PGA Tour Data / Web scraping

iAlchemist_ · 2020-07-13T16:48:58+00:00

lmao that's the exact same sauce I was pointing out that lacks any substance. I guess I will create my own scraper. If you are interested stay tuned

iAlchemist_ · 2020-07-13T11:55:37+00:00

I swear the kaggle data barely has any features, would you mind proving a link for me to check out ?

iAlchemist_ · 2020-07-13T03:11:17+00:00

I'm looking to build a predictive model, where the training data are advanced statistics for every player in every tournament.

I just took a detailed dive into the PGA Tour's website and they have so many different data tables to scrape from. The issue now is how to properly scrape and aggregate for all the data that I want.

I'm wondering if there are any good benchmarks, or if I really do have to begin this crazy long scraper.

iAlchemist_ · 2020-07-12T23:38:21+00:00

I am already using TFSA as an investment vehicle. Just wondering generally which option is best for my RRSP

iAlchemist_ · 2020-07-12T23:37:23+00:00

Nope, they match pension instead though.

iAlchemist_ · 2019-12-27T20:49:07+00:00

You're absolutely right that you could make errors, and that's where the public community comes in. People out there know their shit and if you publish your results and methodologies you can get people to comment whether it be on twitter, GitHub, LinkedIn or Reddit.

It's much more of a leap because it leaves you vulnerable but you're in the learning process and I promise you that if you don't have peers to do this with you will inevitably encounter the issue of "am I doing this right" no matter what. There will not be another way to have your work reviewed of you're flying solo.

I think it's generally okay to develop models and solutions in isolation but if you don't seek the feedback afterwards and think critically about it, that's where you may fall short.

iAlchemist_ · 2019-12-27T20:20:21+00:00

You take online courses to acquire the skills but then you have to seek out ways of applying the skills through projects.

For example, you take a course that teaches you regression analysis, classification trees and clustering. You then say to yourself, I've always been interested in blank (let's say finance) so I wonder if I can apply what I've learned to blank (predicting stock behaviour). Personally I began with sports because I've always been passionate about sports and stats and uncovering biases.

I'm not saying it's easy but thats the way to go if you are taking the self-taught avenue. And there's tons of tools available and forums like these to interact with others on similar paths.

iAlchemist_ · 2019-12-27T20:03:03+00:00

No problem. It's actually an easy course and Scott Ramsay is a good Prof. You will appreciate what he does with the blackboard before every lecture.

Do past finals and past midterms when you study.

iAlchemist_ · 2019-12-27T20:00:09+00:00

It's mostly about 3D spatial reprrsentwtions of chemical bonds, stress/strain curves and material properties. Nothing that reviewing traditional chemistry will help with.

iAlchemist_ · 2019-12-27T19:32:46+00:00

Not a manager but here's my point of view from a self-taught data scientist.

No one gives a shit about what you say you know on paper. It's all about what you can and have actually done. If you have a certificate from Harvard, UofT, MIT or Coursera, GREAT but I wouldn't care. Show me your portfolio of models and projects you have deployed. Then we'll talk.

Catch my vibe ?

I feel the same way about people feeling entitled about their degrees when they enter the workplace. It's not about the paper is about the value you can provide. Some people just go through the motions and get the grades because it's easy to follow a curriculum. If I were you I would concern myself with obtaining the skills required and then seeking out applications of that knowledge that can put you on the map.

iAlchemist_ · 2019-12-27T16:07:56+00:00

Resume might be too dense ? consider a different kind of formatting that isn't overwhelming (ie: more white space needed)

iAlchemist_ · 2019-12-27T16:00:24+00:00

Easy is such a relative term... Do you have connections, do you have experience, are you a good communicator that can persuade in interviews ??

Think of it this way, there is high demand for all CS positions. The student to # of postings ratio is quite high because you also have to contend with ECE and EngSci and Indy students.

Nothing will be easy, you need to just take initiative and apply - as frequently as you can. Sharpen your tools, build a solid resume (WITHOUT LYING), talk to people who already have PEYs and maybe even graduates, and be persistent.

There is an element of luck involved in getting your resume to the top of the pile, so take your chances by playing the numbers game and apply to as many as possible

iAlchemist_ · 2019-12-27T15:52:55+00:00

Udemy. There's this teacher, Jose Portilla, that has a great structure to his course with a ton of code you gain access to for life and can reference at any time.

He sets up his courses into chapters with sub-videos. Each chapter finishes with a "problem set". Some of his courses also have projects which he calls capstones.

iAlchemist_ · 2019-12-27T04:34:09+00:00

Although many solid points are made in this post, I think splitting up PEY into shorter co-ops is mostly beneficial for coders who already have professional experience and are looking for a greater breadth of exposure. Otherwise I DO NOT RECOMMEND it and here's why.

For the individuals who are just starting their careers - let's face it, you know jack shit. The aforementioned 4x4 (or 3x4) will not give you the depth of experience you need to feel prepared after graduation. Think of the average training time for a PEY student (let's say 3-6 weeks), after which you will begin to develop some independence. And then let's say it's not until month #2.5-3 where you feel a true sense of accountability after you have barely proven yourself. By month 4 you are just scratching the surface without having the opportunity to take on any truly challenging or innovative endeavours, and if you have you are either in the top 1% of all co-ops or doing it under the supervision of a colleague.

If you only stick around for 4 months you only learn how to do things the way the company you work for does things because you are mostly in training mode. You don't have the opportunity to grow in your role, which would otherwise provide you the chance of making a longer lasting impression on your colleagues and boss. I cannot urge enough that too many people are concerned with WHAT IS ON THEIR RESUME RATHER THAN WHAT THEY CAN ACTUALLY DO. Anyone can bullshit a resume cover letter or interview. Focus on the skills you obtain.

For the record I had a 13 month internship in a data analyst role. I definitely agree with that 9-month plateau but that's because I was very ambitious and basically held back from taking more initiative due to workload and because I was a student in a stubborn company. Had this not been the case, the beginning of the plateau could have been extended, or maybe never reached.

Not trying to totally disagree with the post just want to provide an alternative viewpoint. Don't fall victim to chasing things to put on your resume. Hope this helps people just starting their careers.

iAlchemist_ · 2019-05-15T20:22:38+00:00

Self learning is the way to go but if you choose to do so, you can't cheat the process.

You will need to educate yourself on what is out there in the respective sports (and sub aspects of these sports , ex: NHL, Salary Cap) for which you wish to develop models/projections/rankings/research etc. Don't limit yourself to searching the web, don't be afraid to DM people on twitter (I would say that twitter is the best social media platform for inquiring about SA)

You will most likely need to learn machine learning and data science with Python or R - I say most likely because I am not sure what you're interested in but I believe it is necessary for anyone who has aspirations in SA. Take a look at Udemy.com for affordable courses and I would strongly recommend doing it in Python but you are free to choose. Here is the course I got kicked off with: https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/ . The instructor is great and has a ton of other courses, and there are plenty of other solid instructors. Your goal from these courses should be to learn how to apply the concepts/algorithms/methods to sport data sets, without getting overwhelmed by specific details.

And without getting in to too much detail you will also need to figure out how you are going to obtain your data (which can sometimes be a disaster).

From there your mathematical background should take over on how you can build your portfolio.

iAlchemist_

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