Career-advice seminar at the University of Toronto – Wednesday, February 7, 2018

I am excited to visit the University of Toronto on Wednesday, February 7, to share my career advice in a seminar and in a question-and-answer session.  Both events will be in the Debates Room at Hart House.  Hart House is located at 7 Hart House Circle in Toronto, Ontario.  The Debates Room is on the second floor of Hart House.

Eric Cai - Official Head Shot

  • My presentation will occur from 11 am to 12 pm.
  • I will answer questions in an open forum from 1 pm to 2 pm.

I will talk about

  • my diverse jobs in industrial statistics, medicine, banking, and marketing analytics since earning my Master’s degree in statistics
  • the skills that my jobs demand but I did not learn in my formal education in statistics
  • effective strategies for finding a job in statistics
  • building an online brand as a statistician
  • developing a meaningful career in a systematic way
  • important steps that students can take during their studies to prepare for a career outside of academia

I strongly encourage all attendees to read my career-advice columns in advance, especially “How to Find a Job in Statistics – Advice for Students and Recent Graduates“.

If you will attend this event, then please feel free to come and say “Hello”!

I thank Jeffrey Negrea and Dr. Radu Craiu from the University of Toronto for their help in coordinating this event.  Jeffrey is the president of the Statistics Graduate Student Union, and Dr. Craiu is the Associate Chair for Graduate Affairs in the Department of Statistical Sciences.

 

University of Toronto Statistical Sciences Union Career Panel

I am delighted to be invited to speak at the University of Toronto Statistical Sciences Union’s first ever Career Panel.  If you plan to attend this event, I encourage you to read my advice columns on career development in advance.  In particular, I strongly encourage you to read the blog post “How to Find a Job in Statistics – Advice for Students and Recent Graduates“.  I will not cover all of the topics in these columns, but you are welcomed to ask questions about them during the question-and-answer period.

Here are the event’s details.

Time: 1 pm to 6 pm

  • My session will be held from 5pm to 6 pm.

Date: Saturday, March 25, 2017

Location: Sidney Smith Hall, 100 St. George Street, Toronto, Ontario.

  • Sidney Smith Hall is located on the St. George (Downtown) campus of the University of Toronto.
  • Update: The seminars will be held in Rooms 2117 and 2118.  I will speak in Room 2117 at 5 pm.

 

If you will attend this event, please feel free to come and say “Hello”!

My Alumni Profile by Simon Fraser University – Where Are They Now?

I am happy and grateful to be featured by my alma mater, Simon Fraser University (SFU), in a recent profile.  I answered questions about how my transition from my academic education to my career in statistics and about how blogging and social media have helped me to advance my career.  Check it out!

During my undergraduate degree at SFU, I volunteered at its Career Services Centre for 5 years as a career advisor in its Peer Education program.  I began writing for its official blog, the Career Services Informer (CSI), during that time.  I have continued to write career advice for the CSI as an alumnus, and it is always a pleasure to give back to this wonderful centre!

You can find all of my advice columns here on my blog.

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Leaving My Dream Career – Reflecting on My Decision 10 Years Later

I just couldn’t pretend any longer.

It was near the end of my second year at Simon Fraser University.  My GPA was pretty high, and I had just won a competitive NSERC Undergraduate Student Research Award to work with an accomplished cardiac physiologist.  I attended all of the relevant seminars to get the “inside scoop” on how to successfully apply to medical school, and I volunteered in numerous organizations to demonstrate my non-academic credentials.  I had already developed good relationships with several professors who would have gladly written strong recommendations for my application.  All of the stars were aligning for my path to medical school.

I was also miserable, angry and devoid of any further motivation to stay on that path.

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Image courtesy of Carsten Tolkmit from Flickr.  Obtained via the Creative Commons License.

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Seminar by Tiff Macklem: Lessons Learned from the Global Financial Crisis – Monday, March 30, 2015

I look forward to attending an upcoming seminar in Vancouver by Tiff Macklem on how he helped to manage the global financial crisis in 2008 while working as the Senior Deputy Governor in the Bank of Canada.  He is now the Dean of the Rotman School of Management at the University of Toronto.  This is an event for alumni of the University of Toronto and their guests.

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Monday, March 30, 2015

6:30 PM to 8:30 PM

Metropolitan Room – Terminal City Club

837 West Hastings Street

Vancouver, British Columbia, Canada

V6C 1B6

If you will attend this seminar, please feel free to come and say “Hello”!

University of Toronto Alumni Reception with Meric Gertler – Tuesday, September 16, 2014 @ Sheraton Vancouver Wall Centre

I will attend the upcoming University of Toronto Alumni Reception in Vancouver to meet the new President of the University of Toronto, Meric Gertler.  If you will attend, please feel free to come up and say “Hello”!

