On 1 April 2019, we saw the power of machine learning techniques in building an automated stock investment system.  A/Prof Carol Hargreaves described a data science approach to identify top stocks, a trading system that not only she built with her expertise but also used by her personally.  We are grateful for the support from the host ING Bank, and for having Annerie Vreugdenhil (Head of Innovation, ING Wholesale Banking) open the event.

 

SIGKDD Singapore & ING Bank are jointly organizing this seminar delivered by A/Prof Carol Hargreaves, Director of the Data Analytics Consulting Centre at the National University of Singapore. The event will be opened by Annerie Vreugdenhil, Head of Innovation, ING Wholesale Banking.

Using Machine Learning Techniques to Identify Top Stocks

April 1st 2019 | 6.00pm to 8.00pm | ING Bank, Spark #12; 1 Wallich Street, Guoco Tower, 078881

Abstract

Interested in what goes into building an automated stock investment system? Using machine learning techniques of both unsupervised and supervised varieties, one can mine troves of data to discover statistical patterns towards identifying top stocks to trade. Demonstrated on a case study, we will see how the learned models perform upon paper trading, comparing the stock portfolio performance to that of the stock market index.

Speaker

A/Prof Carol Hargreaves is the Director of the Data Analytics Consulting Centre at the National University of Singapore. Prof Hargreaves has a joint position in the Department of Mathematics & the Department of Statistics & Applied Probability. Her role includes providing data analytics advisory & consulting services to industry, designing and teaching data analytics executive training courses for industry professionals, and is a noted keynote conference speaker and moderator.

Prof Hargreaves has a passion for solving business problems using analytics and machine learning techniques to build data driven solutions for growth and effective decision-making. She aims to provide her clients with organic revenue growth, allowing business processes to become smarter and faster while keeping customers engaged.

Prof Hargreaves is an analytics and business intelligence professional with over 30 years analytics experience, with leading roles in the pharmaceutical, healthcare & fast moving consumer goods and education industries. Prof Hargreaves has worked with a variety of leading companies to make businesses more intelligent. These include Pfizer, Novartis, Merck Sharp & Dohme, Nestle, MasterFoods, Goodman Fielder, Foxtel, Aztec (IRI), Cegedim Strategic Data (Quintiles), National Health & Medical Research Council, National University of Singapore.

Date/Time Monday 1 April 2019, beginning with light reception at 6.00pm

Venue ING Bank, Spark @ level 12, 1 Wallich Street, Guoco Tower, 078881

RSVP on our Meetup

See you there!

That’s the question that a group of data scientists from industry and academia discussed over dinner on 14 December 2018 at the NUSS Suntec City Guild House.  We were fortunate to have Marianne Winslett, a professor emerita of computer science at the University of Illinois at Urbana–Champaign, to open the event with a keynote address. We see the role of SIGKDD as contributing to identifying and building the skills required for data science professionals.  Though simple and direct answers to such a complex question are elusive, the discussions were fruitful and pointed us in the right direction. It was also a chance for us to reflect and regroup as a community.

 

 

On 18 Sep, we had a chance to learn about the power of networks from Dr. Gábor Benedek (Lynx Analytics), while networking with data science enthusiasts over pizza and beer. We are also grateful to WeWork for hosting our event.  Here are a few pictures to remember by.

 

Big Graph Data Intelligence – Analyzing Large Connected Data in Social and Health Science

September 18, 2018 | 6pm to 8.30pm | WeWork 71 Robinson

Abstract

The miracle behind Social Data is that we have information on the detailed structure of how people are connected to each other, who are the family members, who are the friends, and who are the influencers, who are the followers. We call this structure as graph topology. But beside the Social Data Topology (from diverse sources) we can also observe characteristics of and behavior mechanism among the individuals. Many researches have proved that inside micro-topologies (cliques or communities) people tend to think, decide, purchase or do similar things, have similar profiles in many cases. Thus, if we want to understand or change customers’ decision, we must use the micro-topology information, not just individual connections.

Today, multinational companies (banks, airlines, telecoms, insurance companies and many other domains) are closer than ever to analyze, understand and utilize Social Data. They usually have at least three different sources of Social Data, which is sufficient to build their own transactional Social Network. First, A-to-B transactions (calls, instant messages, money transfers, bookings). Second, (co-)locations and the (co-)movements (same address, sharing bills, traveling together). Third, digital behavior (browsing history, app usage) of customers, potentially complemented with external information. Just like Facebook helps not only to derive the Network, but add much diverse information on attributes and interests. This in turn enables further deep dive on the homogeneity of these communities, verify those cases when the network is similar in demography, when the network is giving an insight on commercial decisions, when the network enables the spread of word of mouth.

However, creating the transactional Network using these attributes is not easy, especially when there are dozens of millions of customers, and billions of possible and measurable interactions of and between them. The different sources of information can contain contradictions and confusions, when we observe that overlaid topologies are not matching. (E.g.: online friends and offline friends.) Lynx Analytics has experiences and solutions to build and use these networks efficiently.

Speaker

Gábor Benedek is an innovation partner at Lynx Analytics providing predictive analytics for communication companies, financial services and healthcare sectors in South-East-Asia. He has been applying SNA methodologies for Celcom, Indosat, Singtel, Telkomsel, Globe, DBS Bank in the region. Gábor received his PhD in 2003, in 2012 his T-Mobile SNA churn study was awarded as the Best Application paper by the European Decision Science Institute, and he is the author of one book and author/coauthor of over 20 articles. He was an Associate Professor at Corvinus University of Budapest, researching and lecturing in the fields of economic and business simulations, social network analysis, econometrics, data mining and predictive analytics. Gábor was among the founders of Data Explorer, the first consulting company in predictive analytics in Hungary. In 1999 Data Explorer built the first social network analysis software for churn and community detection applicable for mobile customers in Europe, based on Gábor’s theoretical foundations and proposals. In 2010 Gabor was contributing to the largest public physicians’ social network in the world, based on real patient-flow data between general practitioners and specialists.

Date/Time Tuesday 18 September 2018, beginning with a light reception at 6pm

Venue WeWork 71 Robinson Road Singapore 068895

RSVP on our Meetup.

See you there!