Event Details

Technical Session - Data Science - Case studies from New Zealand Government​

ICFOSS is organizing a technical session  by ​Dr​. B.P. Nair,  on​ a topic of great demand - ​Data Science. Request your presence for the talk and interactions there on with the Data Science expert. The session is organized in association with IEEE Kerala CS Chapter.

​Topic : Data Science - Case studies from New Zealand Government

Date: 6th (Monday)  November, 2017 

Time: 3:30 pm

Venue: Travancore Hall, Technopark, Thiruvananthapuram

Bhaskaran Nair is an experienced Data Science professional currently working as Senior Researcher (Data Science) at Inland Revenue, New Zealand. He has a Masters degree in Statistics, a ​MPhil degree in Data mining and Machine Learning, his Doctoral thesis is on the genetics statistics in silkworms. Dr. Nair was working as Statistician at Indian Council of Agricultural Research and Scientist (statistics) at Central Silk Board before migrating to New Zealand in 1999.

Over the past 30 years, Dr. Nair has built on diverse expertise in the application of statistical techniques in qualitative and quantitative data derived from diverse fields such as education, economics, agriculture and industry, and publication of research reports, papers and documents in both national and international forums. 

Dr. Nair's current work focusses on the application of predictive analytics and machine learning algorithms to detect tax fraud, identify risky and non-risky tax customers, tax agents and hidden economy.

Some important research project Dr. Nair worked with are:

• Overseas Trade index (OTI) and Retail Trade Deflators project.

• Implemented hedonic regression as an alternative approach to quality adjustment in the Consumer Price Index in Statistics New Zealand. 

• Confidentialised unit record files (CURF) under Statistical Disclosure Control project of New Zealand Household Labour Force Survey data

• measuring information loss in CURF datasets.

• Impact of tertiary education on the post-study

• Labour market outcomes for student loan borrowers and allowances recipients using the LEED (linked employer-employee dataset);

• Produced several publications (web reports and journal publications) that describe the results of investigations, analyses, and research;

• Provided advice to policy units and other tertiary education group staff on trends and outcomes of tertiary education with special reference to student loan borrowers scheme.

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