91福利社

MS Data Science and AI

MS Data Science and AI

 

An intensive exploration of the intellectual and moral virtues in the context of cultivating leadership characterized by magnanimity and humility and built on the cardinal virtues (i.e., prudence, justice, self-control, and courage). Students develop an advanced capacity for self-awareness and managing oneself. Connections between ethical, authentic, servant, and transformational leadership styles and virtues are examined and applied to personal leadership   style and ethical decision making.

  • Gupta Core Course. Required for All Graduate Business Programs.

An examination of business as a creation of man and collective contributor to society  according to its responsibilities articulated by the tenets of Catholic social teaching including 
the common good, solidarity, subsidiarity, the dignity of the human person, worker, and work, 
and a preference for the poor. Emphasis is placed on how managers and their organizations  effect change for a more sustainable planet and just society.

  • Gupta Core Course. Required for All Graduate Business Programs.

The course introduces logic while giving students the opportunity to make arguments. The  course will cover persuasion through defining and explaining what rhetoric is. The course also 
addresses the practice of social influence; marketing politics, interpersonal relationships, and groups. The course will teach students how to communicate effectively in business situations.

  • Gupta Core Course. Required for All Graduate Business Programs.
This course introduces numeracy as a way for students to think competently about quantitative information. Students will learn how to take real world problems, interpret the information, and translate them into mathematical form. The course will address thinking critically, interpreting  deterministic and probabilistic information, statistical reasoning, and mathematical modeling.
This course teaches students the art of solving problems using code. It is open to students from any discipline, with or without programming experience. Topics include translating  problems from real-world applications to software, modeling, abstraction, complexity, and security. The course uses Python as its primary programming language. Additional programming languages related to web development and databases will be introduced toward the end of this course. The course will also cover software engineering principles, including software design patterns, testing, and documentation. This course is included in the 91福利社鈥檚 NSA National Centers of Academic Excellence designation as CAE-Cyber Defense (CAE-CD).

This course challenges students to learn advanced programming techniques by building on knowledge gained in prior programming courses while using AI-powered programming assistants. The course is organized around the process of learning to manipulate data residing in a variety of systems using AI pair-programming tools and techniques. Topics covered include understanding how to use AI-powered coding assistants to complete advanced data preparation, programming interactions with databases and using web-based application programming interfaces. The course adopts a problem-driven approach to learning by requiring students to learn by applying the targeted concepts to solve problems. This course is included in the 91福利社鈥檚 NSA National Centers of Academic Excellence designation as CAE- Cyber Defense (CAE-CD).

Prerequisite:

  • BANA 6360. Programming I.

This course introduces numeracy to teach students to think competently about quantitative information. Students will learn how to take real-world problems, interpret the information, and translate them into mathematical form. The course will address thinking critically, interpreting deterministic and probabilistic information, statistical reasoning, and mathematical modeling.

This course addresses concepts, tools, and techniques for using large datasets to address business problems. This includes understanding big data concepts, common architectures, and using industry-standard tools to store, query, transform and analyze large datasets. Techniques for importing and working with diverse data types across different technical environments are discussed and practiced.

Prerequisite:

  • BANA 6380. SQL Programming and ML.
  • BANA 6360. Programming I.
  • BUAD 6310. Quantitative Reasoning for Decision Making.
  • Completion of the Core Courses

This course provides hands-on experience in data visualization. Students will learn to analyze the context of data visualization, to identify, access and prepare data for visualization, to apply best practices in visual analytics, to design user-oriented visualizations based on essential cognitive and perceptual principles, and to create dashboard and data stories that effectively communicate data insights to facilitate managerial decision making. Students will complete data visualization assignments as well as a final project featuring an interactive dashboard and data story.

Prerequisites:

  • BUAD 6310. Quantitative Reasoning for Decision Making. OR, 
  • ACCT 5351. Data Analytics in Accounting. OR,
    FINA 6305. Managerial Finance.

This course addresses tools and techniques required for analyzing business data for forecasting. Topics include time series analysis and time series forecasting. Students will learn to apply these techniques to support business decision makers.

Prerequisites:

  • BUAD 6310. Quantitative Reasoning for Decision Making.
  • BANA 6360. Programming I.

The course addresses practices related to predictive modeling (decision tree, regression, neural network, ensemble and boosting models, among others). Topics include modifying data for better analysis results, model training and testing, machine learning methods, comparing and explaining complex models, generating predictions, and communicating results to help make better business decisions.

Prerequisite:

  • BANA 7350. Forecasting Methods.
  • BANA 6360. Programming I.
  • BUAD 6310. Quantitative Reasoning for Decision Making.
  • Completion of the Core Courses.

This course addresses tools and techniques required for using spreadsheets to analyze quantitative business problems. Topics include data cleaning, data preparation, and advanced spreadsheet tools for problem analysis. This course assumes intermediate spreadsheet knowledge and knowledge of business terms and functions.

Prerequisite:

  • BUAD 6310. Quantitative Reasoning for Decision Making. OR,
  • ACCT 5351. Data Analytics in Accounting. OR,
  • FINA 6305. Managerial Finance.

This course emphasizes the relational database structure and the use of relational databases for retrieving and reporting information to support business decisions. It covers Structured Query Language (SQL) extensively. Applications of relational databases in many areas of business will be discussed. Topics include relationship database concepts, the relational data model, entity relationship modeling, and introductory, intermediate and advanced SQL queries. Advanced topics include using SQL to implement machine learning and analyze geospatial data.

Prerequisite:

  • BANA 6360. Programming I.
  • BUAD 6310. Quantitative Reasoning for Decision Making.