trthatcher

While I'm not clear why you would want to go back to an undergrad given your education, I would look for programs with names like "Computational Mathematics" or "Computational Science". I earned a Bachelor of Mathematics in Computational Mathematics. The first two or three years were heavily focused on the mathematical foundations (eg. non-linear and discrete optimization, combinatorics and CS fundamentals, statistics and applied mathematics). The latter half of third year and all of fourth year were basically a "create your own adventure" where you could focus on subject areas that interested you (ex. econometrics, computational biology, industrial engineering, computational statistics + machine learning, etc). We had a breadth requirement t that often pushed people to obtain a minor from a different faculty (eg. Arts or Sciences). The program overview for my school might give you an indication of what content to look for [1].

You might also find graduate level programs that are similar in aim; my school also offers an MMath in Computational Mathematics [2].

1. http://ugradcalendar.uwaterloo.ca/page/MATH-Computational-Ma...

2. https://uwaterloo.ca/computational-mathematics/future-master...

I quit my job in May 2019... before the pandemic started.

I was working as a senior data analyst at an insurance company in Canada. The work was boring, thankless and pretty exhausting but I was paid well (for a data analyst, anyway). There were no opportunities on the data science team since I didn't have an MSc so I didn't have a clear next step. At the same time, I had started the OMSCS program that January and that was eating up my off time.

I was too busy just trying to stay on top of school and work that I just couldn't see myself having the time to prep for tech interviews or do some side projects to jump-start the tech side of my resume. I decided I just wanted a breather.

I resigned and cited wanting to get ahead on my degree. I figured this would be a reasonable narrative if the gap in my employment were to ever come up during an interview.

I spent 10 months focusing on school, enjoying my time and improving my development skills.

When I started looking, I applied to about 25 data scientist and a handful of ML engineer roles. I received no callbacks except for one ML Eng role which didn't go anywhere. Luckily an old manager was starting a data science team at another insurance company at the same time as I was looking. He basically handed me a data engineer role with a bump in compensation in early 2020.

If I reflect:

- It worked out extremely well for me. I left a non-technical job, had a nice 10 month break and ended up getting a development job where I get to write code all day. I actually enjoy my work now and I have learned so much since then. Zero regrets for me.

- At the same time, I underestimated how little my experience as a data analyst meant to data science teams. I would have had to apply to many more jobs to get something via the standard online application approach. I think that would have been really stressful.

- I ate through about 25% of my cash which was a little painful to watch.

I think if you have a strong skill set and experience profile, its probably just fine. If you were like me and trying to make a big switch (eg. data analyst to data scientist or some kind of eng), it was a risky move and I wouldn't recommend it without a plan. I lucked out. YMMV.

Data Scientist | Equitable Life of Canada (https://www.equitable.ca) | Waterloo, ON | Full-time | Onsite or Remote (within South-Western Ontario)

Equitable Life is a small mutual life insurance company (~700 people) based in Waterloo, Ontario. We're hiring our first data scientist to help found the data science team. As of right now, the team consists of my manager and myself (data/ml engineer).

This data scientist role is a foundational one; you'll need to help define our methodology, tooling and data strategy. The data scientist will act as an internal consultant within the organization and will help various teams optimize their processes through the application of predictive models. This is a great opportunity for someone with a couple years of experience under their belt.

We are primarily a Windows shop with all infrastructure managed on-premise and most development is waterfall. However, the company is actively working towards being cloud-friendly (Azure) and rethinking its development processes (eg. embracing devops tech, agile).

As this is a foundational role, we're looking for someone with either a masters or PhD in a quantitative discipline and a minimum of a couple years of experience in developing predictive models (preference for supervised learning).

Apply here: https://canr57.dayforcehcm.com/CandidatePortal/en-US/equitab...

Location: Toronto, Canada

Remote: Yes

Willing to Relocate: Depends, open to discussion

Technologies: Python, Julia, some JavaScript, SQL

Résumé/CV: https://thatcher.dev/resume.pdf

Email: tim @ my domain above

I'm seeking opportunities in the data science field, especially ML Ops if you don't mind someone who is just getting started in that space.

I'm currently a part time student in the OMSCS program. I left my corporate job ~10 months ago to focus on self development and to focus on finding work that is a good fit. I'm highly motivated, independent and I love tech; I know I'll perform given the opportunity.

Excel is a piece of software that I would absolutely not hesitate to pay for... but I'm unfortunately stuck with LibreOffice.

I used to work in an insurance company on an actuarial pricing team where the preferred tool was Excel (the modelling was pretty simple). Needless to say, I became very accustomed to and adept with using Excel. We definitely pushed the limits... but what was nice was being able to grab 600K records (maybe 20 cols) from a DB, throw it in Excel and get some results in a matter of minutes. You might have to wait a few seconds based on what you were trying to do, but Excel could handle it.

At home I run Linux... where there is no Excel, so I use LibreOffice instead. Just a few days ago I was poking around the Himalayan Database[1] and one of the tables has about 50K records. LibreOffice absolutely chokes when I try to do any filtering or calculations. As well, the pivot tables in Excel are in a totally different league than LibreOffice in terms performance and flexibility. It's unfortunate because I try to support OSS as much as possible... but LibreOffice is just so painful.

You could argue that I'm using the wrong tool for the job. Ultimately, I do throw it in SQLite or Pandas, but Excel is just so nice for ad hocs if it fits in memory.

[1] https://www.himalayandatabase.com/

You have the right idea.

You're assuming an underlying model for the data. You have a test statistic( that estimates a model parameter) and you have a hypothesis regarding a parameter. The p-value is the probability that you get a test statistic more extreme than the one observed assuming that your hypothesis is true.

Ex. You have a sample of 1000 men's heights. You compute the sample average height as 5'9 and a sample standard deviation of 3 inches.

(Unlikely) hypothesis: the average height is 4 feet. Your p-value is the probability of getting an sample average more extreme than 5'9 given that your 4 ft height hypothesis is true. Given that the sample standard deviation is 3 inches and 5'9 is 7 standard deviations from 4ft... the p-value is going to be small, so you'll reject that.

Note: I'm leaving out details and assumptions

I don't think it will be a problem to do both.

It doesn't take long to learn Julia well enough to write a Julia package that others will use. For me, that was about 3 weeks @ 10-15 hours a week, and I'm no programmer. It's a small language and it's quite readable. I'd reccomend going through the Julia manual; it doesn't take long and only requires some diligence.

The problem is the lack of packages/libraries and lots of documentation is still missing. That, and the packages that do exist with multiple contributors are still in disagreement about standards and consistency.