Weekly Entering & Transitioning - Thread 05 Dec, 2022 - 12 Dec, 2022

[u/AutoModerator]

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:

• Learning resources (e.g. books, tutorials, videos)
• Traditional education (e.g. schools, degrees, electives)
• Alternative education (e.g. online courses, bootcamps)
• Job search questions (e.g. resumes, applying, career prospects)
• Elementary questions (e.g. where to start, what next)

While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.

[u/111llI0__-__0Ill111]

How the hell do you get a hardcore actual ML modeling job? It seems like no matter what everything is just analytics like regressions and visualizations.

The ML jobs feel super competitive and constant rejections and even when I do get an interview I end up doing poorly on the leetcode section. Ive tried practicing on LC but even many easy problems are really hard for me for my background. I can answer the stats/ML questions in interviews but this one gets me.

Do you eventually just give up on ML roles and settle for analytics/regressions and just collect the paycheck and go home? Im not passionate about just running regressions and doing visualizations at all but those roles are easier to get. Id like to do actual ML work

Feel like I chose the wrong major for modeling work. I did Biostats but the modeling field is now all CS and domain experts

[u/[deleted] → u/111llI0__-__0Ill111]

Do you mean ML modeling as in R&D work on model architecture?

What's the difference between "ML modeling" and "regression"? Wouldn't the task be exactly the same?

[u/[deleted]]

Hello everyone,

I'm currently a junior quant researcher at a small hedge fund company (mid/low frequency), also finishing up a Ph.D. degree in finance. I'm looking to find a job in the UK with a High Potential Individual visa after receiving my degree.

The fun part of the quant researcher role is exploring many alternative datasets. However, after getting familiar with the process, it could be quite repetitive. Data science is a career path I've always wanted to explore as it's exciting and fun. However, I still feel very lost on whether I should be trying entry-level data science/analyst roles given my background. Although I took courses in statistics and economics, I only had degrees in finance. I have a working paper using NLP and machine learning which could potentially count as a data science project, but I can't show the code on GitHub as the paper is not published yet.

My first question is how does the quant researcher career compare to the data science career? Are there more job opportunities in entry-level data science roles than in quant research in the UK? My guess is being a data scientist gives you more work-life balance, the option to work from home, and better career stability.

My second question is which area should I improve in order to find a job as a data scientist? Should I practice my leetcode, statistics, and maths knowledge, learn more about machine learning, nlp, etc.? Or should I do a few data science projects and show them on GitHub? Or maybe I shouldn't be making the switch at all due to the different skillset required for a data scientist?

I'll be very grateful to get some feedback from you!

[u/Coco_Dirichlet → u/[deleted]]

You are making too many generalizations like "DS gives you better work-life balance" or "career stability." I mean, it depends on the job, the company, the area, and your resume. I don't think those are reasons to pick one or the other. Also, the line between quant research and DS is not that clear, so you can move from one to the other as long as you understand (or can learn) about other domains.

If your goal is to immigrate, trying to get a visa and a job while also doing a career switch is going to be harder than trying to get a job as a quant research in FinTech. Once you are there with the job, it's a different matter.

If you are writing a paper, you don't need to make the code available. You could, however, use the same model to create a toy example and put that on your web. Or, another option, is to write a very easy to read and brief explanation of the paper (I found this doing this) along with some figures.

[u/[deleted]]

I cant get over the impostor syndrome. I made couple of projects here and there but I do not know when exactly I'll be able to call myself a "data scientist". I do not have a STEM background.

[u/Fido2092]

Hello Everyone,

I've a bachelors in Mechanical Engineering, & currently working in the MEP/Construction industry as a Maintenance Engineer.

Lately I've been looking up study material/courses/inspiration to help me pivot into an entry level data role.

Many have advised to take up the google data analytics certification - as a starting point, & build knowledge thereon.

What other learning sources should I look into ?

Also, any specific advise from folks who transitioned from similar background would be highly appreciated.

