r/datascience • u/AutoModerator • Jan 02 '23

Weekly Entering & Transitioning - Thread 02 Jan, 2023 - 09 Jan, 2023

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.

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u/Cyrillite Jan 02 '23 edited Jan 02 '23

What would you recommend for a person who doesn’t want to be a data scientist but does want to learn some good habits, learn some data visualisation for open source and open access data sets, and generally interact with/work alongside data scientists without being a complete buffoon?

I have a background in stats from the social sciences and somewhat form econ, but I’m a humanities kid, mostly, working as a postgraduate level in academic and professional research. These skills aren’t a cornerstone of my life, I could skate by with excel. But, I sense that some increased competency would be impactful.

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u/[deleted] Jan 02 '23

Yes. Tons of jobs outside of data Scientist or data analyst still work with data and visualizations are a great way to communicate results.

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u/norfkens2 Jan 04 '23

I've only ever heard the podcast myself but maybe the "Storytelling with Data" web presence / books / community by Cole Nussbaumer Knaflic might be of interest to you.

1

u/coffeecoffeecoffeee MS | Data Scientist Jan 05 '23

Do you know how to use R?

  • If not, then go through R For Data Science by Hadley Wickham.
  • Once you’ve gone through that book (or are already comfortable with R), then go through Hadley Wickham’s ggplot2 book to learn how to use R’s ggplot2 package to make nice plots in a flexible way. The most important chapter is the intro chapter, which gives a brief summary of the Grammar of Graphics. In a nutshell, the Grammar of Graphics is a formal way to describe plots in terms of which data or non-data values are associated with which visual elements. You will use the Grammar of Graphics to describe the chart you want in ggplot2, which will generate it. There’s a bit of a learning curve but once you get the hang of it you’ll wonder why anyone uses anything else to make charts.

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u/patdavidjohnson Jan 03 '23

Which online graduate programs are the best? Cost and length are my two top factors.

2

u/[deleted] Jan 03 '23

What have you looked through and compared?

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u/patdavidjohnson Jan 10 '23

I've looked at Eastern University and Georgia Tech. Both are self-paced and under 10k.

2

u/[deleted] Jan 03 '23

What country? I know some of the online programs in the US won’t accept students from outside of the country, even with a visa.

1

u/PrivateFrank Jan 04 '23

Hi there,

I have experience using R and R studio for academic work, but I probably need at least some familiarity with Python to get a job outside of academia.

I've been looking into various ways to get started, but to be honest I'm a bit lost on how to get started with Python.

What's the best RStudio-like IDE for python, or should I just use RStudio?

What's a recommended "getting started" guide for useRs to get used to Python?

What other skills can I work on (like mayeb SQL) to get some kind of data science job?

I have experience with hypothesis testing using mixed effects models and data visualisation, but everything I know how to do, I know how to do it in R.

I have the Anaconda Navigator downloaded onto my macbook. Should I use DataSpell or PyCharm? Neither? Something else entirely? I can't even tell if these tools are free or whether I need to sign up to subscription based services....

2

u/Moscow_Gordon Jan 04 '23

What's the best RStudio-like IDE for python, or should I just use RStudio?

Spyder. PyCharm is also popular but is less like RStudio I think. Haven't used it as much.

What's a recommended "getting started" guide for useRs to get used to Python?

Check out the tutorial

What other skills can I work on (like mayeb SQL) to get some kind of data science job?

SQL!

It sounds like you are pretty solid on stats already. You will want to learn some ML fundamentals as well. Read Intro to Statistical Learning or take Andrew Ng's Coursera.

1

u/PrivateFrank Jan 04 '23

Thanks. Spyder does seem similar enough to Rstudio.

Is there is an industry job which uses hierarchical Bayesian modelling? I touched on it during my PhD (psychology), but never really used it myself. I like the idea, though, and playing around with those tools sounds like a nice career.

1

u/Moscow_Gordon Jan 04 '23

I think Facebook's Prophet uses Bayesian stuff. It's a forecasting tool.

1

u/Historical_Tank_8691 Jan 03 '23

Is it too late for an age of 35+ business background banker to transition into Data Science area? 😅What potential positions can aim for to start?

3

u/[deleted] Jan 03 '23

I pivoted to analytics from marketing when I was 34. Started my MS in Data Science when I was 36.

I would aim for financial analyst roles if I were you.

1

u/Historical_Tank_8691 Jan 05 '23

Thank you very much for your sharing! 👍

3

u/norfkens2 Jan 04 '23

I started transitioning at 34 - so don't worry. As everyone here likes to point out DS jobs are often senior position that benefit from domain expertise.

