Machine Learning Digital Technology Credential

Issued by Marymount University

Earners of this badge have demonstrated in depth knowledge, skills, and abilities in machine learning models, algorithms, mathematical and statistical machine learning, and data ethics. Earners have completed curriculum that aligns with the employer-validated, digital tech competencies required for jobs with Capital CoLAB employers.

Additional Details

Skills

Earning Criteria

DATA325 Data Analytics: The course introduces students to the field of data analytics. It starts with an understanding of data and the terminology used in its collection, storage, quality, validation, analysis and visualization. The students review probability as well as descriptive and inferential statistics and develop models to illustrate their application. The course includes a section on data ethics and the legal and regulatory compliance issues associated with data (e.g. medical data).
DATA370 Machine Learning Technologies: The course enables student to learn how machine learning works and when it can be used. Key terms are introduced including supervised and unsupervised learning. Different techniques are explored. Deep learning architectures are also described. Machine learning models and algorithms are applied and their performance evaluated. A model is trained and used to make predictions. Issues with machine learning are discussed including trustworthiness and bias.
DATA395 Data Visualization: The course examines the role of data visualization in communicating the results of data analysis. Students use a variety of tools such as MS Excel, Tableau, and MS PowerBI to create a variety of displays including dashboards, graphs, tables, maps, and infographics. Students create data visualizations for a wide set of audiences from data sets of various types.
DATA310 Decision Analysis: The course focuses on the use of data to reduce the risk and uncertainty in decision-making. It first reviews probability and statistics, including linear regression. Students conduct a series of analysis including summarization, estimation, prediction, and using pivot tables. Data security is also covered in terms of sensitivity and risk factors.
IT385 Managing Big Data: The course applies the knowledge garnered in IT310 to very large and complex data sets. It begins with a discussion of data governance, including change management, and maintaining quality throughout the data life cycle. On-premise and cloud methodologies are discussed. The course allows students to handle large data sets, throughout their lifecycle resulting in subsequent meaningful data analysis.