Graduate Certificate in Data Analytics for Information Professionals
IBM Marketing Cloud reported that 90% of the data today has been created in the last two years. Gartner estimated that 80% of organization data today is unstructured (big data, for example data from EMR, Facebook, Twitter, etc.). As such, big data analytic skills are in high demand.
Who Can Pursue This Certificate
This certificate program is designed to prepare students, who do not have a computer science background, to apply analytical tools to solve organizational problems. The most popular free data analytics tools, R and Python, are mainly used in the certificate courses.
Students in the MLIS program can complete the MLIS and the certificate concurrently and the courses will count towards both the degree and the certificate.
Students in other OU graduate programs can concurrently complete the data analytics certificate and, depending upon the rules of their graduate program and their advisor's permission, should be able to apply eligible hours to both the graduate certificate and the degree.
People who are not currently students at OU, but who have completed a bachelor's or higher degree (with a G.P.A. of 3.0 or higher), can pursue the graduate certificate.
Students will complete 4 courses (12 credit hours). Two courses are required and two courses will be selected from a list of electives.
Introduction to Data Analytics (3 credit hours)
This course covers various big data analytical tools and concepts. Topics covered include outlier analysis, cluster analysis, association rules, regression analysis, and decision trees.
Advanced Data Analytics (Data Mining for Information Professionals, 3 credit hours)
This course covers sensitivity analysis, advanced predictive modeling, random forest, logic analysis, and neural networks. Students will learn to identify the best analytical tool for a specific issue and manipulate a decision model suitable for a specific issue.
Elective Courses (choose 2)
Fundamentals of Information Technology (Information and Communication Technology, 3 credit hours)
This course covers widely used cutting-edge organizational technologies, so students can be ready for organizing, processing, analyzing, and presenting data and information. Topics include Excel, ERD, database management, big data analytics using R, and website design using HTML and cascading style sheets (CSS). This course is reviewed and revised every year.
Introduction to Information Visualization (3 credit hours)
This course covers information visualization as explanation and as art, as well as for purposes for making decisions and discoveries through data. Students will learn to select appropriate representations based on data frameworks and audience, build skills in planning, developing, and evaluating information visualizations, and experiment using various datasets with freely accessible visualization programs such as Tableau Public.
Information Retrieval and Text Mining (3 credit hours)
This course covers various sources of textual information in society. The course includes different data collection methods, text analytic processes, textual information retrieval models, and different text analytic methods used to analyze textual information and interpret text analysis results for users' information needs.
Database Design for Information Organizations (3 credit hours)
This course covers relational database design and management, which is also foundational for understaning big data. The concepts covered in this class are data types, advanced ERD and one-to-many relationships, and the hands-on exercises include creation and design of tables, queries, forms, and reports.