International Business School Suzhou (IBSS) at Xi’an Jiaotong-Liverpool University & Research Institute of Big Data Analytics (RIBDA) is organising a Big Data Analytics Training Course (April 14-16). This course aims at providing more practical skills to our students with special interests in Big Data and Business Analytics. The major attendees include IMIS year 3 & 4 students, MSc Business Analytics students and staff who are interested in the topics.
Professor Vince Tsou
Chief director of Chinese Academy of R Software (Taiwan)
Ching-Shih Tsou is currently a professor of the Institute of Information and Decision Sciences at the National Taipei University of Business (NTUB) and the director of the Centre of Data Science Applications at NTUB. He has served as the chair of board of directors at the Chinese Academy of R Software (CARS) and the Data Science and Business Applications Association of Taiwan (DSBA).
Professor Tsou recently concentrates on the data analytics applied to practical problems. He has been served as a data analytic consultant for the National Palace Museum, Institute for Information Industry, Industrial Technology Research Institute, Central Weather Bureau (MOTC), Institute of Transportation (MOTC), Sinotech Consultants, SinoPac Bank, Chunghwa Telecom, Central Police University, Deloitte, Shin Kong Life Insurance, Taipei Computer Association (TCA), Directorate-General of Budget, Accounting and Statistics (Executive Yuan), Fiscal Information Agency (MOF), Taiwan Academy of Banking and Finance (TABF), and many universities in Taiwan. Prof. Tsou is one of the pioneers of doing the data science by open source tools in Taiwan.
About the course
R basics (one day, six hours)
|R studio installation and introduction (optional)||NA|
|R packages and functional programming||30 minutes|
|Environment and online help||30 minutes|
|Data importing, storage, and loading||1 hour|
|Data objects and manipulation||1 hour|
|R data-driven programming skills||1 hour|
|Data integration, reshaping, cleaning, and transformation||1 hour|
|Grouping and summarisation||1 hour|
Big data and data analytics hands-on (two days, twelve hours)
|Data mining tasks And critical issues||1 hour|
|Market index construction and principal component analysis||1 hour 30 minutes|
|Market segmentation and K-means clustering||1 hour 30 minutes|
|Online music and market basket analysis||2 hours|
|Stock automatic trading prototype system and neural networks, support vector machine, multivariate adaptive regression splines||2 hours|
|Predicting cytogenetic abnormalities by classifying microarray data (including feature selection, random forest, k-nearest neighbours)||2 hours|
|Meta-learning by bagging and boosting||2 hours|