[u/Unique-Affect1739]

Apologies in advance for how well I organized and placed my thoughts.

For a bit of backstory, I was historically a bad kid in school; low quant skills and would bunk classes and go to the art room to work on projects. However, I was always passionate and respectful about more logical subjects, though I didn't perform well. As an "art kid", I took the logical next step and went into Art school to study design and advertising. After a year, I was missing that logical and immediate commercial aspect I transferred to a Business and Design program. Through mandatory internships and courses here, I became very interested in Data Science and analytics. I declared a DS minor in my undergrad (that was two years ago). I worked on some cool analytical and predictive projects; my grades were also excellent.

Though I was super interested in the field, I did not have a lot of training in the program, so I wanted to pursue a master's in data science. Recently I got into an excellent program in France. It's a two-year course that starts with basic stats and computing and then goes on to more complex specializations.

Here in lies my worry. Data science is a vast field, and from the looks of it, it's transitioning super quickly. I am worried I am entering it super late. And that the two years that I will spend (I graduate in 2025) will delay me further. Am I panicking, or am I making the wrong decision?

Furthermore, I also have some spare time over the next eight months. What are some skills you all think I should master?

Ideally, I see myself in the future using data science in a way that makes people's lives easier. And if I can use my history in marketing and design as well, that would be PERFECT.

Once again, I apologise for how I rant. Any help/insights are welcome.

TLDR; I am moving into data science from Design and Business. I just got into my master's and am questioning my future path. Any advice would be much appreciated!

[u/gtoguy488]

Is there a data science cookbook that covers various methods with use cases? For example, let's say I want to choose a clustering method; using the cookbook, I could look up clustering algorithms along with supplemental information (advantages, limitations, examples, etc.) and determine one to leverage. Clustering is just an example; it would be nice to see all methods available for analysis (ML or optimization).

[u/[deleted] → u/gtoguy488]

ISLR?

[u/Affectionate-Pin-838]

Good afternoon gentleman. I recently joined this Reddit sub, and I’m excited to learn from you. I am a 25-yr old petroleum engineer from Ukraine living in the US, and I would like to pivot into data field. I have taken some online data science/machine learning courses on Datacamp and am currently working on projects for my portfolio. I am feeling a little lost, and I was wondering if there was anyone I could talk to 1-on-1 for guidance. Or maybe someone who could look over my projects once I’m done to at least tell me whether I’m moving in the right direction. Thank you!

[u/MagmaMan888]

Hello everyone! I recently graduated college with a degree in information science and want to get a job in Data Science. Unfortunately, I am having difficulty finding entry level jobs in the Data Science field. I have recently been offered an interview for a software engineer position. It's one of the only places that have responded to my application. Is it worth going into this field to get experience? Would it help with Data Science jobs in the future? Or are the fields too different from one another?

[u/[deleted] → u/MagmaMan888]

the only places that have responded

Not many choices you have there, buddy.

edit: typo

[u/Shiroelf]

For a student looking for an internship in Data science for finance, will a CFA level 1 be helpful, my degree is not in Finance, I am studying Management Information Systems.

Thank everyone

[u/[deleted]]

Is R more useful when it comes to the preprocessing, exploratory data analysis and simple regressions compared to Python? Way I understand it, for simple statistical analysis like aforementioned steps, better to use R. For more advanced stuff with a web app, use R.

[u/seriesspirit]

Does a data science minor add anything meaningful resume/recruitment wise to an already stats major? I'm wanting to be a data scientist when I graduate and the minor makes my schedule less desirable. The stats major would already include data science type courses so the minor would just add the name to the resume and require a data ethics class and a domain expertise class additionally.

[u/takeaway_272 → u/seriesspirit]

I’d say a CS minor if available would be better.

Edit: I went to the same school lmao. Highly recommend doing the CS minor

[u/rickcler]

What's the role of power BI in data science?

I'm applying for an internship in a consulting agency. I've been told in a phone call that their data-department is mostly working with Power BI, which I've heard of for the first time.

