Corporations have dramatically increased investments in their "digital enterprise" in the past few years. It has been estimated that by 2020, IT departments will be monitoring 50 times more data than they are today. This tidal wave of data is driving unprecedented demand for those with the skills required to manage and leverage these very large data sets into a competitive advantage.
Curriculum is designed to help meet the expanding needs for data scientists who are skilled in the utilization of a unique blend of science, art and business. These professionals understand how to automate methods of collecting and analyzing data and utilize techniques to discover previously hidden insights that can profoundly impact the success of any business.
Understand the skills needed to effectively collect and manage big data, perform data-driven discovery and prediction, and extract value and competitive intelligence for your organization.
This program provides the skills required to become a data scientist and provides existing data analysts with opportunities to broaden skills.
Learn topics such as: utilizing concepts in on- and off-cloud; scalable data engineering (inspecting, cleaning, transforming, and modeling data), unstructured data and NoSQL; computational statistics; pattern recognition; data mining /predictive analytics; machine learning; data visualization; and high performance software and hardware.
Who Should Enroll
This program is intended for professionals in a variety of industries and job functions who are looking to help their organization understand and leverage the massive amounts of diverse data they collect. Others who would benefit from this program include: data engineers, data analysts, computer scientists, business analysts, database administrators, researchers, and statisticians.
- Learn from industry experts how to utilize a combination of science, art & business techniques to deliver new insights and competitive intelligence
- Describe the phases of the analytics lifecycle
- Utilize a variety of statistical and computer science tools and techniques to analyze data
- Describe and use the typical tools and technologies required to model and analyze large (big) datasets
- Explain the use of typical tools to explore data (R, STATISTICA, Hadoop, etc.)
- Utilize an inquisitive "hacker" mentality to uncover new meaning from existing data
- Effectively design, model and manage databases
- Describe and utilize unstructured and structured data sets leveraging text analytics tools.
- Define requirements, develop an architecture, and implement a data warehouse plan
On-site Training Available
Through Corporate Training, we can deliver this program or customize one that fits
your organization's specific needs. Visit Corporate Training
or call (949) 824-1847 for information.
Stay Informed About
Presented in partnership with:
Predictive Analytics World is the business-focused event for predictive
analytics professionals, managers and commercial practitioners.
Data Science: Information SessionFree Event: Thursday, March 26, 2015RSVP
Data Science Information Session
R Programming: Information Session
Certificate Eligibility and Requirements
A certificate is awarded upon completion of 15 credit units (6 required and 9 elective credit units) with a grade of “C” or higher in each course.
Students interested in pursuing both the Data Science and Predictive Analytics programs may use two courses from either programs to satisfy elective requirements.
To become an official candidate in the program, students pursuing the certificate must submit a Declaration of Candidacy. Students are encouraged to declare candidacy as soon as possible, but no later than after the third course in the program. To receive the certificate after completing all program requirements, students must submit a Request for Certificate. All requirements must be completed within five (5) years after the student enrolls in his/her first course. Students not pursuing a certificate are welcome to take as many individual courses as they wish.
- Dean Abbott, President, Abbott Analytics
- John Elder, Ph.D., Chief Scientist, Elder Research
- Bernie Jeltema, Principal Consultant, Strategic Frameworks, Inc.
- Derrick Lam, Director, Advancement Information Management
Department, Office of Information Technology, University of California, Irvine
- Amit Manghani, Director and Solution Manager, Business Analytics & Technology, SAP AG Business Objects Division
- Gary Miner, Ph.D., Senior Consultant/Statistician, Statsoft
- Bob Nisbet, Ph.D., Consulting Data Scientist
- Eric Siegel, Ph.D., Chair, Predictive Analytics World
- Jim Sterne, President, Target Marketing, Chairman, Digital Analytics Association, Founder, eMetrics Marketing Optimization Summit
- James Taylor, CEO, Decision Management Solutions