# Data Science Degree

A degree in data science from an institution based in the liberal education tradition offers a breadth of knowledge needed to approach and solve complex problems. For individuals who hold this degree, career opportunities exist in government, industry and academia. Such organizations in need of employees with these skills range from Amazon and Microsoft to local manufacturing facilities and from government agencies such as the NSA to local municipalities.

The number of job opportunities in the data science and analytics field is expected to grow dramatically with salaries typically in the six figures. This emerging field did not exist 10 years ago and is evolving rapidly with the exponential increase of data from online, social media and the expanding Web of Things. It is an area in which many disciplines and backgrounds participate.

## BS Degree — Data Science Major

In addition to the essential education requirements, majors in Data Science must complete 45 semester hours of coursework as described below:

- CSIT1100 Principles of Computing 3 s.h.
- CSIT1200 Data Structures 3 s.h.
- CSIT3300 Database Concepts and SQL 3 s.h.
- CSIT4200 Machine Learning 3 s.h.
- CSIT4300 Cluster Algorithms 3 s.h.
- DSCI1500 Beginning Data Science and Data Analytics 3 s.h.
- MATH1380 Introduction to Statistics 3 s.h.
- MATH1510 Calculus I 4 s.h.
- MATH1520 Calculus II 4 s.h.
- MATH2350 Discrete Mathematics 3 s.h.
- MATH2510 Calculus III 4 s.h.
- MATH3200 Probability and Stochastic Processes 3 s.h.
- MATH3340 Linear Algebra 3 s.h.
- MATH4380 Advanced Statistics 3 s.h.

**Courses for Data Science**

**CSIT1100 Principles of Computing 3 s.h.**

An introduction to the fundamentals of computer programming through extensive practice developing software in the Python language. Fundamental terminology and topics such as integrated development environments, variables, data types, control structures, functions, and objects will be covered. ELO4 Global Learning - Innovation

**CSIT1200 Data Structures 3 s.h.**

Continuation of CSIT1100 with emphasis on more advanced programming that involve classic data structures such as arrays, dictionaries, linked lists, queues, stacks, and trees. Recursive techniques and efficiency considerations will also be covered. Prerequisite: CSIT1100.

**CSIT2100 Intermediate Programming 1-3 s.h.**

Intermediate-level programming using a specific programming language, tool-set, methodology, or genre such as COBOL, C++, PHP, Ajax, debuggers, etc. May be repeated for credit if the content is different. Scheduled course title and transcript listing will include the programming language or topic; e.g. Intermediate Programming - C++. Prerequisite: CSIT1100.

**CSIT3300 Database Concepts and SQL 3 s.h.**

A study of the concepts and structures required to implement a database system including the logical design and physical organization of the database. Emphasis is given to the design and development of database systems that includes understanding and applying entity-relationship models. Implementation of a database using SQL on a database system is included. Prerequisite: CSIT1100.

**CSIT3710 Internship in Data Science (Also MATH3710) 3 s.h.**

Application of data science skills and methods to client projects. Students will interact with clients and prepare formal reports and presentations. (Graded on Pass/ Fail basis.) Prerequisite: Instructor’s consent.

**CSIT4200 Machine Learning 3 s.h.**

A study of regression, kernels, support vector machines, clustering, Neural networks. Prerequisites: MATH3340, MATH2520, MATH3200, CSIT1200.

**CSIT4300 Cluster Algorithms 3 s.h.**

Basic concepts of cluster analysis and algorithms are introduced. Methods for clustering validation and evaluation of clustering quality. Prerequisites: CSIT4200 (Machine Learning).

**MATH1380 Introduction to Statistics 3 s.h.**

Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. Elementary ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. Goal 3A, ELO6 Math

**MATH1510 Calculus I 4 s.h.**

Limits, continuity, differentiation, and applications including exponential, logarithmic, trigonometric, and inverse functions. Mean value theorem, curve sketching, Riemann sums, and the fundamental theorem of calculus. Prerequisite: 2 years high school algebra. Goal 3A, ELO6 Math

**MATH1520 Calculus II 4 s.h.**

Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510. Goal 3A

**MATH2350 Discrete Mathematics 3 s.h.**

A survey of topics in discrete mathematics focusing on introductory logic, methods of mathematical proof, set theory, determinants and matrices, combinatorics, and graph theory. Prerequisite: Instructor approval for non-CSIT/MATH majors, 2 years high school algebra or MATH1280. Goal 3A, ELO6 Math

**MATH2510 Calculus III 4 s.h.**

Conic sections, vectors in space, functions of several variables, partial differentiation, multiple integration, line integrals, and Green’s Theorem. Prerequisite: MATH1520. Goal 3A

**MATH2520 Calculus IV 3 s.h.**

Vectors in space, functions of several variables, partial differentiation, multiple integration, line integrals, and Green’s Theorem. Prerequisite: MATH2510. Goal 3A

**+ MATH3200 Probability and Stochastic Processes 3 s.h.**

Introduction to probability, classical probability models and processes, random variables, conditional probability, Markov Chains, and application. Prerequisite: MATH1520 and MATH2350. Goal 3A

**+MATH3340 Linear Algebra 3 s.h.**

Matrices, vector spaces, linear transformations. Prerequisite: MATH1510 and MATH2350. Goal 3A

**MATH3710 Internship in Data Science (Also CSIT3710) 3 s.h.**

Application of data science skills and methods to client projects. Students will interact with clients and prepare formal reports and presentations. (Graded on Pass/ Fail basis.) Prerequisite: Instructor’s consent.

**MATH4380 Advanced Statistics 3 s.h.**

A study of linear and generalized regression; random-effects models; methods for categorical data; survival analysis; and nonparametric methods, modeling. exploratory data analysis; modern nonparametric regression. Prerequisite: MATH1380, MATH2350