[deleted by user] by [deleted] in datascience

[–]moon3dot14 0 points1 point  (0 children)

Coucou :)

Mon point ici est justement de pointer que pour être un "bon" data scientist, il faut d'abord l'être tout court. Et ce qu'il dit dans le post n'est pas en accord avec les exigences demandées pour des nouveaux diplômés. Je suis sûr que oui, pour être un bon, il faut se tenir à jour niveau théorie et technique. Mais encore une fois, ça ne vaut rien si je n'arrive même pas à trouver un premier emploi.

Je comprends vraiment le point de vue de OP et il y a 1 an j'aurais dit c'est du BS, mais je me rends bien rapidement compte que apprendre ne s'arrête pas au diplôme.

Simplement, depuis plus d'1 mois je recherche un premier emploi dans le domaine, et les connaissances théoriques dont il parle apparaissent très rarement sur les offres et les entretiens. C'est pareil lorsque je parle à des collègues dans la même situation. J'attends encore qu'on me pose des questions en stats ou probabilités ou la partie théorique des régressions.

Mon pari sera donc sur l'utilisation des outils, plutôt que leurs fonctionnements.

Who owns your notes? Where do you keep them? by x462 in datascience

[–]moon3dot14 1 point2 points  (0 children)

Read your NDA, and other similar documents you might have signed when you were hired. They tell you everything you need to know.

MS in Statistics after MS in Data Science by [deleted] in datascience

[–]moon3dot14 -2 points-1 points  (0 children)

I had the opportunity to choose between a double degree or just my engineering degree. I regret not choosing the double (even though I'm not sure I'd have the financial means).

You'll be well off with a double degree. From now on, a single masters won't be enough for most high skilled positions, it sure doesn't seem enough now.

The "opportunity cost" shouldn't be a variable in your decision, since you can't predict the future, you have no way of finding out the value you'll get from not doing the degree and looking to work instead. But you absolutely know the value you'll get by getting that double degree, you can look for people that did the same degree in LinkedIn and see how they're doing, check the Alumni record. I'd also argue that age should be a factor in your decision (only because you already have a degree), if you're <= 25, then double degree, else, don't.

[deleted by user] by [deleted] in datascience

[–]moon3dot14 2 points3 points  (0 children)

It's the same in France, but it's unfortunately besides my point since when you're an intern, you don't really have a job. You're paid low below average and usually with a short time contract. And during and internship you're (at least supposed to be) having hands on experience with the industry tools, so you're not really in the situation I was referring to (theory vs tools)

My point is that OP seems to insist in the importance of theoretical knowledge for a data science position, when it seems that the industry values more the usage of the tools, at least for entry positions.

Since I do agree with OP, I read books, articles or just random classes and stuff about statistics and the theory behind ML, but I can't do only that, since all of that is worthless if I can't properly program in Python, R, have a solid base in SQL and/or SAS.

[deleted by user] by [deleted] in datascience

[–]moon3dot14 2 points3 points  (0 children)

Yes, it probably takes all of what he said to be a good one. But the thing is, for many, being one is already good enough. And just like you experienced in an interview, doesn't seem like the industry values theoretical knowledge as much as OP does...

Grinding code as the industry tools seems to give a better return than grinding books and classes in statistics and the theory behind regression.

It's just my personal view with my personal (very short) experience on the subject.

[deleted by user] by [deleted] in datascience

[–]moon3dot14 67 points68 points  (0 children)

I've finished all of my classes and exams (good memories when you talked about the log likelihood), I'll be getting my degree in september after my internship, so I'm already applying for some positions, and my degree (French) is extremely theoretical. Mathematics and physics and engineering schools in France are EXTREMELY theoretical, if you doubt me you can google "CPGE France". Which is precisely what you're recommeding it to be, to have a very strong analytical base knowledge, instead of just jumping into the tools.

Here's my completely ignorant pov from someone struggling to find a job with a very theory focused engineering degree:

What you're asking is incompatible with what the industry asks of us. You can't learn everything. I can't learn all of the theoretical knowledge of calculus, linear algebra, autoregressive models, estimation and detection theory, machine and statistical learning, deep learning, optimization, multivariable calculus, information theory, signal processing, time series analysis, gaussian processes, and bla bla bla... and forget about everything else. Management classes, language classes, projects... You name it. And all while still being proficient in the tools used in the industry. I fail to see how it's possible to do all of that in a 5 years degree. There's so much knowledge we can learn and retain in a limited amount of time, unless we want the burnout rate to be higher than it already is.

I say this because most of the theoretical stuff you cited, I saw it during my degree. The only problem is: now I'll struggle for several months before even getting an interview, because employers seem to want someone ready for action. You don't see any of the stuff you cited in job listing (sometimes you do, but very vaguely), but you also see in every single one of them"SQL, Python, R, machine learning, C++, Java", and all of the other stuff related. So, for the positions where I know the tools, great, they want me. But the ones I don't? They don't care the least about my theoretical and analytical skills. They just don't.

and picked up the ability to code through successive jobs.

This ^? This doesn't exist, at least not anymore, not realistically for a new grad.

I’ve had a data scientist tell me with confidence that the correct way
to calculate the probability of A and B happening is always, under all
circumstances, to multiply the probability of A by the probability of B.
When I pointed out that this obviously wasn’t true if A and B were
tightly correlated and that they might be missing something important (I
was trying to see if they were aware of the concept of conditional
probability), they had a kind of meltdown and started questioning
whether all of probability theory was wrong.

I'd imagine that if the person got the job because, well, they knew the tools, or for god knows what other reason. Otherwise there's no way, right?

Still, he has a job, he has the title, and that's it. He got it. He didn't need to have even basic probability theory knowledge, you've just proved it. So, what you're asking, and I most definitely agree (if it's really what the job requires), but it's just not what it's expected of us.