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Date: Tuesday, September 16, 2014

Time: 6:30 PM to 8:30 PM

Location:

Sheraton Vancouver Wall Centre
1088 Burrard St.
Vancouver, BC
V6Z 2R9

Opening Doors In Your Job Search With Statistics & Data Analysis – Guest Blogging on Simon Fraser University’s Career Services Informer

The following post was originally published on the Career Services Informer.

Who are the potential customers that a company needs to target in its marketing campaign for a new service? What factors cause defects in a manufacturer’s production process? What impact does a wage-subsidy program have on alleviating poverty in a low-income neighbourhood? Despite the lack of any suggestion about numbers or data in any of these questions, statistics is increasingly playing a bigger – if not the biggest – role in answering them. These are also problems your next employer may need you to adress. How will you tackle them?

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The information economy of the 21st century demands us to adapt to its emphasis on extracting insight from data – and data are exploding in size and complexity in all industries. As you transition from the classroom to the workplace in a tough job market, becoming proficient in basic statistics and data analysis will give you an edge in fields that involve working with information. This applies especially to STEM (science, technology, engineering, and mathematics) and business, but it also applies to health care, governmental affairs, and the social sciences. Even fields like law and the arts are relying on data for making key decisions.

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Don’t Take Good Data for Granted: A Caution for Statisticians

Background

Yesterday, I had the pleasure of attending my first Spring Alumni Reunion at the University of Toronto.  (I graduated from its Master of Science program in statistics in 2012.)  There were various events for the alumni: attend interesting lectures, find out about our school’s newest initiatives, and meet other alumni in smaller gatherings tailored for particular groups or interests.  The event was very well organized and executed, and I am very appreciative of my alma mater for working so hard to include us in our university’s community beyond graduation.  Most of the attendees graduated 20 or more years ago; I met quite a few who graduated in the 1950’s and 1960’s.  It was quite interesting to chat with them over lunch and during breaks to learn about what our school was like back then.  (Incidentally, I did not meet anyone who graduated in the last 2 years.)

A Thought-Provoking Lecture

My highlight at the reunion event was attending Joseph Wong‘s lecture on poverty, governmental welfare programs, developmental economics in poor countries, and social innovation.  (He is a political scientist at UToronto, and you can find videos of him discussing his ideas on Youtube.)  Here are a few of his key ideas that I took away; note that these are my interpretations of what I can remember from the lecture, so they are not transcriptions or even paraphrases of his exact words:

  1. Many workers around the world are not documented by official governmental records.  This is especially true in developing countries, where the nature of the employer-employee relationship (e.g. contractual work, temporary work, unreported labour) or the limitations of the survey/sampling methods make many of these “invisible workers” unrepresented.  Wong argues that this leads to inequitable distribution of welfare programs that aim to re-distribute wealth.
  2. Social innovation is harnessing knowledge to create an impact.  It often does NOT involve inventing a new technology, but actually combining, re-combining, or arranging existing knowledge and technologies to solve a social problem in an innovative way.  Wong addressed this in further detail in a recent U of T News article.
  3. Poor people will not automatically flock to take advantage of a useful product or service just because of a decrease in price.  Sometimes, substantial efforts and intelligence in marketing are needed to increase the quantity demanded.  A good example is the Tata Nano, a small car that was made and sold in India with huge expectations but underwhelming success.
  4. Poor people often need to mitigate a lot of risk, and that can have a significant and surprising effect on their behaviour in response to the availability of social innovations.  For example, a poor person may forgo a free medical treatment or diagnostic screening if he/she risks losing a job or a business opportunity by taking the time away from work to get that treatment/screening.  I asked him about the unrealistic assumptions that he often sees in economic models based on his field work, and he notes that absence of risk (e.g. in cost functions) as one such common unrealistic assumption.

The Importance of Checking the Quality of the Data

These are all very interesting points to me in their own right.  However, Point #1 is especially important to me as a statistician.  During my Master’s degree, I was warned that most data sets in practice are not immediately ready for analysis, and substantial data cleaning is needed before any analysis can be done; data cleaning can often take 80% of the total amount of time in a project.  I have seen examples of this in my job since finishing my graduate studies a little over a year ago, and I’m sure that I will see more of it in the future.

Even before cleaning the data, it is important to check how the data were collected.  If sampling or experimental methods were used, it is essential to check if they were used or designed properly.  It would be unsurprising to learn that many bureaucrats, policy makers, and elected officials have used unreliable labour statistics to guide all kinds of economic policies on business, investment, finance, welfare, and labour – let alone the other non-economic justifications and factors, like politics, that cloud and distort these policies even further.

We statisticians have a saying about data quality: “garbage in – garbage out”.  If the data are of poor quality, then any insights derived from analyzing those data are useless, regardless of how good the analysis or the modelling technique is.  As a statistician, I cannot take good data for granted, and I aim to be more vigilant about the quality and the source of the data before I begin to analyze them.