Contact or visit us
Austin Mohr, Ph.D.
Associate Professor of Mathematics and Computer Science
(803) 543-8735
amohr [at] nebrwesleyan.edu (amohr[at]nebrwesleyan[dot]edu)
Data Analytics (B.A., B.S.)
º¬Ð߲ݴ«Ã½â€™s degree in data analytics brings together skills in computer programming, quantitative reasoning, collaboration, communication and creative thinking. Our students develop a broad technological toolkit they can use to obtain, visualize and analyze complex data.
The cognitive flexibility and problem-solving skills we develop in this program are key to understanding life in an information-rich society. They’re also crucial to success in our rapidly changing professional environments. By creating data visualizations, you can effectively convey a message or tell a story.
Data analytics majors may choose from six concentration areas of specialization:
- advanced data analytics
- business
- computer science
- cybersecurity
- project management
- supply chain management
Academically equivalent, both the Bachelor of Arts and the Bachelor of Science will fully prepare you for a career in data analytics. If you choose to graduate with two majors, and one major is only offered as a B.A. or B.S., your data analytics major will match the first degree.
starting salary for data analysts according to Glassdoor
data analytics job growth over the next decade
Learn more.
The Data Analytics major brings together skills in computer programming, quantitative reasoning, collaboration, communication, and creative thinking. Students who pursue this major will develop a broad technological toolkit for obtaining, analyzing, and visualizing data. By applying their skills to projects and internships, students will acquire flexible problem-solving skills for rapidly-changing professional environment.
Academically equivalent, both bachelor of arts and bachelor of science degrees will fully prepare you for a career in data analytics. If you choose to graduate with two majors, and the one major is only offered as a B.A. or B.S., the second major should match the first degree.
º¬Ð߲ݴ«Ã½â€™s data analytics program features in-person courses enhanced with a selection of specialized online courses taught by national experts. Below is a list of data analytics courses required for this degree.
| Technical Foundations | 20 hours |
|---|---|
| CMPSC 2200 Python Programming I | 4 hours |
| CMPSC 3200 Python Programming II | 4 hours |
| DATA 1200 Excel and SQL Programming | 4 hours |
| DATA 1350 Introduction To Data Analytics | 4 hours |
| DATA 3100 Data Visualization With R | 4 hours |
| Concentration (Choose one) | 6 hours |
|---|---|
Advanced Data Analytics | 3 hours 3 hours |
Business | 3 hours 3 hours |
Project Management | 3 hours 3 hours |
Cybersecurity | 3 hours 3 hours |
Supply Chain Management | 3 hours 3 hours |
Computer Science | 3 hours 3 hours |
Other Students may propose an alternate concentration in an area of their interest relevant to data analytics. The proposed concentration must be comprised of two courses representing at least six credits of study and must be approved by their academic advisor and the department chair. | 6 hours |
| Experiential Learning Internship | 6 hours |
|---|---|
| DATA 4970 Internship | 3 hours |
| Capstone | 3 hours |
|---|---|
| DATA 4980 Capstone Project | 3 hours |
| Supporting Program | 6 - 7 hours |
|---|---|
| MATH 1300 Statistics | 3 hours |
Take one of the following: | 3 - 4 hours |
*This course is offered remotely via º¬Ð߲ݴ«Ã½'s partnership with a Consortium. The partnership allows students to earn º¬Ð߲ݴ«Ã½ credit for specific courses. Classes are designed by top academics and industry leaders, vetted by º¬Ð߲ݴ«Ã½, and taught by experts in the field.
**A Data Analytics major may earn either a B.A. or B.S. degree. However, if a student has a first major that is associated with a different baccalaureate degree, the Data Analytics major may serve as a second major for the degree associated with the first major (B.FA., B.M., B.S.N.).
This course will introduce students to the power of effective project management through two primary frameworks: waterfall and agile. Students will also learn vital project-management concepts applicable to a wider range of industries and occupations. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
This course will review the basics of effective oral and written communication and apply these basics to business writing and presentations. A variety of individual and collaborative projects, including memos, letters, and reports, will emphasize the process of drafting, revising, and editing business communications.
Prerequisite(s): Accounting, Business Administration, Business Analytics, Cybersecurity, Economics, International Business, or Sport Management major.
Any successful project starts with a plan. This course provides students with a deep understanding of project planning. Projects are a series of tradeoffs between scope, cost, and time, so students will need to learn how to balance them to create a realistic and achievable plan. Students will also learn to leverage resources and manage risk, quality, and stakeholder expectations to ensure project success. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): Grade of "C-" or better in BUSAD 1650 Introduction to Project Management.
This course will provide an introduction and overview to the managing of information systems (MIS) in today's organizations. The focus is on the use of strategic information systems related to decision making processes and activities in the functional areas of organizations such as operations, management, and marketing.
Prerequisite(s): BUSAD 2500 Principles of Management or permission of the instructor.
This course will review modern quantitative methods used in decision making. The intent is to expose the student to various modeling techniques and to apply these techniques using Excel. Topics include productivity and capacity analysis, forecasting, regression analysis, linear programming, PERT/CPM, and statistical process control.
Prerequisite(s): Grade of "C-" or better in BUSAD 2100 Business and Economic Statistics, ECON-2100, MATH 1300 Statistics, or MATH 3300 Mathematical Statistics I, and one of MATH 1100 College Algebra or MATH 1600 Calculus I, or department chair permission.
(Normally offered each semester.)
An introduction to computational problem-solving using Python. Hands-on labs are used to motivate basic programming concepts, including basic data types and structures, functions, conditionals, and loops. Additional topics may include building and scraping HTML webpages. The course is recommended for all who wish to explore data science and/or computer science. Recommended: Math ACT score of at least 21 or instructor permission of a prerequisite waiver.
