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BUSINESS ANALYTICS CORE
Take the following courses:
This course develops an understanding of management principles in the areas of planning,
organizing, staffing and control, including but not limited to the aspects of strategy,
legal environment, operation/supply chain management. 3 CreditsS Introduces fundamental principles and assumptions of accounting as they relate to
transaction analysis and basic financial statements. 3 CreditsS The broad focus of the course is to examine how individuals come together to form
a successful organization. The course is broken into three major sections: people,
organizations, and leadership. The course emphasizes student involvement and engages
students in a variety of in-class exercises, case analysis role playing exercises,
small group exercises, and an off-campus class experience or two. One or more off-campus
experiences are required for the course. 4 CreditsCW,S,WK-SIPrerequisite: Sophomore standing. Macroeconomic conditions affect individuals and businesses in numerous ways: employment
opportunities, the purchasing power of wages and salaries, the cost of borrowing money,
sales, profits, and competitiveness against foreign businesses. This course develops
the theories relevant to understanding the business cycle, inflation, unemployment,
deflation, exchange rates and balance of payments problems. It also examines the options
and tradeoffs governments face as they seek to provide a stable macroeconomic environment
through monetary and fiscal policies. Case studies of the macroeconomic performance
and policies of diverse countries provide a comparative orientation. 3 CreditsSPrerequisites: Sophomore, Junior, or Senior standing Emphasizes accounting concepts for the internal use of management in planning and
control. Course focuses on spreadsheet applications to analyze management policies. 3 CreditsS,QM,CWPrerequisite: EB131. Analyzes consumer behavior leading to selection of product as well as pricing, promotion
and distribution strategies. Research projects help students apply concepts to the
complexities of decision making in marketing. 3 CreditsSPrerequisite: EB201. This course is a survey of the various visual, statistical, and modeling approaches
commonly used in the analysis of environmental data. The course covers: (1) visual
literacy from exploratory data inquisition to poster creation; (2) elementary group
comparison such as t-test and ANOVA and their non-parametric analogs;(3) basic systems
modeling; and (4) regression modeling techniques based on the generalized linear model
framework. 3 CreditsN, QS, CTGES, CTGISPrerequisites: Sophomore standing and permission of the instructor. An introductory study of computer science software development concepts. Python is
used to introduce a disciplined approach to problem solving methods, algorithm development,
software design, coding, debugging, testing, and documentation in the object oriented
paradigm. This is the first course in the study of computer science. 3 CreditsN,CTGES,CTGISRecommended programming experience or IT110 or IT100, IT111 or IM110 or MA103 but
not necessary. Focuses on concepts and structures necessary to design and implement a database management
system. Various modern data models, data security and integrity, and concurrency are
discussed. An SQL database system is designed and implemented as a group project. 3 CreditsN,CTGISPrerequisites: CS110. This course introduces the student to the emerging field of data science through the
presentation of basic math and statistics principles, an introduction to the computer
tools and software commonly used to perform the data analytics, and a general overview
of the machine learning techniques commonly applied to datasets for knowledge discovery.
The students will identify a dataset for a final project that will require them to
perform preparation, cleaning, simple visualization and analysis of the data with
such tools as Excel and R. Understanding the varied nature of data, their acquisition
and preliminary analysis provides the requisite skills to succeed in further study
and application of the data science field. Prerequisite: comfort with pre-calculus
topics and use of computers. 3 CreditsN This course considers the various aspects of presenting digital information for public
consumption visually. Data formats from binary, text, various file types, to relational
databases and web sites are covered to understand the framework of information retrieval
for use in visualization tools. Visualization and graphical analyses of data are considered
in the context of the human visual system for appropriate information presentation.
