Data Analytics Certificate

Data analysis is an integral component in modern business decision-making processes. This certificate program offers students training in essential skills in data analytics. 


  • Manipulate and prepare a large data set for analysis through common techniques to clean data, and identify trends and outliers.
  • Manage a large data set through database management, and build an effective database application
  • Describe and apply the common techniques used in data analytics and choose an appropriate technique to model and make predications on a dataset.


DATX 5801: Data Management

Credit Hours: 3

This course covers the basic concepts of database systems and emphasizes the real-world database applications relevant to the management of data in an organization environment. The topics include (not limited to) database environment, database development, relational database management systems, SQL/NoSQL data management language, data normalization, data warehousing, and internet database environment. Credit will not be given for both DATX 5801 and CSIS 3722.

DATX 5803: Data Visualization

Credit Hours: 3

Data visualization refers to the graphical representation of information revealed through data analysis. With the assistance of various visualization elements, we can present data in a clear and effective manner. More importantly, turning data into impactful images, we are able to gain valuable insights and intelligence that help improve our decision-making processes. This course introduces students to various types of visualization techniques like charts, tables, graphs, maps, infographics and dashboards. It emphasizes applying appropriate visualization techniques in uncovering information from data. Moreover, it will help students develop skills of data storytelling, i.e. effectively communicating actionable insights through the combination of data visualization and narratives.

DATX 5805: Predictive Modeling Algorithms

Credit Hours: 3

Predictive modeling (also referred to predictive analytics and machine learning) applies statistical techniques in analyzing data to predict outcomes. Through a hands-on approach, this course helps students develop basic skills in predictive analytics. Topics may include (not limited to) k-nearest neighbors, naïve-Bayes, linear and logistic regression models, time-series models, classification and regression trees, Principle Component/Factor Analysis, non-linear models, neural networks, random forests, and cluster analysis among others.


Undergraduate Certificate requires a Junior Standing, and a GPA of 2.5 or higher. 

Graduate Certificate requires an Undergraduate Degree with a GPA of 2.5 or higher. 

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