Applied Data Science Analytics Curriculum
The Applied Data Science Analytics curriculum allows students to develop theoretical understanding of data analytics and translate theory into practice through handson applications. Students can benefit from innovative courses such as Digital Marketing (BUS496), which engages students in the analytics of online advertising and promotion data, and Careers for the Digital Age (IND250), which explores computing and digital skills essential to professionals in the 21st century.
Students can also choose a minor in a specialized field, such as a business field, political science, sustainability, biology, psychology, mathematics, or more.
Program Requirements

+Major Requirements

51 credits
BUS171 Information Systems and Operations This course explores basic concepts of communication networks (e.g., the Internet), hardware, software, databases, and systems. Students apply information systems to decision making, communication, collaboration and coordination in the operations of contemporary organizations. Students gain skills in word processing, presentation software, data visualization, spreadsheets, and relational databases.
3 BUS310W Business Analytics: Research Methods This course introduces research methods and tools as the foundations of business analytics. Topics include problem definition, literature review, theory development, research design, sampling theory, construct measurement, data collection, data analysis, reporting results, interpreting findings, and developing actionable recommendations.
3 BUS421 Information and Cybersecurity This course introduces fundamental issues in information and cybersecurity, with an emphasis on vulnerabilities available to cyber attackers. Students develop conceptual tools for identifying vulnerabilities, assessing threats, analyzing risk, and selecting controls to mitigate risk, and practical skills in implementing security, responding to incidents, and designing systems that prevent cyberattacks.
3 CMP120 Introduction to Programming An introduction to the theory and practice of computer programming with an emphasis on problem solving. No previous programming experience is required.
3 CMP202 Introduction to Programming An introduction to programming using C++ for students with no previous computer programming experience. Includes introduction to algorithms and objectoriented programming techniques.
3 CMP283 Database Management Systems This course is a study of database management systems and their applications to a wide range of information processing needs. Students design and implement database management systems while being introduced to a conceptual model of a database environment comprised of five basic components: databases, database management systems, data dictionary/directory systems, database administration, and usersystem interfaces.
3 DSA150 Introduction to Data Science Data Science is the study of the tools and process used to extract knowledge from data. This course introduces students to this important, interdisciplinary field with applications in business, communications, healthcare, etc. Students learn the basics of data organization, packaging, and delivery. Simple algorithms and data mining techniques are introduced.
3 DSA400W Data Visualization and Communication Cover the different ways of visualizing data, given different types and characteristics of data. Includes assessment and evaluation of existing data visualization techniques. Current tools used transform data and visualize data are reviewed, including Python, Google Charts, and/or Tableau.
3 DSA411 Machine Learning and AI An introduction to machine learning and artificial intenlligence. Topics include classification, regression, clustering, planning, and scheduling. Includes current issues relevant to big data problems.
3 INTDSA303 Internship  Data Science Analytics 3 MTH110 Elementary Statistics Topics include statistical measures and distributions, decision making under uncertainty, application of probability to statistical inference, linear correlation, introduction to nonparametric statistical methods, and application to problems drawn from the natural and social sciences. Three hours of class per week. Three hours of class per week.
3 MTH151 Calculus I This is the first course in the calculus sequence. Topics include differential and integral calculus for algebraic and trigonometirc functions with applications. Four hours of class per week.
4 MTH152 Calculus II This is the second course in the calculus sequence. Topics include differential and integral calculus for the transcendental functions, advanced methods of integration, and infinite sequences and series.
4 MTH221 Linear Algebra Topics include finite dimensional vector spaces, geometry of R, linear functions, systems of linear equations, and theory of matrices and determinants.
3 MTH222 Multivariate and Vector Calculus An introduction to multivariate calculus using vector spaces, partial differentiation and multiple integration, calculus of vector functions, applications to extremum problems, and differential equations. Three hours of class per week.
3 MTH244 Discrete Mathematics This course is an introduction to the fundamental logic and mathematical concepts of discrete quantities, as employed in digital computers. Emphasis will be on the careful and precise expression of ideas. Topics include sets and logic, relations and functions, proof techniques, algorithms, combinatories, discrete probability, graphs, and trees. Three hours of class per week.
3 MTH310 Probability An introduction to the theory of probability and the role of proofs in mathematics. Topics include discrete and continuous probability functions, random variables, expectations, moments, moment generating functions, the central limit theorem, and Chebyshev's inequality. Applications of probability such as queuing theory, Markov processes, and reliability theory also will be covered. Three hours of class per week.
3 Nine (9) credits of approved electives: choose from list below, or from the list of 200+ level MTH courses, or get Program Director approval BUS317 Systems Analysis and Design This course introduces information systems analysis and design for contemporary organizations, with a focus on developing critical skills in communicating with people as users, analyzing processes, translating needs into information systems requirements, and testing of prototype ideas. Topics also include functional, structural, and behavioral modeling, and Unified Modeling Language (UML).
3 BUS416 Computer Networking & Telecommunication This course introduces students to the foundational network technologies for data encoding and transmission. Topics may include telephone network and internet architecture, communication protocols (e.g., HTTP, SMTP), transport protocols (e.g., UDP, TCP), and network protocols (IP), TCP/IP, LANs, WANs, circuit vs. packet switching, network security, and multimedia.
3 COM261 Web Design I: Code + Aesthetics This introductory course in web design and net art production addresses formal design, aesthetic, conceptual and theoretical methods for the creative production and dissemination of student projects via a global network. Technical focus is on authoring nonlinear documents using software and basic web programming languages. Students conceptualize projects around a variety of topics including: online social networks, memory and database theory, cultural interfaces, the screen and the body, and collective media. Crosslisted as FDT261. Additional Fee(s): Course Computing Fee.
3 SUS404 Quantitative Ecology Drawing from case studies in landscape design and natural resource management, this course will apply quantitative methods to ecological data analysis. Students will work with the software program R to apply statistical inference and mathematical modeling using previously collected data sets on single species, species interactions, communities, and food webs.
3 
+Minor Requirements