(Normally offered every spring semester.)
This course, built in collaboration with Google, will teach you how to understand and use data structures. Data structures are used by almost every program and application to store, access and modify the vast quantities of data that are needed by modern software. By the end of this course you'll learn what data structures are and learn how to use them in the applications you build. This online class has optional live sessions. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions. Prerequisite(s): CMPSC-2100.
A project-based continuation of the techniques developed in CMPSC 2200 Python Programming I. Topics include object-oriented programming, algorithm design and analysis, data structures, and general problem-solving techniques (such as recursion) while following industry-standard software development principles. Prerequisite(s): Grade of "C" or better in CMPSC-2200 or instructor permission of a prerequisite waiver.
(Normally offered every fall semester.)
This course explores algorithms from a coding-focused perspective, using Python. Students will learn about the issues that arise in the design of algorithms for solving computational problems and will explore a number of standard algorithm design paradigms and their applicability. Students will also become familiar with concepts of runtime, recursion, implementation and evaluation. This course features a heavy emphasis on practical application of algorithms to common development and engineering challenges. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): CMPSC 3000 Data Structures and MATH 1600 Calculus I.
Students will explore components of leadership theory, skills, and behaviors, and will examine and practice effective communication behaviors as related to leadership processes and roles.
A study of theories and practices of persuasion within a variety of communication contexts. Students will be expected to apply these concepts to out-of-class persuasive situations.
Prerequisite(s): Junior standing.
(Normally offered each semester.)
This course is designed to help students develop theoretical and practical understandings of dialogic communication. Students will develop the skills necessary to effectively participate in and facilitate transformational dialogue. In addition to developing a comprehensive understanding of current dialogic research, students will have several opportunities to practice their facilitating skills by helping º¬Ð߲ݴ«Ã½ and Lincoln community groups engage impasse through dialogue.
Prerequisite(s): Sophomore standing.
Students will create and deliver presentations for a variety of communication contexts and audiences. Skills in interviewing and group problem solving will be also be developed.
Prerequisite(s): Junior standing and instructor permission.
(Normally offered each semester.)
A study of managing, manipulating, and summarizing data using Excel and SQL. Topics in Excel include, but are not limited to: functions, filters, charts and visualizations, pivot tables, and macros. Topics in SQL include, but are not limited to: queries, joins, and basic database management.
(Normally offered every spring semester.)
An introduction to data analytics from three perspectives: inferential thinking, computational thinking and real-world relevance. Topics include, but are not limited to: organizing real-world data by filtering, sorting, and using pivot tables; exploring data; visualizing data; using programming tools to analyze data through a statistical lens. Statistical topics include: center and spread of data, descriptive statistics, inferential statistics, regression, causality, classification and prediction.
(Normally offered every fall semester.)
In today's world, no one is safe from cyber-attacks, but everyone can be prepared. This course will teach you how malicious actors use social skills and technology to facilitate cyber attacks and provide you with the tools and information you need to defend against those attacks. Whether you pursue one of the many available jobs in cybersecurity or just want to secure your own privacy, you'll learn how to make the Internet safer.This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Supply chain management is the process by which organizations get us the products we consume, and companies need talented employees to help optimize their supply chain. This course will teach you how to use forecasting techniques to match supply and demand, and how to develop logistics networks that help minimize costs and deliver top customer service. This online class has optional live sessions. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
In today's modern economy, something as simple as a razor might be manufactured in multiple countries with each part coming from a different supplier. This course will teach you how businesses manage this increasing complexity behind the scenes through efficient sourcing of suppliers and operations. You will have the opportunity to apply this knowledge by conducting a real-world case study of a product of your choosing. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): DATA 2200 Forecasting And Logistics.
Cybercrime is one of the biggest threats companies face on a daily basis, and they are constantly looking for new hires to help protect them. In this course, you will get a firsthand look at the methods used to commit cybercrimes. You will also learn how governments detect, investigate, and stop these crimes, and become familiar with the laws and policies in place to deter cybercriminals. This online class has optional live sessions. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): DATA 1700 Introduction to Cybersecurity.
A study of data visualization, including principles and techniques. Students will analyze the effectiveness of visualizations, create a wide array of visualizations using the programming language R, and communicate a story through them. Significant emphasis will be placed on getting and cleaning data.
Prerequisite(s): Grade of "C" or better in CMPSC 2200 Python Programming I and grade of "C" or better in MATH 1300 Statistics.
(Normally offered every spring semester.)
This course explores key principles and techniques of data science in Python, including programming foundations, data management, advanced data visualization, integration with SQL, and machine learning. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): CMPSC-2100 and DATA-1400.
This course builds on DATA 3200 by exploring key principles and techniques in time series analysis and forecasting, advanced regression, unsupervised and deep learning, feature engineering, and ethical issues. This course is an online class offered through the Lower Cost Models Consortium.
Prerequisite(s): DATA 3200 Principles and Techniques of Data Analytics I and MATH 1600 Calculus I.
On-the-job training in data analytics in situations that satisfy the mutual interests of the student, the supervisor, and the instructor. The student will arrange for the position in accordance with the guidelines established by the department. Pass/Fail only.
Prerequisite(s): Permission of the instructor and approval of the department chair.
A student-driven collaborative project synthesizing skills developed in the data analytics major.
Prerequisite(s): At least Junior standing and grades of "C" or better in CMPSC-2100 and DATA 3100 Data Visualization With R.
(Normally offered every spring semester.)
An introduction to statistics concepts with an emphasis on applications. Topics include descriptive statistics, discrete and continuous probability distributions, the central limit theorem, confidence intervals, hypothesis testing, and linear regression.
(Normally offered every semester.)