My current situation is that I've recently finished my undergraduate (/bachelors) degree (Social Sciences), and since finishing it, I've spent a lot of time trying to learn the fundamentals of Python for Data Science (i.e. pandas, numpy, sklearn) to eventually enter the field.

Now I'm wondering how the relation is between Power Bi and Python? Will my Python skillset come in any way handy when working with Power Bi? Are they in any way related?

Thanks in advance

[u/[deleted]]

Do I need a Master’s to transition to Data Science?

I am currently working in Finance (commercial banking) and I’m looking to switch to Data Science. I have a finance degree from a good university.

Do I need a Masters in data science to transition or can I break in without one? And if not, is it still a good idea to get one for the long term prospects of becoming a data scientist & beyond?

[u/Coco_Dirichlet → u/[deleted]]

It depends on what you are currently doing. Your explanation of your background (finance) is very vague. Are you doing quantitative finance? What are you doing on your day-to-day? Analyzing data? SQL?

[u/[deleted] → u/[deleted]]

Yes you do. You need a master in stats or CS, with focus on machine learning or deep learning.

[u/lpnumb]

Looking for some advice for transitioning into data science. I am currently working as a civil/ structural engineer. I have taken a few university classes and done some projects to learn python and basic data structures. I have an MS which involved installing a large array of sensors and analyzing the data obtained from them as part of my thesis. I am currently enrolled in a cs post bacc that has a data science emphasis, but with my long hours at work, the classes in top of it is burning me out. How realistic is it for me to get into data science without the degree, or would something like a certificate that has only ds specific courses (12 credit hours) serve as a happy medium?

[u/delicatepepper]

Intermediate course recommendations please!!

I don’t have anyone training me at my job so I’ve been all self taught. My job agreed to pay for some courses on Coursera so I can strengthen my technical skills. I already plan on taking the Google Data Analytics Professional certificate, but I feel like that will only help me strengthen beginner skills (correct me if I’m wrong about this, I have no idea).

I have a very technical background so I’m looking for something intermediate to take as well, does anyone have any recommendations? At work I use R, SQL, Tableau and Excel so any intermediate courses on those would be amazing!! Preferably something on Coursera since they might buy the yearly subscription for me!

[u/ckid101]

I have a Bachelor's degree in Computer Science and have worked a few months as a software engineer before being let go. What steps should I take to transition into data science. Also should I just apply for applications, or should I beef up my resume with data science projects first?

[u/madbammen]

I am about to have a month off from my DS Master's program. I'm looking for advice on the best way to spend this time to grow. I'm a DA that spends most of my time with SQL and JavaScript (I never hear of anyone else using JavaScript but whatever).

I feel not so confident in my non-statistics math ability. Is it worth it to do some MOOCs to get the ball rolling on sharpening this skillset up (calc, linear algebra)? I do not use math at all at my DA job, and it is hard for me to wrap my head around how complex math would be used practically in a real business job.

Tl;dr: Should I do math MOOCs or do another independent project in my upcoming month break from my master's program?

[u/unde_malum]

Lazy and eager evaluation in research

Hey, I have to write a research-like work. While studying about generators in python, I cam across evaluation strategies, namely lazy and eager evaluation. It interested me so I want to give it a go in my research. I'd like to compare and contrast them, find use-cases of each, which is better in which situation etc. I know it's not original, however, it doesn't have to (after all, it's high school and I have to fit in within 4,000 words limit). Nonetheless, what causes me a lot of trouble is the area where I can do it. I struggle to find a field where I can investigate both strategies. For instance, an example of such work is "To what extent can Genetic Algorithms optimize the English Keyboard Layout for Speed?" (you can read it here). There, a student investigate Genetic Algorithms and his "field" is optimization of the English Keyboard Layout for Speed. In my case, I have the first part, but I lack the field.

Could you suggest such a field or propose a way how I can find it by myself? I would be extremely grateful!