Preparing Statistician/Statistics Graduates to Be Data Scientists (ADDED FEE) — Professional Development Continuing Education Course
ASA , Quality and Productivity Section , Section on Physical and Engineering Sciences
With the recent big data revolution, enterprises ranging from FORTUNE 500 to startup companies across the US are hungry for Data Scientist to bring valuable insight from data collected. Statistics graduate students are great data scientist candidates, but there are relatively few data scientist with statistics education background. In this CE course, we will go through the needed data science knowledge and skills to prepare statistician to be excellent data scientist. The cloud-based computation environment will be used in this course with plenty of hands-on exercises. We will use case studies to cover how to leverage big data distributed platform (Hadoop / Hive / Spark), data wrangling, modeling, dynamic report (R markdown) and interactive dashboard (R-Shiny) to tackle real-world data science problems. Data ETL in production environments is one typical gap for statistician and it will be covered. Data science is a combination of science and art with data as the foundation. We will also cover the "art" part to guide participant to learn soft skills to define data science problems and to effectively communicate with business stakeholders. The prerequisite knowledge is MS level education in statistics and entry level of R-Studio.
Instructor(s): Ming Li, Amazon and TAMU - Commerce, Hui Lin, DuPont Pioneer