Various open-source and commercial digital tools are considered for development of
visualization projects. 3 CreditsN,CTDH,CTGESPrerequisite: IT 110, IT 111, IM 110, DS 110, or CS 110 or permission. EB-100 Introduction to Management
EB-131 Financial Accounting
EB-202 Behavioral Analysis of Organizations
EB-222 Principles of Macroeconomics
EB-236 Managerial Accounting
EB-351 Marketing Management
ESS-230 Environmetrics
CS-110 Computer Science I
CS-370 Database Management Systems
DS-110 Intro to Data Science
IM-242 Info Visualization
INTRODUCTORY STATISTICS
Take one of the following courses below:
EB-211 Business Statistics
This course covers basic descriptive and inferential statistics, normal curve and
z-score computations, and addresses hypothesis testing using Chi-Square, T-Test, ANOVA,
and linear regression modelling.
3 Credits
QS,S
MA-205 Elementary Statistics
Introduction to traditional statistical concepts including descriptive statistics,
binomial and normal probability models, confidence intervals, tests of hypotheses,
linear correlation and regression, two-way contingency tables, and one-way analysis
of variance.
4 CreditsN, QS, WK-SPPrerequisite: FYC-101 or EN-110 or EN-109
MA-220 Introduction to Probability & Statistics
An introduction to the basic ideas and techniques of probability theory and to selected
topics in statistics, such as sampling theory, confidence intervals, and linear regression.
4 CreditsN, QS, CTGESPrerequisite: MA130
EB-211 Business Statistics
This course covers basic descriptive and inferential statistics, normal curve and z-score computations, and addresses hypothesis testing using Chi-Square, T-Test, ANOVA, and linear regression modelling.
3 Credits QS,S
MA-205 Elementary Statistics
Introduction to traditional statistical concepts including descriptive statistics, binomial and normal probability models, confidence intervals, tests of hypotheses, linear correlation and regression, two-way contingency tables, and one-way analysis of variance.
4 CreditsN, QS, WK-SPPrerequisite: FYC-101 or EN-110 or EN-109
MA-220 Introduction to Probability & Statistics
An introduction to the basic ideas and techniques of probability theory and to selected topics in statistics, such as sampling theory, confidence intervals, and linear regression.
4 CreditsN, QS, CTGESPrerequisite: MA130
ELECTIVES
Take four of the following courses below:
CM-200 Art of Public Speaking
Seeks to develop and improve fundamental principles and methods of selecting, organizing,
developing, and communicating a line of reasoning and evidence for constructive influence
in speaking situations. Students make three formal presentations, analyze messages,
and improve their listening skills
3 CreditsCS, HPrerequisites: Sophomore, Junior, or Senior standing.
MA-116 Discrete Structures
Introduces mathematical structures and concepts such as functions, relations, logic,
induction, counting, and graph theory. Their application to Computer Science is emphasized.
4 CreditsN, QPre-requisite high school algebra.
MA-321 Multivariate Statistics
A class in multivariate statistical techniques including non-parametric methods, multiple
regression, logistic regression, multiple testing, principle analysis.
3 CreditsN, QSPrerequisites: MA-130 or MA 160; an introductory statistics course from the following
list: BI-305, EB-211, ESS-230, ESS-309, MA-205, MA-220, PY-366, or SW-215
MA-325 Statistical Consulting
The participating students will receive training during the semester in consulting
on statistical problems and to assist in collaborative efforts with faculty and/or
staff on client-partnered projects that are pre-determined. The semester-long project
provides the student with both real work experience in the field of statistics and
a project-based learning experience in partnership with the client. May be taken multiple
times for credit.
3 CreditsN, QS, CW, SW-LEPrerequisite: Take one course from this list: BI-305, EB-211, ESS-230, ESS-309, MA-205,
MA-220, PY-361, PY-366, SW-215. Also take FYC-101 or EN-110 or EN-109.
DS-210 Data Acquisition
Students will understand how to access various data types and sources, from flat file
formats to databases to big storage data architecture. Students will perform transformations,
cleaning, and merging of datasets in preparation for data mining and analysis.
3 CreditsNPRE-REQ: CS 110 and DS 110.
DS-352 Machine Learning
This course considers the use of machine learning (ML) and data mining (DM) algorithms
for the data scientist to discover information embedded in datasets from the simple
tables through complex and big data sets. Topics include ML and DM techniques such
as classification, clustering, predictive and statistical modeling using tools such
as R, Matlab, Weka and others. Simple visualization and data exploration will be covered
in support of the DM. Software techniques implemented the emerging storage and hardware
structures are introduced for handling big data.