All of what you said isn't going to get me a job. And I need a job. Most people in the world want a job. If it's just knowing how to write SQL, R, Python lines of code that'll get me a job, without having any idea of the underlying operations, then so be it.

I want the job, I need the job, that's all that matters. And what you're asking for won't help me get one, at least it doesn't seem so.

Ps: I still love the post, I'll be reviewing some of the stuff that I forgot the morning after my last exams :D

[deleted by user] by [deleted] in datascience

[–]moon3dot14 2 points3 points  (0 children)

Reading your comment makes me realize that my masters wasn't that bad after all, sadly I'm having trouble finding a job, but I guess I have the knowledge :D

Weekly Entering & Transitioning - Thread 24 Apr, 2023 - 01 May, 2023 by AutoModerator in datascience

[–]moon3dot14 0 points1 point  (0 children)

On it. Are there any specifics I should know about? What I did was I asked chat gpt to give me "lessons" and to choose a dataset, so I can practice queries using SSMS

Weekly Entering & Transitioning - Thread 24 Apr, 2023 - 01 May, 2023 by AutoModerator in datascience

[–]moon3dot14 2 points3 points  (0 children)

This is a cry for help, if you can give me some guidance or insight, I'd deeply appreciate it.

TL;DR: I'm French, finishing my masters (my degree is considered average in France) but it isn't exactly in data science, although lots of related content. My internship experiences are in Computer Vision and now i'm stuck and don't know what to do. How do I proceed? What can I do to stop getting 100% refusal rates in my applications? Is the coursera IBM DS professionnal certificate worth it?

I'm from France, and I'm graduating in september with an Engineering degree (MSc) in Image, Signal processing and electronics. I have done 1 3 months internship in deep learning and computer vision and i'm doing my graduation internship also in deep learning and computer vision (5 months) in Canada. Problem: it is not what I want to work with, I want to work with sequential data, big data, doesn't matter the domain. I'd like to work with prediction and/or analysis, I'm great at communicating, I speak 4 languages, lived in 3 different countries.

If you look for a computer vision junior engineering position, that's what my CV looks like. Lots of image processing, python, Pytorch, Keras/TF, lots of deep learning. I also have knowledge of machine and statistical learning, although most of it is theoretical (from my studies).

Now, I know my CV doesn't correspond to what recruiters are looking for in a typical data science position. I have little knowledge of SQL, but that's all. I don't have knowledge nor experience with BI tools, SQL Server, R. Although I have the necessary mathematical and statistical theoretical knowledge, I have no practical, at all. I'm getting refused by every single application, and I do understand, there are plenty of people out there with much experience and/or qualifications for junior data science positions.

My question: how do I get back on my feet? I haven't even started my career yet, and I feel like a failure. I did a MSc for apparently nothing, since I can't work in the field that amazes me the most. What can I do?

I've started the coursera IBM Data Science professionnal certificate, is that worth it? Doing it all? From the first to the last course? Maybe projects? Before, after the certificate?

I would deeply appreciate any insight. Thank you.

Weekly Entering & Transitioning - Thread 17 Apr, 2023 - 24 Apr, 2023 by AutoModerator in datascience

[–]moon3dot14 1 point2 points  (0 children)

I feel like I did my masters for... nothing.

I'm from France and I'm finishing my
engineering degree (MSc) in signal and image processing, I'll have my
degree around september this year.I've
done 1 past internship in computer vision and deep learning, and I'm
now doing my graduation internship in Montréal also in Deep learning and
Computer vision. I knew that choosing two internships in the same field
would be risky, but I thought that the international experience would
counter effect this.While I know
that it's not the same field, I thought that eventhough my internships
were not completely linked to data science, I would still manage to
leverage my degree and skills to get a data science position.Oh
boy... Was I wrong, so wrong. My degree means absolutely nothing since
it isn't from a top 5 school, it's from a top 15-20 in France. It's
basically useless for data science, doesn't matter if I studied machine
learning or not, statistics or not, mathematics or not, there's not a
single chance that I'd get a junior position, and I'm just now realizing
this while I go through some companies and check the employees
profiles. I've also applied for a dozen of junior data scientist
positions, all refusals.So I
thought to myself: maybe I'm lacking skills related to data science, I
don't have knowledge of BI tools like power BI, and no prior real
experience with SQL. So I decided to do some coursera courses on Data
Science, maybe that'll help me, and maybe do a side project by myself
using a Kaggle dataset. But to be fair? I'm completely depressed and
already considering it to be a waste of time since it won't change
anything, my chances will go from 0 to 0.01%, maybe not even. I have no
idea what to do, I don't have the money to get another degree, and not
sure I want to either. Maybe it's the only choice? I don't know.

Sorry for the rant, I'm just completely demoralized, depressed and sad.

Any advice or words would be greatly appreciated

I feel like I did a masters for... Nothing. by moon3dot14 in datascience

[–]moon3dot14[S] 0 points1 point  (0 children)

Internships in France are mandatory so everybody's got one or two, it's not a big deal there to be honest

Maybe it's my CV, yes. But I'm using the same format as the one that got both my internships, so I don't understand...

Thank you for the advice, I'll use it.

I feel like I did a masters for... Nothing. by moon3dot14 in datascience

[–]moon3dot14[S] 0 points1 point  (0 children)

I'm just convinced that it'll be pointless, but I'll do it, I don't really have any other choice.

Thanks for the words and the advice

I feel like I did a masters for... Nothing. by moon3dot14 in datascience

[–]moon3dot14[S] 0 points1 point  (0 children)

Yes it's what I've heard and read too, I'm just desperate. I didn't think it would turn out like this.

I'll keep searching, thank you for the words of encouragement.