NUS SOC Summer Workshop 2019
About Topic
Data Analytics for Winning Data Competitions
Cluster: Big Data and Cloud Computing
Data Science and Business Analytics are exciting areas that has many new applications that could potentially revolutionize our lives. One of the most important area is forecasting business or economics activities. For example, banks using customers data to predict their credit card default rate, retailers use online browsing behaviors to predict online advertisement click-through rate, and governments using analytics to reduce crime rate and traffic congestion.
This workshop begins with important classification algorithms including decision tree, random forest, and gradient boosting machines including XGBoost and LightGBM. We will discuss how to conduct exploratory visualization for data transformation, handle missing values, cross-validation, features engineering with domain knowledge, grid search for hyperparameter tuning, and stack results from multiple prediction models. We will learn and practice R and Python to apply algorithms on Kaggle’s famous tutorials for beginners, such as predicting real-estate prices, predict future sales, or predicting the survivors during the Titanic event.
During the second phase students will work in groups to participate in an (active) data competition problem on Kaggle.com. We will work on topics that are more relevant to business analytics based on mostly structured dataset. For example, the current topics in 2018 October include “Google Analytics Customer Revenue Prediction” and “Using News to Predict Stock Movements”.
This workshop begins with important classification algorithms including decision tree, random forest, and gradient boosting machines including XGBoost and LightGBM. We will discuss how to conduct exploratory visualization for data transformation, handle missing values, cross-validation, features engineering with domain knowledge, grid search for hyperparameter tuning, and stack results from multiple prediction models. We will learn and practice R and Python to apply algorithms on Kaggle’s famous tutorials for beginners, such as predicting real-estate prices, predict future sales, or predicting the survivors during the Titanic event.
During the second phase students will work in groups to participate in an (active) data competition problem on Kaggle.com. We will work on topics that are more relevant to business analytics based on mostly structured dataset. For example, the current topics in 2018 October include “Google Analytics Customer Revenue Prediction” and “Using News to Predict Stock Movements”.
Students interested in this workshop should have basic knowledge of object-oriented programming and basic Statistics.
About Lecturer
Professor Huang Ke Wei
Dr. Ke-Wei Huang is a faculty member in the Department of Information Systems and Analytics at the National University of Singapore (NUS). Dr. Huang joined NUS in July 2007. He received his Ph.D. (2007), M.Phil. (2005), and M.Sc. (2002) degrees in Information Systems from the Stern School of Business at New York University, and his M.B.A. in Finance (1997) and B.Sc. in Electrical Engineering (1995) from National Taiwan University.
Dr. Huang's research interests are in the economics of information systems and data mining for financial applications. Currently, he focuses on various topics of pricing digital goods, labor economics of IT professionals, and data mining or econometrics issues for topics in accounting or finance.
Dr. Huang's research interests are in the economics of information systems and data mining for financial applications. Currently, he focuses on various topics of pricing digital goods, labor economics of IT professionals, and data mining or econometrics issues for topics in accounting or finance.
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