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Data Science BS

About The Program

The Data Science Bachelor of Science degree offers students skills and knowledge in advanced analytics, data mining, business analytics, and statistics for generating meaningful insights and developing data-centric strategies essential for industry. Students seeking a Data Science bachelor’s degree are part of a multi-disciplinary program integrating coursework in four areas: statistics, mathematics, computer science, and management. Students graduating with the Data Science BS degree should be prepared to interact with data at all stages of an investigation and will possess the oral and written communication skills to work effectively within a team environment.

Student outcomes

After completing the Data Science major, students will be able to:

  • Use statistical knowledge and computing skills to formulate problems, extract and gather data from relevant resources following ethics guidelines
  • Use computational skills to manipulate, organize, scrape, and prepare data for analyses
  • Use statistical knowledge and computing skills to visualize and analyze data
  • Effectively communicate findings within a team environment
  • Effectively offer insights from data to scientists in diverse fields

How to enroll

Current students: Declare this program

Once you’re admitted as an undergraduate student and have met any further admission requirements your chosen program may have, you may declare a major or declare an optional minor.

Future students: Apply now

Apply to Metropolitan State: Start the journey toward your Data Science BS now. Learn about the steps to enroll or, if you have questions about what Metropolitan State can offer you, request information, visit campus or chat with an admissions counselor.

Get started on your Data Science BS

Program eligibility requirements

Students expressing interest in the Data Science Bachelor of Science when they apply for admission to the university will be assigned a faculty advisor in the Department of Mathematics & Statistics and will be given premajor status.

Students interested in pursuing the Data Science BS should take the following steps:

1. Speak with a faculty member in the Mathematics & Statistics Department or contact the Chair of the department ( to learn more about the Data Science BS.

2. Complete the following Premajor Foundation Courses:

  • Take STAT 201 Statistics I, ICS 140 Introduction to Computational Thinking with Programming, and MATH 215 Discrete Mathematics

  • Attain grades of C- or higher and a cumulative GPA of 2.5 or higher in the above courses.

3. Declare the Data Science BS using the online College of Sciences declaration form.

Transfer coursework equivalency is determined by the Mathematics and Statistics Department.

Courses and Requirements


Students must complete premajor courses with grades C- or higher and with a cumulative GPA of 2.50 or higher to be admitted into the program.Students must complete a minimum of 20 credits in the program at Metropolitan State University. Take note of prerequisites prior to registering for courses.

Major Requirements

+ Premajor Foundation (12 credits)

This course introduces fundamental concepts in computer programming and the development of computer programs to solve problems across various application domains. Topics include number systems, Boolean algebra, variables, decision-making and iterative structures, lists, file manipulation, and problem deconstruction via modular design approaches. Lab work and homework assignments involving programming using a language such as Python form an integral part of the course.

Full course description for Computational Thinking with Programming

This course covers the basic principles and methods of statistics. It emphasizes techniques and applications in real-world problem solving and decision making. Topics include frequency distributions, measures of location and variation, probability, sampling, design of experiments, sampling distributions, interval estimation, hypothesis testing, correlation and regression.

Full course description for Statistics I

+ Core (44 credits)
Math, Data, & Statistics Courses

An introduction to methods and techniques commonly used in data science. This course will use object-oriented computer programming related to the processing, summarization and visualization of data, which will prepare students to use data in their field of study and to effectively communicate quantitative findings. Topics will include basics in computer programming, data visualization, data wrangling, data reshaping, ethical issues with the use of data, and data analysis using an object-oriented programming language. Students will complete a data science project.

Full course description for Data Science and Visualization

This course covers introductory and intermediate ideas of the analysis of variance (ANOVA) method of statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include one-factor ANOVA models, two-factor ANOVA models, repeated-measures designs, random and mixed effects, principle component analysis, linear discriminant analysis and cluster analysis.

Full course description for Analysis of Variance and Multivariate Analysis

This course covers fundamental to intermediate regression analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include simple and bivariate linear regression, residual analysis, multiple linear model building, logistic regression, the general linear model, analysis of covariance, and analysis of time series data.

Full course description for Regression Analysis

Statistical machine learning (often referred to simply as statistical learning) has arisen as a recent subfield of statistics. It emphasizes the interpretability, precision, and uncertainty of machine learning models. This course assesses the accuracy of several supervised and unsupervised machine learning models for both regression and classification. Topics include the bias-variance trade-off, training and test datasets, resampling methods, shrinkage and dimension reduction methods, non-linear modeling techniques such as regression splines and generalized additive models, and decision tree-based methods. Applications include examples from medicine, biology, marketing, finance, insurance, and sports.

