOR MARKETING DECISIONS. 4 credits. Pre-requisite of MKTG 311. Offered occasionally.
Levels: Undergraduate
-
MKTG 475 - Data Driven Marketing
Deals with the use of data to make marketing decisions. Students are exposed to
key concepts and methods of quantitative modeling, analysis, and interpretation. Topics
include marketing mix models, choice models, Internet marketing metrics, etc. The
course provides hands-on exercises and applications using software tools. Prerequisites:
Introduction to Marketing Credits: 4
Levels: Undergraduate
-
MKTG 480F - Special Topics
Particular topics within broad area of marketing topics determined in advance.
May be repeated for credit. Prerequisite: consent of instructor.
Credits: 4
Levels: Undergraduate
-
MIS 480F - IT for Business Analytics
The class focuses on the technology component of business analytics; more specifically
storing, processing, and accessing data for the purpose of business analytics. The
majority of the class explores variety of technology solutions for big data on cloud
infrastructure. The first part of the class focuses on comparing/contrasting relational
databases, in-memory databases, and NoSQL databases, virtualization, and basics of
parallel computing and cluster computing. The second part of the class covers alternative
database models including column, key-value, graph, and document. The last part of
the class focuses on data processing languages and distributed machine learning technologies.
At the end of the class, students will have hands-on experience with distributed file
systems, various NoSQL databases, and virtualization solutions for the purpose of
preparing data for business analytics. Pre-reqs: MIS 311 Credits: 4
Levels: Undergraduate
-
MIS 480H - Web Mining/Soc Netwk Analysis
This course provides an overview of measures, models, and methods of analysis that
can be used to study social networks to further business interests within organizations
using data from internal and external social media data sources. The focus of the
course will be on modeling methods and IT tools to collect and analyze large volumes
of data for predictive and descriptive analysis. Students will also learn the use
of standard statistical software packages and special network analysis software.
Traditionally offered in spring
Prerequisite of MIS 311 and CQS 311
credits 4
Levels: Undergraduate
-
MIS 480J - Intro to Machine Learning
Machine learning is the science of allowing computers to learn from data and perform
tasks without being explicitly programmed. It becomes so pervasive today that we are
using it in many aspects of our lives without even knowing it. Some examples include
speech recognition, image recognition, web search, personalized recommendation, and
self-driving cars. This course provides an overview of machine learning techniques
(e.g., supervised vs. unsupervised methods) to explore, analyze, and exploit data
with a focus on designing machine learning workflow and algorithms.
Whereas no prior knowledge of machine learning is needed, students are assumed to
have a basic understanding of calculus, probability theory, and linear algebra. No background knowledge of programming
is required.
Prerequisites MIS 311, CQS 311
Credits: 4
Levels: Undergraduate
-
MIS 480L - Data Science Project
Data Science is a growing field that combines scientific techniques, processes,
algorithms and technology to extract knowledge and help drive decisions. This course
provides an understanding of how Data Science supports business objectives and decision
makers. The primary focus is to understand the Data Science Process and its principles,
understand how to convey information and work with technologies to help provide your
audience with an understanding of your results. This course will cover what it means
to be a Data Scientist. We will start with an overview of Data Science. Next, we will
talk about designing and understanding experiments in data. We will delve into the
art of Data Science with such concepts as data collection, handling, analyzing, visualizing,
interpreting results and making decisions. Finally, the class will work on a semester-long
team based project based on data science. This class will focus on how to work with
data. It is backed by technology and there is an expectation to work with technology,
but the primary focus is on organizing data projects, defining objectives and conveying
results including factors such as bias, privacy and ethics. Prereq CQS 311
Levels: Graduate, Undergraduate
-
MKTG 480N - Digital Analytics
Digital analytics course is designed for students who have a general interest in
collecting, analyzing, visualizing, and lastly, but most importantly, gaining insights
about digital marketing data. With a focus on hands-on skills, we are going to cover
several analytical tools in class such as Excel, R, Google Analytics, and Tableau.
MUST BRING LAP TOP TO CLASS
Prerequisites: MKTG 311 and Junior Standing Credits: 4
Levels: Undergraduate
-
MKTG 480R - Data Mining Techniques in MKTG
This class covers a variety of analytical tools used in Data Mining, such as visualization,
prediction, clustering, and text mining. Students will learn to apply these tools
to solve marketing problems in a variety of contexts. Prerequisite MKTG 311. Credits
4
Levels: Undergraduate
-
SCM 365 - Supply Chain Management
Supply Chain Management Students will develop a strong understanding of managerial
considerations and technologies in supply chain management. The course will cover
supply chain strategy, demand planning, inventory management, transportation, distribution, warehousing, and supply chain
coordination. It will use lectures, readings, cases, and online simulations. Pre-Requisites:
CQS, OPM and MIS 311. Traditionally offered in the Fall Semester. Credits: 4
Levels: Undergraduate
-
SCM 465 - Managing Healthcare Operations
In this course we will explore the challenges faced when delivering quality health
care, take a principles-driven approach to study health care management and improve
the health care value chain. We will learn how to evaluate the performance of operating
units, understand why they perform as they do, design new or improved operating procedures
and systems for competitive advantage, make short and long run decisions that affect
operations, and manage the workforce. Prereq OPM 311
Levels: Undergraduate