Big Data Executive Program
According to Gartner, 85% of Fortune 500 organizations will not be capable of capitalizing on big data to seek competitive advantage by 2015. Most of the optimism surrounding big data has so far revolved around the technologies but it is obvious from Gartner’s findings that organizations need talents who possess the right skills and mindsets to fully exploit the power of big data.
The Big Data Executive Program is designed to address this by training executives to become technically savvy data scientists who can not only manage big data, but also create value from it. Choose from three courses - Business Visualization for Big Data, Business Data Management for Big Data and Data Science Using Big Data.
Whether your aim is to determine the root cause of failures, to detect fraud or to simply deduce what is important and what isn’t, these programs feature real-life industry case studies which help participants to solve problems using SAS software.
"Customers frequently praised SAS for its training programs." – Gartner, February 2015
Special offer: To complement your learning, the first 30 people who register for any of the Big Data Executive Programs will
receive an iPad mini 3 or Samsung Galaxy Tab 3 V. *Terms and conditions apply
Featured Executive Programs
Business Visualization for Big Data (BV)
SAS Visual Analytics helps people see things that were not obvious to them before. Even when data volumes are very large, patterns can be spotted quickly and easily. Visualizations convey information in a universal manner and make it simple to share ideas with others. It lets people ask others, “Do you see what I see?” And it can even answer questions like “What would happen if we made an adjustment to that area?”
What you get: Aspirants who want to become Business Data Visualization analysts/experts and build a strong SAS programming foundation to manipulate data, perform queries and analyses, and generate reports. And those who want to access the power of SAS for analysis and report generation on big data using the point-and-click interface.
Who should attend: Business, Data, and Statistical Analysts
Prerequisites: No particular prerequisites. Flair of any programming language is preferred but not necessary.
Business Data Management for Big Data (BDM)
SAS Big Data Management is the organization, administration and governance of large volumes of data. Corporations, government agencies and other organizations employ big data management strategies to help them contend with fast-growing pools of data. SAS enables faster management of the data processes, enabling faster delivery of analytics goals.
Duration: 80 hours/10 days
What you get: Aspirants who want to become Big Data Manager and build skills to build a data warehouse and/or data mart for business intelligence and big data analytics applications.
Who should attend: Business Analysts, DI Developers
Prerequisites: Flair of any programming language is preferred but not necessary. Candidates with experience in Data Management preferred.
Data Science Using Big Data (DS)
SAS Business Analytics along with Visual Analytics brings in the capability to support the business and management with clear and insightful analyses on the data at hand. This includes data mining skills, advanced modelling techniques, business visualizations testing and creating and explaining results in clear and concise reports.
Duration: 96 hours/12 days
What you get: Aspirants who want to become Business analysts/experts, Business Visualization Analysts or experts and build a strong SAS programming foundation to manipulate data, perform queries and analyses, and generate analytical insights. And those who want to access the power of SAS for analysis of complex business problems through data mining and predictive modelling using the point-and-click interface.
Who should attend: Business, Data, and Statistical Analysts
Prerequisites: Flair of any programming language is preferred but not necessary. Candidates with Engineering/MBA/MCA/Science/Statistics degree or Analytics experience preferred.