3 CreditsNPrerequisite: CS-110, DS-110, and an approved statistics course from this list: MA-205,
MA-220, BI-305, PY-214, PY-260, PY-366, or EB- 211.
DS-375 Big Data
This course considers the management and processing of large data sets, structured,
semi-structured, and unstructured. The course focuses on modern, big data platforms
such as Hadoop and NoSQL frameworks. Students will gain experience using a variety
of programming tools and paradigms for manipulating big data sets on local servers
and cloud platforms.
3 CreditsNPrerequisites: DS 110 Intro to Data Science and CS 370 Database Management Systems
CM-200 Art of Public Speaking
Seeks to develop and improve fundamental principles and methods of selecting, organizing, developing, and communicating a line of reasoning and evidence for constructive influence in speaking situations. Students make three formal presentations, analyze messages, and improve their listening skills
3 CreditsCS, HPrerequisites: Sophomore, Junior, or Senior standing.
MA-116 Discrete Structures
Introduces mathematical structures and concepts such as functions, relations, logic, induction, counting, and graph theory. Their application to Computer Science is emphasized.
4 CreditsN, QPre-requisite high school algebra.
MA-321 Multivariate Statistics
A class in multivariate statistical techniques including non-parametric methods, multiple regression, logistic regression, multiple testing, principle analysis.
3 CreditsN, QSPrerequisites: MA-130 or MA 160; an introductory statistics course from the following list: BI-305, EB-211, ESS-230, ESS-309, MA-205, MA-220, PY-366, or SW-215
MA-325 Statistical Consulting
The participating students will receive training during the semester in consulting on statistical problems and to assist in collaborative efforts with faculty and/or staff on client-partnered projects that are pre-determined. The semester-long project provides the student with both real work experience in the field of statistics and a project-based learning experience in partnership with the client. May be taken multiple times for credit.
3 CreditsN, QS, CW, SW-LEPrerequisite: Take one course from this list: BI-305, EB-211, ESS-230, ESS-309, MA-205, MA-220, PY-361, PY-366, SW-215. Also take FYC-101 or EN-110 or EN-109.
DS-210 Data Acquisition
Students will understand how to access various data types and sources, from flat file formats to databases to big storage data architecture. Students will perform transformations, cleaning, and merging of datasets in preparation for data mining and analysis.
3 CreditsNPRE-REQ: CS 110 and DS 110.
DS-352 Machine Learning
This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in datasets from the simple tables through complex and big data sets. Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Matlab, Weka and others. Simple visualization and data exploration will be covered in support of the DM. Software techniques implemented the emerging storage and hardware structures are introduced for handling big data.
3 CreditsNPrerequisite: CS-110, DS-110, and an approved statistics course from this list: MA-205, MA-220, BI-305, PY-214, PY-260, PY-366, or EB- 211.
DS-375 Big Data
This course considers the management and processing of large data sets, structured, semi-structured, and unstructured. The course focuses on modern, big data platforms such as Hadoop and NoSQL frameworks. Students will gain experience using a variety of programming tools and paradigms for manipulating big data sets on local servers and cloud platforms.
3 CreditsNPrerequisites: DS 110 Intro to Data Science and CS 370 Database Management Systems
UPPER-LEVEL CORE
Take an additional course from the EB Department at the 300/400 level.
CAPSTONE
Take the following course:
EB-480 Senior Seminar
A capstone course for POE in Business. Through the use of readings, case studies and simulations, students in the course will formulate corporate strategy and implement it in a competitive environment. How firms may gain and sustain competitive advantage with the formulated strategy will be examined. In addition, students will also be trained to craft business reports on corporate strategies. The evaluation of performance will mainly depend on the content and the quality of the business reports.
3 CreditsS
POE Credit Total = 56-58
Students must complete at least 18 credits at the 300/400-level. Any course exception must be approved by the advisor and/or department chair.