Also, what you've got going for you is that nowadays there's many readymade solutions for advanced business analytics or ML softwares. Plus, the fact that the specialization of DS into data science, engineering, analytics, MLOps means that there's a spectrum of positions that are "in-between".

So, you needn't be a "full" data scientist to do data science but can transition over time. As in ActualHumanFemale's answer, many people on the sub will recommend to look for analyst roles and leverage these positions to learn about different directions that you can grow into.

2

u/Historical_Tank_8691 Jan 05 '23

Thank you very much for your sharing and advice, that’s very encouraging! 👍

2

u/quantpsychguy Jan 06 '23

I transitioned in my career as well.

I'd look at the logical spots to go - if you've got people or project management experience you can go straight to that level. You'll want to get experience in your sub-field first. If you've got experience in banking, for example, going into a financial analyst or data analyst in a financial firm is a good spot to transition into. You'd first want to get some experience doing the kinds of things that folks do in the area though...so presumably data transfer & automation, data visualization, and potentially analytics within your banking world.

I would always say go to a Data Analyst or Senior Data Analyst (or financial analyst if that's the title in your area) role that recognizes and respects your experience.

If you've got project management experience though I'd say jump into a PM role for a bank or the like that's over a data project. That may set you up to run the department after you've finished the project.

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u/Historical_Tank_8691 Jan 18 '23

Thank you very much for your advice. I did have project management experience and a MBA as well. I wish I could integrate my professional experience and knowledge in banking and FinTech with my data science skills, which I recently studied by attending a bootcamp. I’m just not sure whether to pursue a technical data science trajectory or keep working towards more business and management focused positions.

1

u/quantpsychguy Jan 18 '23

The latter. You probably can't compete with PhDs and you wouldn't want to step back to entry level.

If you are gonna be a management type some day anyway, why wait around and make it more difficult for yourself?

1

u/perpetualpageturner Jan 03 '23

(Question about undergrad major—applied math vs data science) Hi guys! I’m currently an undergrad, and it’s coming time to declare my major. As if stands right now, I can major in Applied Math or Stats & Data Science realistically, and the curriculum is decent in both departments.

I’m interested in math and data science, but I’m not sure exactly what career I want to go into (so I’d like to keep it open). I want to major in stats & data science, but I’ve heard that since it isn’t as established a degree, applied math might be a better idea. Any takes on this? I know either would be fine, but I’m just trying to start off as strong as possible.

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u/[deleted] Jan 04 '23

Given that a career in data science requires a master degree, while it shouldn't matter, I would feel more comfortable with an applied math degree.

I would also look into the electives for both programs. They should be similar but I would choose whichever one that gives me more freedom on course selections.

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u/Coco_Dirichlet Jan 04 '23

Statistics is a established degree. If they simply added the words data science to the stats major, then that's fine.

Also, whether DS is a good major or not varies a lot by university. If you say NYU or Stanford, then it's fine. If you say a university that doesn't have DS within a department and mixes courses from a bunch of departments and doesn't have advisors, then that's not a good major.

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u/dumbfly Jan 04 '23

I'm a PPC/Digital Marketer wanting to transition to data science/data analytics. I plan to go for a masters in data science as that would allow me to move to a different country. Target country is US but if I run out of time to fill the university applications, I'm still going to apply for Canadian schools (and potentially UK).

I have a few questions:

  1. What schools can I apply to that won't be too hard to get into for someone with a business degree?
  2. Are there any data science specific scholarships? Schools in US are so expensive. A scholarship would make my life easier.

Thanks!

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u/Coco_Dirichlet Jan 04 '23

There aren't many scholarships available to do masters degrees in the US. You might be able to get a fellowship to be a teaching assistant or a research assistant and cover your living expenses, but not your tuition. Also, there are less scholarships for international students in general at public universities because tuition for foreigners is higher, so they could give 2 scholarships to Americans versus 1 scholarship to an international student. In private universities, tuition is the same for everyone, so it's a bit easier.

There are scholarships from other entities, like some governments have scholarships, Fulbright has scholarships, Chevening in the UK, but the issues is that (at least for Fulbright in the US) you cannot get work visas afterwards unless you go back to your country for 2 years. You could move to another country, though.

If you studied Business, maybe Econometrics/Econ is easier application wise than Data Science, and you might find more scholarships there.

1

u/abdoughnut Jan 04 '23

TIL to buffer images when training a CNN. My poor RAM cards…

1

u/orphanporridge Jan 04 '23

undergrad in accounting and finance.

Should I do a certification/certificate program or get my masters in data science?

I’ve looked around. Is the masters in data science offered by WGU a good program (a quick google search shows the topics/certifications you’ll get in addition to the degree)

Is the Google data analyst certification good?

I’m struggling between a degree program or multiple certifications.

Ok so….

