http://www.tri-c.edu/programs/information-technology/professional-development/data-analytics/index.html

ZDTM-1007 Data Analytics for Business Transformation

0.8 CEU’s

Businesses are increasingly looking to take advantage of data analytics to be competitive. In addition to data scientists, organizations need data-savvy business leaders who can identify opportunities to solve business problems using advanced analytics and business intelligence to lead an analytical team. This course gives business leaders the skills and knowledge to better communicate, implement and manage analytical efforts for their business. It describes how to get started and what is required to effectively run projects which leverage data analytics.

Contact hours: 8

Not financial aid eligible.

ZDTM-1011 Data Science and Big Data Analytics

3.6 CEU’s

This course provides practical, foundation-level training that enables immediate and effective participation in big data and other analytics projects. It includes an introduction to big data and the Data Analytics Lifecycle to address business challenges that leverage big data. The course provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. Labs offer opportunities for students to learn how these methods and tools can be applied to real-world business challenges from a practicing data scientist. The course takes an "open," or technology-neutral approach, and includes a final lab that addresses a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle. The course prepares students for the Proven™ Professional Data Scientist Associate (EMCDSA) certification exam.

Contact hours: 36

Not financial aid eligible.

ZDTM-1012 Advanced Methods in Data Science and Big Data Analytics

3.6 CEU’s

This course builds on skills developed in the Data Science and Big Data Analytics course. The main focus areas are Hadoop (including pig, Hive and HBase), natural language processing, social network analysis, simulation, random forests, multinomial logistic regression and data virtualization. The course takes an "open," or technology-neutral, approach and utilizes several open-source tools to address big data challenges.  

Contact hours: 36

Not financial aid eligible.