According to data from the US Bureau of Labor, between 2019 and 2029, the 11th fastest growing job is data scientists with a growth of 31% and a median salary of $98,000. This minor was developed with input from one of the leaders in the Big Data solutions world. With many Fortune 100 companies as clients, the main focus of this minor is to teach students the hard and soft skills needed to break into this rapidly growing field. Chadron State College is offering the first Minor in Data Analytics in the state of Nebraska. The curriculum starts with the assumption that the student has limited to no experience with programming. By setting the foundation properly with an eye toward what employers want and providing experience in highly sought-after fields such as machine learning and business intelligence, students who complete this minor are setting themselves up to provide an essential skillset for a multitude of industries or employers where they might be employed.
Minor in Data Analytics Plan of Study
Fall Semester
| MATH 200 | Intro to Data Analytics | 3 |
| MATH 201 | Intro to Programmatic Data | 3 |
| MATH 202 | Intro to Database Structures | 3 |
Spring Semester
| MATH 301 | Data Life Cycle and Appl. Devel. | 3 |
| MATH 302 | Applied ‘Big Data’ | 3 |
Fall Semester
| MATH 439 | Theory of Statistics | 3 |
Spring Semester
| MATH 426 | Operations Research | 3 |
MATH 200 and MATH 201 can be taken concurrently.
MATH 200 is offered each semester.
MATH 201 is a prerequisite for MATH 202.
MATH 202 is a prerequisite for MATH 301.
MATH 301 is a prerequisite for MATH 302.
MATH 429 is offered in the fall of even numbered years (and alternate summers).
MATH 426 is offered in the spring of even numbered years (and alternate summers).
Course Descriptions
MATH 200 Introduction to Data Analytics (3 cr)
Introduction to statistical programming in R and its applications. Students will become familiar with the process, techniques, and goals of exploratory data analysis. Students will be able to create, assess, debug code effectively, and interpret their findings in an effective manner.
MATH 201 Introduction to Programmatic Data (3 cr)
Introduction to programming: a holistic approach to learning how to code with a lens toward Big Data principles. Topics include but are not limited to Datatypes, Immutables, Functions, Packages, Loops, Recursion, and an introduction to object oriented programming (OOP)
MATH 202 Database Structures (3 cr)
Principles of the RDBMS, DBMS, Structured Query Language (SQL), MySQL, NoSQL, JSON, Remote Database Access, and API Requests
MATH 301 Data Life Cycle and Application Development (3 cr)
Principles of the Data Life Cycle and Management, Applying DLM principles to a real world scenario and data situations, Applying programming principles to learning additional languages
MATH 302 Applied Big Data (3 cr)
Machine learning, simple and linear regression, principal component analysis, neuro-linguistic programming, visualizations, and additional topics relevant to the field of “Big Data” – analysis and applications
MATH 426 Operations Research (3 cr)
Game theory, linear programming, simplex method, duality, transportation and assignment problems, introduction to dynamic programming, and queuing theory. Applications of business and industrial perspectives.
MATH 439 Theory of Statistics (3 cr)
Joint distribution concepts, conditional expectations, method of distribution functions, transformation, method of moment-generating functions, order statistics, sampling distributions, central limit theorem, continuous and discrete
random variables.