Bachelor of Science inFunctional Data Analytics

Introduction to speech communication: formal speaking, group discussion, analysis and evaluation of messages.

Introduces students to foundational concepts that are used in the data analytics industry to understand and frame projects

Introduces students to the core areas of descriptive analytics, where the main objective is to answer "what happened" in a project. Students will learn how to accurately describe the attributes of data variables available to them for their projects. Students will have the opportunity to work on real-life projects and cases throughout the course, learning the importance of communicating findings to different constituents.

This course takes descriptive data a step further by examining the relationships between variables through root cause analysis. Students will learn current tools and techniques that allow analysts to explain why the patterns are observed in data and discover relationships between two or more attributes of the data.

Helps students learn to predict what will happen in future events based on historical data. Students will learn how to properly examine problem contexts to use the most appropriate method to develop the best predictive model as well as how to evaluate their results and interpret findings to users at different levels of an organization.

Cultivates awareness of ethical, privacy, and security rules and standards to guide how to use and analyze data and communicate the findings. The course will emphasize ethics at various stages of data analytics, including the communication of the implications of data analysis to stakeholders, how data is collected and stored, as well as how it is analyzed and presented to different stakeholders.

Introduces students to how data analytics assists in making decisions and advocating for a course of action. Helps students learn to develop a set of viable decision options, rank those decision options to create alternative courses of action, and achieve an optimal decision.

Offers an overview of data analytics techniques for non-numeric data (e.g., text, videos, or images). Students will explore methodological reasons for a qualitative rather than a quantitative approach, and the underlying challenges with this type of data, depending on the hypothesis or research question. A strong background in statistics is required for this course.

This course allows students to demonstrate their ability to use industry or organization data to develop a descriptive, diagnostic, predictive, and prescriptive analysis. This portion of the capstone addresses the understanding of the industry data, project scope, data journalism, and descriptive analysis.

This course continues the industry project (assigned in DA 475) and the remaining three-part analysis of organization-specific analysis (diagnostic, predictive, and prescriptive).

Presents techniques and software used for data visualization. Students will be introduced to complex data sets and learn how to present findings in interactive and innovative ways.

Students will develop an understanding of the systems, processes, tools, and implications of this field.

Writing for students in scientific and technical disciplines.

Introduction to formal languages, mathematical logic, and discrete mathematics, with applications to information sciences and technology.

Covers the technical and people skills related to project management within a variety of organizational structures. Students learn the tools and concepts needed to successfully balance schedule, budget, and scope while managing risk and resources during the project life cycle. Leadership and teamwork skills are emphasized and practiced in a hands-on approach.

In-depth analysis of social media management, digital marketing, SEO/M, and analytics of current digital business practices. Presents an analysis of current issues in digital marketing and the tools needed to assess those issues.

Using case studies in various business domains, exercises, and projects, this course presents practical dimensional modeling techniques, extract/transformation/load (ETL) logic, ETL design considerations, and report generation. Key concepts related to data warehousing, including dimensional table characteristics, fact table characteristics and granularity, types of dimensions, types of fact tables, dimension attribute hierarchies, consolidated fact tables, slowly changing dimension techniques, and multivalued dimensions and weighting factors are covered in the course.

This course provides an introduction to the basic questions of ethics, the major currents in traditional ethical theory (virtue ethics, deontology, consequentialism), and more recent developments (e.g., care ethics).

This course will introduce students to data computing fundamentals and a reproducible workflow using the R programming language and related tools. R is a powerful, open-source programming language used widely for applications in statistics and data science. Students will be expected to access, join, wrangle, clean, and visualize real data from various sources (e.g., CSV, HTML scraping, web URL, R packages).

Introduction to programming techniques design and implementation of algorithms. Structured programming. Problem-solving techniques. Introduction to a high-level language, including arrays, procedures, and recursion

Prepares students both to understand the communications that surrounds them and to succeed in their own communication efforts. The course focuses specifically on analyzing verbal and visual texts (reading) as well as on producing such texts (writing), always in terms of rhetorical principles.

Introduction to the use of analytics in business contexts, including data-driven decision-making and performance optimization in areas such as finance, management information systems, marketing, international business, accounting, and economics.

Introduction to the application of analytics in engineering, with a focus on system optimization, process improvement, and predictive modeling in engineering problems.

Introduction to the application of analytics in humanities and social sciences, including text analysis, thematic and content analysis, statistical modeling, regression analysis, media analytics, program evaluation, trend analysis, text mining, demography, ethnography, simulation, and the interpretation of cultural data in fields such as education, psychology, sociology, political science, communication, cultural studies, history, and language arts.

Introduction to the use of analytics in natural sciences, covering topics like experimental data analysis, statistical modeling, and simulation in scientific research.

Nature of finance function; risk and return concepts; working capital; dividend policies; mergers; security markets; acquisition and management of corporate capital; analysis of operations; forecasting capital requirements; raising capital; and planning profits. Available to baccalaureate students only.

The course focuses primarily on the development of spreadsheet applications and the use of the case method to apply decision-making procedures to real-world problems in finance areas.

Investment and risk, types of security investments, sources of investment information, the broker, the stock market, portfolio management.

6–10 case studies demonstrating types of studies in financial analytics

Introduction to general psychology; principles of human behavior and their applications.

Introduction to methods of psychological research, with special attention to hypothesis formation and testing, threats to validity, and data presentation.

Identify and understand the normative assumptions specific to organizations about their basic program design, implementation, and assessment processes.

6–10 case studies demonstrating types of studies in program evaluation