Full course description for Applied Machine Learning

Computer Science Courses

Structure, design, and implementation of object-oriented computer programs. Topics include sequential structures, selection structures, repetition structures, recursion, quadratic sorting algorithms, exceptions, objects, and classes. Emphasis on methods, parameter passing, arrays, and arrays of objects. Exploration of problem-solving and algorithm-design techniques using pseudocode and Unified Modeling Language (UML). Design of good test cases and debugging techniques are highlighted. Programming projects involving multiple classes.

Full course description for Problem Solving with Programming

This course provides basic introduction to data structures and algorithms and emphasizes the relationship between algorithms and programming. Students will learn intermediate object-oriented design, programming, testing and debugging. Topics include inheritance, polymorphism, algorithm complexity, generic programming, linked list, stack, queue, recursion, trees, hashing, searching, and sorting.

Full course description for Introduction to Data Structures

Covers concepts and methods in the definition, creation and management of databases. Emphasis is placed on usage of appropriate methods and tools to design and implement databases to meet identified business needs. Topics include conceptual, logical and physical database design theories and techniques, such as use of Entity Relationship diagrams, query tools and SQL; responsibilities of data and database administrators; database integrity, security and privacy; and current and emerging trends. Use of database management systems such as MySQL. Coverage of HCI (Human Computer Interaction) topics and development of front ends to databases with application of HCI principles to provide a high level usability experience. Overlap: ICS 311T Database Management Systems.

Full course description for Database Management Systems

This course presents the key algorithms and theory of machine learning. Students will examine supervised and unsupervised learning algorithms. And they will gain an understanding of machine learning foundational concepts used in artificial intelligence, statistics and data science. Topics include learning algorithms used in recent application as autonomous vehicles, google search, and Facebook photo tags.

Full course description for Machine Learning

Management Information Systems Courses

Business Intelligence is the user-centered process of exploring data, data relationships and trends - thus helping to improve overall decision making for enterprises. This course addresses the iterative processes of accessing data (ideally stored in the enterprise data warehouse) and analyzing data in order to derive insights and communicate findings. Moreover, the course also addresses the use of software tools for analysis and visualization of data, especially report design along with the use of dashboards.

Full course description for Business Intelligence and Analytics

This course builds upon prior coursework related to analytical thinking and competence in business intelligence and analytics approaches. The course serves to advance and refine expertise on theories, approaches, tools and techniques related to prediction and forecasting in business. Students will gain practical experience in analyzing a variety of business analytics cases and scenarios using industry-standard tools and platforms. The course prepares learners to help organizations make more effective business decisions based on the gathering and analysis of data. The design and delivery of the course enables an engaged learning environment.

Full course description for Predictive Analytics

+ Electives (8 credits)

Students must complete two of the following courses. With approval from your academic advisor, other upper division mathematics courses may also fulfill this requirement.

This course covers the fundamental to intermediate ideas of nonparametric statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include nonparametric methods for paired data, Wilcoxon Rank-Sum Tests, Kruskal-Wallis Tests, goodness-of-fit tests, nonparametric linear correlation and regression. Completion of STAT201 (Statistics I) is a prerequisite for this course.

Full course description for Nonparametric Statistical Methods

This course covers the fundamental to intermediate ideas of the statistical analysis of categorical data. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include analysis of 2x2 tables, stratified categorical analyses, estimation of odds ratios, analysis of general two-way and three-way tables, probit analysis, and analysis of loglinear models. Completion of STAT201 (Statistics I) is a prerequisite.

Full course description for Analysis of Categorical Data

A time series is a sequence of observations on a variable measured at successive points in time or over successive periods of time. This course provides an introduction to both standard and advanced time series analysis and forecasting methods. Graphical techniques and numerical summaries are used to identify data patterns such as seasonal and cyclical trends. Forecasting methods covered include: Moving averages, weighted moving averages, exponential smoothing, state-space models, simple linear regression, multiple regression, classification and regression trees, and neural networks. Measures of forecast accuracy are used to determine which method to use for obtaining forecasts for future time periods.

Full course description for Time Series Analysis and Forecasting

Covers the concepts and approaches that are used by big-data systems. Topics covered include: fundamentals of big data storage and processing using distributed file systems, the map-reduce programming paradigm, and NoSQL systems. Students will gain hands-on experience by implementing solutions to big data problems using tools like Hadoop, Apache Pig Latin, Hive, Impala, MongoDB, Cassandra, Neo4J, or Spark.

