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Jeffrey Ng
  • Hong Kong
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  • Donghuan Tang
 

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Profile Information

Short Bio:
Customer Analytic in the investment banking industry in APAC
My Website or LinkedIn Profile (URL):
http://www.linkedin.com/pub/jeffrey-hm-ng/21/744/8b2
Field of Expertise:
Business Analytics, Predictive Modeling, Data Mining, Marketing Databases, Operations Research, Econometrics, Statistical Programming, Statistical Consulting, Artificial Intelligence
Years of Experience in Analytical Role:
10
Professional Status:
VP, Consultant
Interests:
Finding a New Position, Networking
Your Company:
International Bank
Industry:
Banking: Retail, Corporate & Investment banking
Your Job Title:
Head of Department
How did you find out about AnalyticBridge?
Linkedln

Jeffrey Ng's Blog

What do I learn from developing Analytics in a weird place?

Posted on February 9, 2016 at 8:58am 0 Comments

4 years ago, before all these hype on Big Data Analytics, right after my MBA, I dream about the digital future in investment banking. I visualized it in my mind, based on what I learnt in retail banking, and credit risk modeling, I feel that it was a possible dream. A dream that was so fragile that it could be popped one day. Or it could be a super-long journey that leads me via fire and pain or even be embarrassed. Looking back, these are the lessons I learnt:

1. Intra-preneur: It is…

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R or Python, a practical problem

Posted on June 16, 2015 at 11:14am 0 Comments

Which technology works best in a team when we are introducing data mining. The team has been using Excel as the data analysis tool, how can we apply/ run the data mining model (such as decision tree) on excel? I have been using R for a while and enjoy it very much. Good supply of fresh grad with training in R...However, when it comes to using and running the data mining model, R does a very poor job in execution. It is such a good tool when we develop and research patterns in a laboratory…

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How Analytics help in Corporate & Institutional Banking?

Posted on May 23, 2015 at 3:30am 0 Comments

There is a fire, everything burnt into ashes, reborn the phoenix.

Challenges faced by the industry

It happens to every industry, the down cycle is the time to restructure, optimize for the longer journey. The same happening to the investment banking industry. Weighted by public scrutiny and heavier regulation on disclosure, rounds of cost cutting and restructuring follows. Silicon Valley has overtaken Wall Street as the…

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Predictive Analytics: When to use or not to use a consultant?

Posted on December 26, 2014 at 3:00am 3 Comments

There are two circumstances that you should use a consultant:

1. When the consultant has both the domain knowledge and exact modeling experience: There are times that the consultant come to you and sell you the ideas of modeling something. Look for exact experience. Like an academic researchers, the most challenging task is to get the data-set, not the idea. Dataset is the execution. In the world of Big Data analytics - whoever owns the data has the command, especially in the…

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