Using data-supported solutions for improving business performance
- Understand why “big data” is so important in today’s business decisions
- Join the rapidly growing analytics field
- Geared toward professionals from a variety of backgrounds, including anyone who deals with large amounts of data
- 15-week live-online course — September 14-December 16 — REGISTER NOW
- Discounts, payment plan, scholarships available
In today’s business world, data is easier than ever to collect and store. While the management of this big data is increasingly important to the decision-makers in the organization, big data is ever more difficult to analyze.
Analytics professionals or data scientists are invaluable to an organization’s success. They have the unique combination of computational, analytical and communication skills necessary to discover data-supported solutions to important business questions, from an ever-increasing wealth of data.
This program introduces students to the tools needed to analyze large datasets in order to make more informed business decisions. Students learn to gather and organize data for more effective analysis and how to communicate their analyses in a clear and concise manner.
- Who should participate in this program
- Course outline
- Learner outcomes
- Technology requirements
- What our students say
- For more information
- Business, marketing and operations managers
- Data analysts or professionals in any field who deal with large amounts of data
- Financial industry professionals
- Small business owners
Steven P. Bailey was with DuPont’s corporate Applied Statistics Group for over 36 years until his retirement as a principal consultant in 2016. During his last 16 years with DuPont, Bailey led DuPont’s corporate Six Sigma Master Black Belt Network. A past president and chairman of the board of the American Society for Quality (ASQ), he is certified as a Six Sigma Black Belt and Master Black Belt by both DuPont and ASQ.
Bailey, who served as an adjunct faculty member in UD’s Department of Mathematical Sciences, has been an instructor for the Analytics: Optimizing Big Data Certificate program since 2012. He also provides statistics and Six Sigma training and consulting services for a variety of businesses. He earned his B.S., M.S. and doctorate in statistics at the University of Wisconsin.
Aaron J. Owens retired in 2015 as a senior research fellow with the Decision Analytics Group at the DuPont Company. The group uses analysis of big data as well as quantifying uncertainties to aid in making important business decisions.
Owens holds a B.S. with highest honors in physics from Williams College (1969) and an M.S. (1971) and a doctorate (1973) in theoretical physics from Caltech. Following several years of teaching and astrophysics research at Lake Forest College, Kenyon College and the University of Delaware, he joined DuPont in 1980. Owens specialized in mathematical modeling of chemical systems, color modeling and supercomputing, and was the technology leader of applied statistics. He founded DuPont’s data mining efforts, developed the company’s proprietary neural network technology and adopted chemometric methods for process modeling. Currently, he does freelance consulting on data mining, chemometrics and applied statistics for the chemical and pharmaceutical industries.
- Importing data into an analytics software package
- Performing exploratory graphical and data analysis
- Building analytics models using tools such as multiple regression and decision trees
- Finding the best model to explain correlation among variables
- Learning how to control and assess data variability to better meet customer requirements
Grading policy — To earn the Analytics: Optimizing Big Data Certificate, students must complete each of the following modules with a grade of “C” or above:
- Analytics Basics
- Big Data Tools
- Process Control and Capability
- Individual Project
In this module, students will be introduced to the basics of analytics by learning key terms, concepts and knowledge areas and the use of SAS JMP analytics software. At the end of this module, students will be able to:
- Navigate JMP Software
- Input data from various spreadsheets and databases
- Perform graphical exploratory analysis
- Compare two or more groups
- Analyze paired data
- Use regression analysis to quantify the relationship between two variables
- Create and analyze a designed experiment
Big Data Tools
In this module, students will learn how to use big data tools to understand correlation among many different variables. At the end of this module, students will be able to analyze large datasets and build analytical models to predict future performance using the following multivariate tools:
- Neural Networks
- Partial Least Squares
- Principal Component Analysis
- Decision Trees
- Multiple Regression
- Cluster Analysis
- Discriminant Analysis
Process Control and Capability
In this module, students will learn how to evaluate if a process is stable and its ability to meet customer requirements. Students will also learn how to evaluate a measurement system’s performance and variability. At the end of this module, students will be able to use the following tools:
- Process Control Charts
- Process Capability Analysis
- Measurement Systems Analysis
As a final requirement of the certificate, students will apply the concepts and techniques learned throughout the course to a case study project. The project will incorporate the use of a current data problem the student would like to solve, which will be approved by the instructor. Guidance in completing project milestones is done throughout the program.
- A fully charged laptop (PC or Mac) is required to participate in this class.
- A copy of JMP® Pro statistical analysis software is included with this program, and the software is compatible with both Mac and PC.
While there are no formal requirements for admission to this program, please note the following recommendations:
- Statistics background—The course uses statistics throughout the curriculum. A basic understanding of statistics is required.
- Some prior college coursework is recommended, as the modules are taught at the baccalaureate level and are fairly rigorous.
- Prior experience with data management is helpful.
- Study and class preparation—Most students have said that for every hour in class they spent one to two hours outside of class studying and preparing for class.
- JMP® Pro statistical analysis software will be used in the classroom.
- “I didn’t have the confidence to go for my master’s in data science until I went through the classes, did the work and realized that I could indeed do this.” – Jamie Spencer
- “I am definitely implementing what I learned at UD into my work” – Sambhavi Parajuli
- “The ability to think conceptually about big data has been enormously helpful in transitioning to a new job that I was able to get during the course of the program.” – Patrick Caruso
- “This course has gone a long way in simplifying my workload and showing quick results to my internal customers.” – Amogh Prabhu
- “There is an explosion of data that can be found in every industry and government agency around the globe. The trick is to be able to take this data and turn it into information to drive better decisions.” – Joe Messick
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