Full course description for Big Data Storage and Processing

Data Mining involves an intelligent analysis and discovery of patterns information stored in data sets. It has gained a high attention among practitioners in a variety of industries and fields. Nowadays, almost every institution collects data, which can be analyzed in order to support making better decisions, improving policies, discovering computer network intrusion patterns, designing new drugs, detecting credit fraud, making accurate medical diagnoses, predicting imminent occurrences of important events, monitoring and evaluation of reliability to preempt failures of complex systems, etc. In this course, the students will be exposed to data mining concepts, techniques, and software utilized in the overall process of discovering knowledge within data.

Full course description for Computational Data Mining

Covers the fundamental concepts of distributed computer systems and its major branch of cloud computing in which computing is delivered as a service over a network whereby resources are rented rather than owned by the end user. Topics include distributed operating and file systems, cloud-enabling technologies, virtualization, cloud service models, cloud platform architecture, and cloud services including compute, storage, networking, and big data services. Students will gain hands-on experience by implementing projects utilizing public cloud infrastructures like Amazon Web Services (AWS), Google App Engine, or Microsoft Azure.

Full course description for Distributed and Cloud Computing

This course provides an introduction to the field of social network analysis. Social network analysis is applied in different areas such as health, cyber security, information retrieval and communications. The focus of this course is on network analysis and theory. This course introduces the main structural concepts of social networks, and it combines theory and practice using programming. Students will explore several examples related to social network analysis. Students will apply NetworkX Python library in creating, manipulating, and study of the structure of social network.

Full course description for Social Network Analysis

This course is designed to define the role of information systems in organizations, and in particular the roles of IS staff and end-users in developing and maintaining computer systems. The managerial aspects and implications of databases, telecommunications, hardware, software and e-commerce are included. Special attention is given to management information systems theories in the organizational setting including: infrastructure, transaction processing, operational reporting, decision support systems and executive information systems. Also included are all phases of the systems development life cycle (SDLC) as well as alternative development methodologies. The course prototypically includes analysis of real world business cases and post-implementation audit report of a recently completed management information system. All students taking this class must have completed as a prerequisite the MIS 100 Fundamentals of Information Technology in Organizations course or its approved…

Full course description for Principles of Management Information Systems

This course presents approaches and methods for the analysis and design of IT applications. It also covers different methods for creating graphical models of IT project requirements. System development life cycle (SDLC) and alternate development approaches to information systems development are examined in detail. The course provides students with critical tools and representations (both traditional and object-oriented) for eliciting and documenting user requirements and for developing effective applications that meet organizational technology needs. Students work individually and in teams on assignments and projects. The roles of open source software, component based development and service oriented architecture in systems development are also examined.

Full course description for Information Systems Analysis and Design

Competence in management and use of organizational and external databases is a skill needed by all business people and critical to management information systems effectiveness, especially in the new era of "big data". This course teaches the development and accessing of internal and external information resources. Topics include: ensuring the availability of appropriate data; interrelating and applying data to typical business problems; normalized database design; protecting and managing information resources; scalability; and compatibility issues.

Full course description for Management and Use of Databases

+ Integrative Experience (4 credits)

Students must complete one of the following courses.

This advanced workshop will give students exposure to the statistical and non-statistical issues that arise in statistical problem solving, and provide an experiential background in statistical consulting. Students will develop the knowledge, skills, and professional rapport necessary to interact with clients, including the skills necessary for communicating technical statistical content with non-statisticians.

Full course description for Statistical Consulting

This course provides a culminating experience in formulating and resolving data science and business analytics questions, regardless of domain or nature of scientific inquiry. Students work in teams on a comprehensive project to apply data science concepts and principles. Students will complete a real-world project with data collection, data cleaning, data visualization, data modeling and analysis, and presentations of findings.

Full course description for Data Science Capstone

Internships offer students opportunities to gain deeper knowledge and skills in their chosen field. Students are responsible for locating their own internship. Metro faculty members serve as liaisons to the internship sites¿ supervisors and as evaluators to monitor student work and give academic credit for learning. Students are eligible to earn 1 credit for every 40 hours of work completed at their internship site. Students interested in internships within the Mathematics and Statistics department should work with their advisor and/or faculty internship coordinator to discuss the process for your specific major.

Full course description for Data Science Internship