Big Data Analytics Training Course (TR-002)

Events

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.

Speaker

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)

OutlineDuration
R studio installation and introduction (optional)NA
R packages and functional programming30 minutes
Environment and online help30 minutes
Data importing, storage, and loading1 hour
Data objects and manipulation1 hour
R data-driven programming skills1 hour
Data integration, reshaping, cleaning, and transformation1 hour
Grouping and summarisation1 hour

Big data and data analytics hands-on (two days, twelve hours)

First day

OutlineDuration
Data mining tasks And critical issues 1 hour
Market index construction and principal component analysis1 hour 30 minutes
Market segmentation and K-means clustering1 hour 30 minutes
Online music and market basket analysis2 hours

Second day

OutlineDuration
Stock automatic trading prototype system and neural networks, support vector machine, multivariate adaptive regression splines2 hours
Predicting cytogenetic abnormalities by classifying microarray data (including feature selection, random forest, k-nearest neighbours)2 hours
Meta-learning by bagging and boosting2 hours