Strategies and Concepts for Data Scientists and Business Analysts
Seria Wiedza Biznesowa course
Autorzy szkolenia Catherine Truxillo, Ph.D., Director, Analytical Education, Education Division, SAS; or Jeffrey Thompson, Ph.D., Analytical Training Consultant, Education Division, SAS; or Peter Christie, Analytical Training Consultant, Education Division, SAS
Note: This course does not provide comprehensive training on software products, but you do use SAS Visual Statistics, SAS Enterprise Miner, and SAS Text Miner software to complete class assignments. Prior knowledge of these software products is not required.
To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.
In this course, you gain the skills that data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data.
This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience.Naucz się
Kto powinien uczestniczyćStatisticians, market researchers, information technology professionals, data scientists, and business analysts who want to make better use of their data
Before attending this course, you should have taken a college-level course in statistics, covering distribution analysis, hypothesis testing, and regression techniques or have equivalent knowledge. You can gain this knowledge from the Statystyka 1: wprowadzenie do analizy wariancji, regresji i regresji logistycznej course.
To szkolenie wykorzystuje oprogramowanie SAS Enterprise Miner, SAS Text Miner, SAS Visual Statistics