Upper Division Certificate in Data Analytics

About the Program

The field of Data Analytics (DA) focuses on problem and data (big or small) understanding, identifying data, preparing data, analyzing data, interpreting results, and building models for solving problems and for decision-making purposes using methods, approaches, technologies, and tools. With advances in the use of data and information technology in all sectors, competencies in data and analytics are increasingly becoming essential in all disciplines. The proposed Upper Division Certificate Program will focus on teaching students about data and its management as well as DA applications for problem-solving and decision-making using information technology. This Upper Division Certificate program is open and benefits all undergraduate students in any major. To list a few of the majors that highly benefits include Finance, Economics, Marketing, Accounting, Information Systems, Mathematics, Statistics, Biology, Chemistry, Psychology, Sociology, Political Science, Education, History and Governments, and Criminal Justice.  

The UDC-DA was modeled on the National Consortium on Data Science three dimensions: data flow, data curative, and data analytics (Ahalt, 2012).  The UDC-DA goals are: 

• Produce graduates with knowledge and skills of data science and analytics’ methods, models, process, technologies, and tools. 

• Produce graduates who will be able to address local, national and global problems using big data and analytics.  

• Produce graduates who can effectively tell stories from the results of analytics using oral, written. and visual forms of communication.  

The expected learning outcomes for the UDC-DA are:

• Able to explain big data and its applications. 

• Able to explain data science and analytics methods, models, and processes. 

• Able to apply critical thinking skills for problem identification, problem solving and decision-making using data and analytics.   

• Able to apply data curation and management principles to prepare data resources for analytics. 

• Able to develop model-based solutions using datasets and statistical and machine learning techniques. 

• Able to prepare and effectively communicate outcomes of analytics visually, orally and in writing.