Data Analytics

The fields of business administration, marketing, finance, insurance, professional services, and information technology. Additionally, rapid growth in the areas of telemedical services and health informatics is generating a demand for practitioners who develop the knowledge, skills, and abilities to organize, interpret and publish the increasing volume and complexity of health data.

All courses are eight-week sessions. There are 2 sessions in a semester. To be considered a full-time undergraduate student, a student must take 12 credit hours per semester. For the 12 credit hours, the student must take 2 courses (6 credit hours) the first eight-week session and 2 courses (6 credit hours) the second eight-week session. Semesters in this program are Spring, Summer, and Fall.

The regional outlook is strong, with 15.83% job growth expected over the next 10 years, and 14.02% job growth, nationally. Therefore, the market demand for graduates of this particular degree is evident.

BUSN 2614 Qualitative Methods
Models for decision-making for marketing, finance, accounting, production and operations management, parametric and nonparametric statistics. An introduction to simple regression models, constrained and unconstrained optimization, and other techniques. Prerequisite: MATH 1220.

INDS 3610 Pre-Internship
The purpose of this course is to prepare students for the practicum/internship experience. Topics included are internship selection, making the most of the internship, resume building, and facing internship challenges.

BUSN 3502 Business Ethics
This course includes an analysis of business policies and practices with respect to their social and moral impact. It raises basic questions on moral reasoning and the morality of economic systems, both nationally and internationally. It also examines the impact of governmental regulations on corporate behavior, and the ethical relationships between the corporation and the public. Prerequisite: BS 101. (Offered every fall.)

CYBR 1211 Introduction to Computer Programming
This course introduces computer programming and problem solving in a structured program logic environment. Topics include language syntax, data types, program organization, problem-solving methods, algorithm design, and logic control structures. Upon completion, students should be able to use top-down algorithm design and implement algorithmic solutions in a programming language. Examples and assignments will be in C++. Prerequisites: None

CYBR 2112 Introduction to Secure Scripting
This course offers an in-depth introduction to scripting languages, including basic data types, control structures, regular expressions, input/output, and textual analysis. Examples and assignments will be in Python. Prerequisite: CS 122

CYBR 2213 Database Programming
A course introducing the student to the logic, design, implementation, and accessing of organizational databases as contrasted to older conventional data file techniques introduced in COBOL programming. Particular emphasis is placed on relational database management that focuses on the logical nature of databases. Popular microcomputer-based database programs will be utilized. Prerequisite: None

CYBR 2311 Computer and Systems Security
An in-depth study of computer and systems security covering the domains of the Security+ Certification. Focus is on the knowledge and skills required to identify risk and participate in risk mitigation activities, provide infrastructure, application, operational and information security, apply security controls to maintain confidentiality, integrity and availability, identify appropriate technologies and products, and operate with an awareness of applicable policies, laws and regulations. Prerequisite: Sophomore standing.

DATA 1552 Data Analytics Tools
A study of the basic principles of data science and the tools and skills that are essential in data science. Topics to be covered include: data acquisition, cleaning, processing, and drawing inferences from such data. Prerequisite: BS 214.

DATA 2552 Applied Statistics for Data Analytics
A study of the methods of statistical description, inference, probability, sampling, hypothesis testing and regression analysis with a focus on application to real situations. Prerequisite: BS 214.

DATA 2553 Data Structures and Algorithms
Statistical models for data analysis and discovery in big-data settings, with primary focus on linear regression models. The challenges of building meaningful models from vast data are explored, and emphasis is placed on model building and the use of numerical and graphical diagnostics for assessing model fit. Interpretation and communication of the results of analyses is emphasized. Prerequisites: DATA 1552.

DATA 3553 Dataset Organization, Reporting, and Management
The study of the basic principles of organizing, managing, and presenting (visual format) data in multiple ways from any source with applications in multiple disciplines. Prerequisites: DATA 2553.

DATA 3554 Introduction to Data Mining, Machine Learning & AI
Students learn basic principles of data mining which include methods for locating, extracting, processing, determining appropriate methods for handling and ultimately extracting useful knowledge from raw data. Topics to be covered include: data extraction, cleaning, and other preprocessing tasks of data, classifications, clustering, transformation, pattern recognition, anomaly detection, machine learning, artificial intelligence, and overall knowledge discovery. Prerequisites: DATA 2553.

DATA 4625 Internship
Supervised internship provides students with the opportunity to integrate classroom instruction with on-the-job training in an area associated with data analytics. Prerequisite: DATA 3553, IS 310, junior standing (Offered by department discretion)

DATA 4552 Introduction to Big Data
A course that looks at the concept of “Big data.” “Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs.” -Gartner Trends in big data include new technologies for data storage, new tools to mine data from these huge datasets, new database technologies to address the three Vs. Open Source tools will primarily be utilized. Prerequisites: DATA 3554

DATA 4653 Data Analytics Capstone
An independent research project done at the senior level. It involves a project supervised by a faculty member. The number of hours required is a minimum 15 contact hours for each credit hour and depends on student interest, standing, and background. Prerequisites: senior standing.