This set of courses was valid from 2022-2024. This set of courses will be retired as of Aug 1, 2024 replaced with the new specialization paths. I may try and obtain course info and branch. See new version of the MSDA can be found here
This data analytics degree program focuses on both theory and application, “learn by doing” as you complete data science and analytics projects in stages. The program covers working with medical and corporate datasets and an original capstone project. Coursework is completed programtically with Python, R, Postgres-SQL or with common tools such as Tableau/Power BI Equivalent
The Data Analytics Journey gives an overview of the entire analytics life cycle. Learners gain fluency in data analytics terminology, tools, and techniques. The course contextualizes the data analytics journey firmly with organizational metrics and requirements to position graduates to answer key questions for businesses and other employers. This course has no prerequisites.
Data Cleaning continues building proficiency in the data analytics life cycle with data preparation skills. This course addresses exploring, transforming, and imputing data as well as handling outliers. Learners write code to manipulate, structure, and clean data as well as to reduce features in data sets. The following courses are prerequisites: The Data Analytics Journey, and Data Acquisition.
Exploratory Data Analysis covers statistical principles supporting the data analytics life cycle. Students in this course compute and interpret measures of central tendency, correlations, and variation. The course introduces hypothesis testing, focusing on application for parametric tests, and addresses communication skills and tools to explain an analyst’s findings to others within an organization. Data Cleaning is a required prerequisite for this course.
Predictive Modeling builds on initial data preparation, cleaning, and analysis, enabling students to make assertions vital to organizational needs. In this course, students conduct logistic regression and multiple regression to model the phenomena revealed by data. The course covers normality, homoscedasticity, and significance, preparing students to communicate findings and the limitations of those findings accurately to organizational leaders. Exploratory Data Analysis is a prerequisite for this course.
Advanced Data Analytics prepares students for career-long growth in steadily advancing tools and techniques and provides emerging concepts in data analysis. This course hones the mental and theoretical flexibility that will be required of analysts in the coming decades while grounding their approach firmly in ethical and organizational-need-focused practice. Topics include machine learning, neural networks, randomness, and unconventional data sources. Data Mining II is a prerequisite for this course.
Representation and Reporting focuses on communicating observations and patterns to diverse stakeholders, a key aspect of the data analytics life cycle. This course helps students gain communication and storytelling skills. It also covers data visualizations, audio representations, and interactive dashboards. The prerequisite for this course is Data Mining I.
Data Mining I expands predictive modeling into nonlinear dimensions, enhancing the capabilities and effectiveness of the data analytics lifecycle. In this course, learners implement supervised models—specifically classification and prediction data mining models—to unearth relationships among variables that are not apparent with more surface-level techniques. The course provides frameworks for assessing models’ sensitivity and specificity. D208 Predictive Modeling is a prerequisite to this course.
Data Mining II adds vital tools to data analytics arsenal that incorporates unsupervised models. This course explains when, how, and why to use these tools to best meet organizational needs. The prerequisite for this course is Advanced Data Acquisition.
Data Acquisition builds proficiency in Structured Query Language (SQL) and the initial stages of the data analytics lifecycle. The course introduces relational databases. Students gain concrete skills in data transference and database manipulation. There are no prerequisites.
Advanced Data Acquisition enhances theoretical and SQL skills in furthering the data analytics life cycle. This course covers advanced SQL operations, aggregating data, and acquiring data from various sources in support of core organizational needs. The prerequisite for this course is Representation and Reporting.
The Data Analytics Graduate Capstone allows students to apply the academic and professional abilities developed as a graduate student. This capstone challenges students to integrate skills and knowledge from several program domains into one project. Advanced Data Analytics is a prerequisite for this course.