Bachelor of Science in Analytics Program (ANLY)

The Analytics program explores the knowledge and skills that are essential to collect, analyze, interpret and present information obtained from data drawn from multiple, often disparate sources of organizational data.  In today’s workplace, analytics is essential for keeping an organization running smoothly.  Many analytics professionals plan and coordinate all technology-related activity for a business and work beside managers of the company to meet the technological needs of the organization.   This degree program of study is designed for the student seeking a greater emphasis on studying informatics and business intelligence.  Professionals in analytics use advanced computation and mathematical techniques to solve critical business problems.  Computer technology is used to develop quantitative models and create database systems that the student applies to management decision-making.

Program Goals

Graduates of the Bachelor of Science in Analytics program are able to:

  • Identify and assess the opportunities, needs and constraints for data usage within an organizational context;
  • Integrate information technology and data science to maximize the value of data;
  • Design innovative and creative data analytics solutions;
  • Communicate clearly and persuasively to a variety of audiences;
  • Strengthen state and local economies by meeting the demand for well-educated and skilled data analysis; and,
  • Lead analytics teams and projects.

As part of the Analytics Program, the student completes a professional portfolio as a means for assessing learning outcomes and enhancing personal and professional development.

Analytics Requirements This program requires a total of 50 semester hours.  The semester hour value of each course appears in parentheses ( ).


Complete all of the following courses – 50 semester hours:

ANLY 400
Analytics Tools and Techniques
(4 credits)
ANLY 405
Predictive Modeling
(3 credits)
ANLY 410
Data Warehousing and Mining
(3 credits)
ANLY 415
Advanced Analytics and Reporting
(3 credits)
CISC 120
Fundamentals of Computing
(4 credits)
CISC 160
Data Structures
(4 credits)
CISC 233
Essential Algorithms
(4 credits)
CISC 340
Introduction to Artificial Intelligence
(4 credits)
CISC 491
SW Development Processes & Quality
(4 credits)
CISC 460
SQL Database Design & Implementation
(4 credits)
MATH 310
Discrete Mathematics II
(3 credits)
MATH 380
Mathematical Modeling
(4 credits)
MEBA 110
Introduction to eBusiness Management
(3 credits)
GEND 400
The Entrepreneurial Mind
(3 credits)

Course Descriptions Undergraduate

ANALYTICS (ANLY)

 

ANLY 298 Project I (3 semester hours)

Prerequisites:  SEMR 200, an approved learning contract, permission of the Office of Experiential Programs, designation of an appropriate academic advisor, and a minimum of 40 earned semester hours

Description:  This first project in the student’s experiential program challenges the student to identify, investigate and analyze a particular topic in the program of study or a concentration.  A key objective is to apply skills, methods, and knowledge obtained in prior courses with independent thinking and research; the final product represents the successful and purposeful application of knowledge.  The project is undertaken with the close mentorship of a faculty member, and may involve a community partner.  Projects can involve scientific-based research or laboratory experiences, needs analysis or development plans for external organizations, or market studies and business plan proposals.  Offered as needed.


ANLY 365 Internship (3 semester hours)

Prerequisites:  SEMR 300 or permission, an approved learning contract, permission of Office of Experiential Programs, designation of an appropriate academic advisor, and a site supervisor

Description:  An internship allows the student to put theory into practice.  The student applies classroom experiences to the workplace at an off-site placement, where ideas are tested and competencies and skills are developed.  Throughout the internship, the student works regularly with a faculty supervisor, the Office of Experiential Programs, and a site supervisor who guides the learning process.  The student integrates the collective observations, analyses, and reflections of the experiential team into an internship portfolio that showcases the accomplishments of the experience.  The unique portfolio is constructed throughout the internship, and represents the evolutionary and dynamic nature of the learning process.  Offered as needed.


ANLY 400 Analytics Tools and Techniques (4 semester hours)

Prerequisites:  MATH 280

Description:  The use of analytics is a common practice in modern business settings.  This course introduces the basic concept and practice of analytics and its role in business.  The emphasis is on the tools and techniques of analytics with case studies and examples.  Topics include:  data querying and reporting; data access and management; data cleansing; statistical programming; data mining introduction; relational databases; and, statistical analysis of databases.  The student is also introduced to Business Intelligence (BI) and statistical methodology (i.e. clustering, decision tree, etc.) along with using popular analytics packages such as SAS, Google Analytics, Business Objects, Aginity, and others.   Offered Fall Semester, annually.


ANLY 405 Predictive Modeling (3 semester hours)

Prerequisites:  ANLY 400 and MATH 380

Description:  The development and implementation of models to predict outcomes based on input data is becoming an essential skill in modern enterprises.  The objective of this course is to teach this skill.  The course covers the principles of qualitative as well as quantitative models that can be used for predicting outcome based on input data.  The predictions may be definitive, based on the assumptions or estimates based on probabilities.  The student explores how to prepare input data, build predictive models, and assess the models by examining the output produced.  Topics include:  exploratory data analysis, linear regression, multiple linear regression, regression diagnostics, logistics regression, analysis of variance (ANOVA), time series and forecasting, statistical methods for process improvement, classifiers, and non-linear models.  General concepts behind how software packages roll up and how they screen data and produce risk scores on topics such as in-patient probability of readmissions.  Offered Spring Semester, annually.


ANLY 410 Data Warehousing and Mining (3 semester hours)

Prerequisites:  CISC 460 or permission of instructor

Description:  Data mining evolved from the disciplines of statistics and artificial intelligence.  This course addresses emerging topics to design, build, manage, and evaluate advanced data-intensive systems and applications.  Data engineering is defined as the role of data in the design, development, management, and utilization of complex computing or information systems.  Topics of interest include:  database design; meta-knowledge of the data and its processing; languages to describe data, define access, and manipulate databases; strategies and mechanisms for data access, security, and integrity control; and extracting, transforming and loading data (ETL).  Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these data repositories.  Successful applications have been developed for specialty areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments.  Offered Fall Semester, annually.


ANLY 415 Advanced Analytics and Reporting (3 semester hours)

Prerequisites:  ANLY 400 and 405

Description:  The student is introduced to deterministic and stochastic decision tools used by leading corporations and applied researchers.  The student utilizes these tools to solve complex, real-world problems, building on the basic theoretical understanding of optimization, simulation and predictive modeling obtained in prerequisite courses.  The student works with commercial decision modeling programs such as Premium Solver professional (linear, integer and non-linear optimization), TreePlan (decision-trees), Crystal Ball (simulation), and OptQuest (optimization under uncertainty).  Throughout the course, the importance of outside-the-model considerations, model limitations, and sources of modeling error are stressed while general frameworks for approaching particular problem types are developed.   Offered Spring Semester, annually.


ANLY 498 Project II (3 semester hours)

Prerequisites:  ANLY 298, an approved learning contract, permission of the Office of Experiential Programs, designation of an appropriate academic advisor

Description:  This project must be in the student’s program of study or concentration(s).  It should demonstrate application of the skills, methods, and knowledge of the discipline to solve a problem or answer a question representative of the type to be encountered in the student’s profession.  As with Project I, this is undertaken with the close mentorship of a faculty member, and may involve a community partner.  The ideal project has a clear purpose that builds directly upon the learning that occurs within the student’s first project and internship.  Offered as needed.