18 credits
BUS171 Information Systems and Operations This course explores basic concepts of communication networks (e.g., the Internet), hardware, software, databases, and systems. Students apply information systems to decision making, communication, collaboration and coordination in the operations of contemporary organizations. Students gain skills in word processing, presentation software, data visualization, spreadsheets, and relational databases.
3 CMP120 Introduction to Programming An introduction to the theory and practice of computer programming with an emphasis on problem solving. No previous programming experience is required.
3 CMP202 Introduction to Programming An introduction to programming using C++ for students with no previous computer programming experience. Includes introduction to algorithms and objectoriented programming techniques.
3 CMP283 Database Management Systems This course is a study of database management systems and their applications to a wide range of information processing needs. Students design and implement database management systems while being introduced to a conceptual model of a database environment comprised of five basic components: databases, database management systems, data dictionary/directory systems, database administration, and usersystem interfaces.
3 DSA150 Introduction to Data Science Data Science is the study of the tools and process used to extract knowledge from data. This course introduces students to this important, interdisciplinary field with applications in business, communications, healthcare, etc. Students learn the basics of data organization, packaging, and delivery. Simple algorithms and data mining techniques are introduced.
3 DSA400W Data Visualization and Communication Cover the different ways of visualizing data, given different types and characteristics of data. Includes assessment and evaluation of existing data visualization techniques. Current tools used transform data and visualize data are reviewed, including Python, Google Charts, and/or Tableau.
3 MTH110 Elementary Statistics Topics include statistical measures and distributions, decision making under uncertainty, application of probability to statistical inference, linear correlation, introduction to nonparametric statistical methods, and application to problems drawn from the natural and social sciences. Three hours of class per week. Three hours of class per week.
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