The graduate certificate in Data Analytics consists of 4 courses (12 credit hours), and is a professionally focused offering that will provide a strong foundation in the field of Data Science and Analytics, and is open to students with backgrounds outside of computer science. The certificate is ideally suited to support the needs of students seeking analytics-enabled types of jobs, where a broad understanding of the subject matter is needed.

The graduate certificate will require the following four graduate-level courses. Completion of the first two courses is required before proceeding to the second two.

  • Introduction to Data Science and Python (3) A hands-on introduction to the field of Data Science and its applications.  Covers a wide range of topics to provide an overview of the use of data in different fields.  Provides hands-on practice with basic tools and methods of data analysis.  Prepares students to use data in their field of study and in their work and to effectively communicate quantitative findings. Focus is on the use of Python in data analysis and mastering tools for acquiring, parsing, manipulating, and preparing data for statistical analysis.
  • Applied Statistics and Data Analysis (3)  Introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. Covers techniques in modern data analysis: regression and econometrics, prediction, design of experiment, randomized control trials (and A/B testing), and data visualization. 
  • Introduction to Machine Learning (3) Prerequisite: Applied Statistics and Data Analysis. Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and Bayesian networks. 
  • Applications of Data Analytics and Development (3) Prerequisite Applied Statistics and Data Analysis. This course develops an overview of the challenges of developing and applying analytics for insight and decision making.  Examples and cases will come from engineering, social media analytics, business analysis and other data-centered domains.   The focus will be on programming and data manipulation techniques for constructing analytics-based applications.  Topics include SQL or no-SQL databases, using web service API’s to acquire data, introduction to Hadoop and MapReduce, and use of third-party analytic component API’s.