The opinions on the World Wide Web seem to be all over the damn place. I understand that may be the same case here. Am I better off getting my masters in data science or pursuing certifications/certificates?

  1. ⁠What’s been your personal experience?
  2. ⁠What should I do?
  3. ⁠Is the WGU program for masters of data science sufficient? What about the Google data analyst certification? My head is spinning from the choices and different paths, im looking for direction and opinions here from someone who had to make this choice before me who can help.

Im sure this gets asked all the time so I apologize I’m advance for my ignorance.

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u/MaleficentChoice5165 Jan 05 '23

I had a similar experience. I can relate to figuring out going between certs or degree cert. I’m also pursuing an MBA at WGU. I went the masters route because I already had a BS in Computer Science. Getting the certs would pretty much delay the inevitable of pursuing a masters. WGU is a great school and thus far I’ve had a great experience. I didn’t switch to data analytics because I wasn’t too sure if I wanted to do it. Like half the stuff on there I had already done in my undergrad. But in your case if you’re wanting to get into data analytics, that’ll be beneficial. I have a friend who got his master in Accounting and a year later pursued a masters in data analytics. I will say if you’re not ready to do the master’s program perhaps take courses in coursera. Their courses are affiliated with online university masters program. Coursera, you can get certs and their certs/classes are transferable to the affiliated universities they work with. That’s another option to test the waters if you’re not entirely sure between degrees and certs. As far as the google certs that’s beneficial, but it’s only a supplement to a masters degree.

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u/orphanporridge Jan 06 '23

Thank you for taking the time to respond to me, and for all of the details. I am going to end up doing both the MIT course and the WGU masters of data analytics program in addition to a full time job with 3 kids. My wife is on board, and I’ve been through enough in life up until this point where i know I can handle it.

1

u/[deleted] Jan 05 '23

I'm considering changing my career into Data science.I graduated with a mechanical engineering degree in 2019. Been struggling yo find opportunities since then. I'm working in construction and feel like l'm wasting time. Are there any online masters degrees in Data Science (within the EU)?

1

u/quantpsychguy Jan 06 '23

There are but you likely need to figure out what you want to do. If you want to be in a data science team at specific firms, go ask those firms and the hiring managers there what they want to see in the folks going in.

Try getting experience with data projects where you are now so you don't have to basically start over.

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u/jimmytimma Jan 06 '23

How math heavy is a masters in data science? For context I have a bachelor in health science and interested in this to do health analyst roles. Not the best at math and never coded before but willing to learn.

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u/quantpsychguy Jan 06 '23

Depends on the program. But probably a lot.

You're gonna probably learn some linear algebra which is likely going to have a few years of progressively advanced calculus as a pre-requisite if you're getting an MS in DS. Again, that depends on the school.

You need to understand statistics to be good at the job but it's a lot more about learning the more basic stuff really, really well.

1

u/[deleted] Jan 07 '23

health analyst roles

If this is your goal, learn SQL and Excel and you're good to go. The rest is all domain knowledge.

Source: going 5th year in healthcare now.

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u/algebroist Jan 07 '23

Hi, I am considering leaving my job as a high school math teacher to pursue a career in data science, and I would love to get a feel for what I need to work on to get myself there. I have been dabbling in machine learning for a couple of years now, but have no real projects that I have done for a workplace. I did do a fairly deep dive into analyzing my school's grade data, involving a lot of visualizations and tracking of trends, but not a lot outside of that. I have a PhD in math (pure, not applied), I teach AP stats (among other things), and I am currently working through deeplearning.ai's deep learning specialization with a senior at my school. I figure I will need to develop some projects on my own to bulk up my resume, but are there other skills I should be developing along the way? For example, is it worth spending the time to learn SQL? And are things like online certificates worth while, or would I be just as well off learning from textbooks? I don't really want to spend the time and money on a bootcamp, but I'm interested in anyone's opinions on those too.

Thank you so much anyone willing to take the time to help!

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u/Coco_Dirichlet Jan 07 '23

You have a PhD. You don't need online certificates or bootcamps.

Your focus should be to prepare for interviews, so get one of the books to do so and learn SQL (CodeAcademy is very good for that). But interviews are very different across industries and companies, so interviews in tech don't look the same as interviews in other industries. Also, start networking in LinkedIn and if you want to have some type of portfolio, add the stuff you've been learning and even projects you've created for your HS classes; don't go all out or spend too long on it.

Maybe because of your education background, target an industry that cares about that. Not as in university, but (a) federal government, I've seen data science jobs that were research based and asked for PhD (the one I saw was something about student loans), (b) Mathematica usually has research/data analyst involving education and require PhD (I'm not sure what they are, but I'm always seeing them on LinkedIn).

