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Executive Certificate in Financial Decision Making: Big Data and Machine Learning

Executive Certificate in Financial Decision Making: Big Data and Machine Learning
Organizer: HKU Space
Apr 23, 2019 (Tue) - May 24, 2019 (Fri)
Tue, Fri, 7:00pm - 10:00pm
Application Fee
HK$150 student only needs to pay one time application fee for all EC in Big Data Series

Course Fee
Course Fee : HK$8000 per programme (course fees are subject to change without prior notice)


The Executive Certificate in Financial Decision Making: Big Data and Machine Learning programme aims to provide students with the fundamental concepts and knowledge about Big Data and to develop their analytical skills by applying regression analysis and machine learning to solve business problems.



It provides a practical approach for the students to apply regression and machine learning methodologies for analyzing big data and facilitating business and financial decision making.



On completion of the programme, students should be able to:




  • Outline data preparation procedures and examine the process for handling Big Data;


  • Interpret regression results and build business models using regression methods;


  • Apply machine learning methodologies to perform analysis and forecasting;


  • Evaluate various regression and machine learning methods as well as identify patterns for business and financial decision making.



Course Content:



Data Preparation Process




  • Data Cleansing, Data Integration, Data Evaluation


  • Import Data


  • Data Cleansing: Handle Missing Values, Recode and Rescale Variables, Separate into Training and Testing Sets


  • Solution for handling Big Data: Hadoop, AWS, Azure



Regression Analysis and Business Model Building




  • Concepts and techniques of regression analysis


  • Assumption Validation and Model Assessment by interpretation of statistical results


  • Issues on analysis of financial Big Data and Cases on business model building



Machine Learning and Forecasting for Big Data




  • Supervised and unsupervised learning approaches: Decision Tree, Regression, Artificial Neural Networks, Cluster Analysis, Association Rule Mining


  • Naïve Bayes Model for Machine Learning


  • Time Series Model for forecasting and model building


  • Multivariate Data Analysis (MDA)


  • Natural Language Processing (NLP): Text Mining, Sentimental Analysis


  • Case study of machine learning for business and financial decision making


For more details and registration, please click here.