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As a Data Science major, you’ll take courses in programming, information and database systems design, data structures, calculus, and more.

The courses in the Data Science program focus specifically on learning the language of computers and refining your mathematics skills. You’ll also engage in a semester-long data analysis internship to develop an applied research project.

To learn more, you can also view the program strengths and learning outcomes for this program.

A degree in data science will require students to complete different classes from the computer science, mathematics, and statistics programs. Included in this coursework is a data analyst internship that consists of a semester-long research experience involving multivariable statistics in an applied research project.

• Callings and Careers in Computer-Related Fields: A survey of the various careers and fields of service that are possible in the field of computing. Topics include the breadth of opportunities available, insight into how to prepare, and guidance on selecting a unique set of concentration courses for the computer science major, and application to the computer science program.
• Programming I: An introduction to computer programming. Basic notions of abstraction, elementary composition principles, the fundamental data structures, and object-oriented programming technique are introduced. Topics include variables, control structures, arrays, and input/output.
• Information Systems Design: An introduction to the nature of information systems, the conceptual foundations and use of such systems. Topics include information systems project management, requirements analysis and use cases, structural and behavioral modeling, prototyping, use of the Unified Modeling Language, and an introduction to SQL database access.
• Programming II: A continuation of Computer Science 115. The course includes advanced programming techniques, in-depth examination of object-oriented principles, good programming style including documentation, basic data structures including array lists and linked lists, and basic algorithm design, with attention to the sorting problem.
• Data Structures: A study of the various types of information forms handled by a computer, including the format of data and the design and analysis of algorithms to manipulate data. Topics include the use of functional programming and multi-threaded algorithms.
• Database Systems Design: A study of the design, development, and implementation of an information system for management. Topics include database architecture, data definition and manipulation, report generation, and high-level language interface.
• Calculus I: A study of the basic concepts and techniques of calculus for students in all disciplines. Topics include limits, differentiation, integration, and applications. This course is intended for students without any previous calculus credit.
• Calculus II: Continuation of Mathematics 152; a study of transcendental functions, integration techniques, Taylor series approximations, calculus in polar coordinates, vectors, calculus of vector valued functions and applications of calculus. Students with one semester of calculus credit should take this course instead of Mathematics 152.
• Multivariable Calculus: A study of differential and integral calculus of functions of several variables, and line and surface integrals. Prerequisite: grade of C- or higher in Mathematics 153.
• Elementary Linear Algebra: An introductory study of vectors, matrices, linear transformations, vector spaces, determinants, and their applications, with particular emphasis upon solving systems of linear equations.
• Accelerated Introductory Statistics: This course covers the same content and learning objectives as Statistics 131 but in half the time. This course, along with Statistics 202 and Statistics 203, also serves as preparation for Actuarial Exam SRM. Additionally this course, along with Statistics 202, Statistics 203, Statistics 220 and Statistics 352, serves as preparation for Actuarial Exam MAS I. Offered first half of spring semester.
• Applied Statistical Models: This course surveys multivariable design and statistical methods used across various disciplines and seen in peer-reviewed research. Topics include multiple and non-linear regression, general linear models, multivariable statistical models, and multifactor experimental design emphasis is on active-learning using group activities and projects, critiquing research, and statistical software. Offered second half of spring semester.
• Statistical Programming in R: Data acquisition, cleaning, and management in R; use of regular expressions; functional and object-oriented programming; graphical, descriptive, and inferential statistical methods; random number generation; Monte Carlo methods including resampling, randomization, and simulation.
• Machine Learning/Modern Data Analysis Methods: An introductory survey of modern machine learning. Machine learning is an active and growing field that would require many courses to cover completely. This course aims at the middle of the theoretical versus practical spectrum. We will learn the concepts behind several machine learning algorithms without going deeply into the mathematics and gain practical experience applying them. We will consider both pattern recognition and artificial intelligence perspectives.
• Introduction to Univariate Probability: An introduction to the theory and techniques of general probability and common univariate probability distributions. Topics include but are not limited to basic set theory, introductory probability rules (independence, combinatorials, conditionals, Bayes theorem, etc.), common univariate distributions (e.g., binomial and normal) and expected value/variance. This course, along with Statistics 216, also serves as preparation for Actuarial Exam P/1. Offered first half of the semester.
• Introduction to Multivariate Probability: An introduction to multivariate probability distributions. Topics include but are not limited to joint probability density functions, conditional and marginal probability distributions, moment generating functions, covariance and correlations, transformations and linear combinations of independent random variables. This course, along with Statistics 215, also serves as preparation for Actuarial Exam P/1. Offered second half of the semester.
• Mathematical Statistics: The theory of hypothesis testing and its applications. Power and uniformly most powerful tests. Categorical data and nonparametric methods. Bayesian vs. Frequentist methods. Other selected topics. This course, along with Statistics 132, Statistics 202, Statistics 203 and Statistics 352, serves as preparation for Actuarial Exam MAS I. Additionally this course, along with Statistics 290 and Statistics 353, serves as preparation for Actuarial Exam MAS II.
• Introduction to Data Science: Introduction to the field of data science and the workflow of a data scientist. Types of data (tabular, textual, sparse, structured, temporal, geospatial), basic data management and manipulation, simple summaries, and visualization. This course also serves as preparation for
  Actuarial Exam PA. Additionally this course, along with Statistics 220 and Statistics 353, serves as preparation for Actuarial Exam MAS II.
• Data Analysis Internship: A semester-long research experience that involves a significant use of multivariable statistics in an applied research project. Students will identify and work with a primary faculty mentor to develop a project proposal prior to enrolling; students will also be supervised by a statistics professor. Part of the course will include an oral and written presentation of results. The course will be offered as needed and is run as an individual study. May be repeated for up to 12 credits. Permission of instructor required.

See the course catalog for more information.