This course introduces advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. Students are introduced to descriptive, predictive, prescriptive analytics, and optimization models. The course utilizes Microsoft Excel to engineer and analyze business models. Students identify the proper use of complete regression, optimization, and exponential smoothing models.

Upon completion of this course, students are expected to be able to do the following:

• Utilize datasets to develop statistics and probability to predict future outcomes
• Implement appropriate models needed to analyze and critically evaluate business objectives
• Develop written and oral communication skills required to report on data-intensive business situations
• Organize data-intensive content in a professional setting
• Execute advanced analytics techniques

The course utilizes the data processing requirements necessary to implement technology-based analytics. The course explores the strengths and limitations of various data formats to make better decisions. The importance of structured and unstructured data formats and performing methods of data extraction, transformation, and loading are covered. Data wrangling methodologies explore constructing custom data pipelines to support efficient analysis. These methods include cleaning, filtering, standardizing, and categorizing data. Processes to review data for accuracy, consistency, and completeness are covered as well as techniques to mitigate error and improve data integrity. The course also investigates the legal and ethical considerations of data management.

Upon completion of this course, students are expected to be able to do the following:

• Perform extract, transform, and load (ETL) processes using structured and unstructured data formats
• Assess data for error and implement techniques to improve data integrity
• Determine appropriate data formats for given situations
• Design and document processes for converting raw data into a product suitable for analysis
• Identify legal and ethical issues related to the processing and dissemination of data

This course focuses on the framework for utilizing the Python programming language for artificial intelligence applications. The course explores performing, creating, and scripting. Students gain experience in programming Python scripts and data science applications.

Upon completion of this course, students are expected to be able to do the following:

• Write high-quality, maintainable Python programs for deep learning
• Import Python libraries for artificial intelligence development
• Articulate and demonstrate the value and concepts of Python programming language for analytical modeling
• Apply mathematical operations and code aligned with standard machine learning techniques
• Finally, evaluate common approaches to artificial intelligence application development

Note: Students may choose either BIA 645 or BIA 681.

This course focuses on explaining complex datasets, models, and analysis to a variety of stakeholders including internal and external organizations and personnel of various disciplines. Audience analysis, and effective strategic communication are studied. Students identify and analyze problems. Professionalism in both oral and written communication is expected.

Upon completion of this course, students are expected to be able to do the following:

• Conduct stakeholder analysis and communications results
• Identify business problems and create analytical approaches to solve them
• Write reports based on best-practice data analysis frameworks
• Demonstrate best practice communication techniques to visualize, explore, and act on data science findings

Note: Students may choose either BIA 645 or BIA 681.

This course introduces students to the complex nature of deep learning. First, by studying supervised and unsupervised models, students explore the benefits deep learning offers businesses. Then, using Python, students build a neural network model using the Keras framework and interpret the results for stakeholders.

Upon completion of this course, students are expected to be able to do the following:

• Communicate a conceptual understanding of why neural networks are gaining popularity
• Compare neural networks with other data structures and the situations in which neural networks should be used over other structures
• Visualize and interpret the components of a shallow neural network
• Construct a basic deep neural network model using Python and the Keras library
• Explain profound neural network model results for stakeholders

• An undergraduate degree from a regionally accredited institution
• A minimum GPA of 2.75 on a 4.0 scale
• A written statement: Your background and future goals all impact your success with Saint Mary’s and inform your education. As you prepare your written statement, think about including previous experiences, education, and your strengths and interests. Share your motivations for entering the online human resource management certificate program and how it will contribute to your career

No prerequisite coursework or experience is required for this program.

You don’t need GRE/GMAT scores to apply for this program.

Transfer credits are not accepted for this program.

Students with transcripts from universities outside the United States, the United Kingdom, Ireland, New Zealand, Australia, and Canada (except for the Quebec Province) must take and pass English proficiency testing

• Test of English as a Foreign Language (TOEFL) with a minimum score of 72
• International English Language Testing System (IELTS) with a minimum score of 6.0
• Michigan Language Assessment Battery (MELAB) with a minimum score of 74%
• International Test of English Proficiency (ITEP) with a minimum score of 4.0 or Pearson Test of English with a score of 51
• Duolingo with a minimum score of 100

Whether you apply through our online application via hard copy, or in-person with a Saint Mary’s admissions counselor, your application must include the following items:

Official transcripts: An official transcript issued to Saint Mary’s University of Minnesota from the institution posting the applicant’s completed degree and other relevant transcripts documenting program prerequisites and potential transfer credits must be sent separately and directly from your institution. Saint Mary’s University of Minnesota accepts official transcripts electronically from previously attended institutions. Transcripts should be sent to oladmissions@smumn.edu or mailed to:

Transcript Processing Center
Saint Mary’s University of Minnesota
851 Trafalgar Court, Suite 420
Maitland, FL 32751

An up-to-date résumé: Include both your educational and professional experience.

Two references: Provide the names of two people (including title, organization, email, and phone number) who can serve as professional references, if needed, during the application review process. The best references are those who know you professionally, such as current or former supervisors.

Upcoming application deadlines for Saint Mary’s online Artificial Intelligence certificate:

• Spring: December 18, 2023
• Summer: April 22, 2024
• Fall: August 20, 2024

Upcoming start dates for Saint Mary’s online Artificial Intelligence certificate:

• Spring: January 8, 2024
• Summer: May 6, 2024
• Fall: September 3, 2024

Once you’ve submitted your application, expect to be notified about acceptance approximately two weeks after your submission date.