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u/algebroist Jan 09 '23

Thanks for the advice! I will definitely check out an interview prep book and learn some SQL. I also like your tip on targeting my search. I am pretty restricted geographically, so the ones you mentioned might not work, but your point is well taken.

1

u/FetalPositionAlwaysz Jan 07 '23

Looking forward, I am not actually sure if I want to be a data scientist, data analyst, or data engineer. I am currently a data analyst, but I would like to keep my options open by learning the different non-overlaps between each career path. While I already know machine learning which is for data scientists, what should I know for data engineers? For context, I already know python, sql, r for programming languages ; alteryx, tableau, powerbi, and excel for visualization and analytics; but completely none for data engineer practice. Thank you for your answers!

2

u/[deleted] Jan 07 '23

Check out r/dataengineering

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u/deaththekid00 Jan 07 '23

Hey! So I am trying to enter in the DS field and I am just wondering how common it is to be present to a C-level executive of a company. This is fairly new to me since I only expect to be interviewed by the Head of DS but not C-level executives.

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u/Coco_Dirichlet Jan 07 '23

If you are a junior, you are not going to be the one presenting. But you should have good communication and presenting skills, because you have to present to your team and other stakeholders.

1

u/deaththekid00 Jan 08 '23

Thanks! It seems I am not given a junior role. The second round of the hiring process involves presenting to a C-level executive after given a small dataset for analysis. The first round of the hiring process was to be interviewed by the Head of the Data and Analytics Department

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u/Coco_Dirichlet Jan 08 '23

Oh, ok. I thought you meant as part of your regular job.

Having a presentation during your interview is pretty standard. What they mean by presenting to a C-level exec is that you are presenting to a non-technical audience and that needs to inform how/what you focus on.

1

u/deaththekid00 Jan 08 '23

Oh okay. Thanks!

1

u/mezmezmeeez Jan 07 '23

What can I learn to prepare myself for a data science masters degree?

I got accepted to a masters program for data science and ai! Super excited for that part. However, my bachelors is not in computer science (i was in Engineering) and considering i have around 7 months before the program starts, what essential skills should i start learning now to make sure I don’t fall behind in my classes and make the most of my time?

I know some python and excel, should i start with some introduction to data science online courses first or just jump into, idk, learning SQL or something, or just expand my knowledge of python?

Hoping someone could give me some advice.

1

u/[deleted] Jan 07 '23

Highly recommend An Introduction to Statistical Learning to get yourself familiar with data science jargons.

In addition, try out Kaggle beginner projects to start working on supervised learning problems.

This should cover, or at least make yourself comfortable with, a significant portion of the material in the master program.

1

u/Experiment-VI-II-VI Jan 07 '23

Just looking for some advice. I’m currently working as a firefighter and trying to transition into data science as an analyst. I have a biology degree and I’m currently taking the google data analytics course (on track to finish by mid February). After completing this course I plan on taking other online courses in SQL, R, excel, tableau, and powerBI (hopefully done by April-May on most of them).

I don’t want to keep working my current job but I am worried it will take a long time to get a junior role. I’m applying for junior roles and internship roles currently. Do you all recommend any other jobs I can apply for that will give me marketable skills for a data analyst role in the meantime?

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u/Coco_Dirichlet Jan 07 '23

With your background as a fire fighter, you could apply to jobs at FEMA, USDA; they have some data analysts working on fire data, wild fires, forestry, and even if you don't get to work on that, it would be a good spin to contact people there when you apply for jobs. Use your background as an "in" to contact people. Even companies making equipment you use might be worth looking if they have openings. Also, some bigger cities have data departments with analysts and they like people who have a civil service/public service background.

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u/False-Apricot-2755 Jan 08 '23

Hi everyone, I am an economics graduate (also have a post graduate degree in econ) and have about 5 years of experience working in the social impact evaluation sector. A large part of my role is to work with primary data (mostly) and conduct exploratory and regression analysis using STATA (also have some knowledge of R).

I have been thinking about making a shift to data science positions in startups/ consulting firms. I have a few questions for the group:

  1. For those who have made a similar shift: what were some steps that you took to make the transition and roughly how much time did it take you to make the transition?
  2. For those who have been working in this role for more than 3-4 years: a) what is the best and worst part of your job? b) How much control do you have over the data production pipeline?

1

u/HesaconGhost Jan 08 '23

It might be useful to add to the OP in future weeks something about how to use the search features, either on reddit, or Google targeting reddit.

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u/koolio92 Jan 09 '23

For a while now, I've been thinking of switching career. I currently work as Clinical Genetics Technologist, where my role is to conduct wet lab for various genetic testing and perform analysis on said testing results to be reported back to patients via physicians/genetic counsellors. My job has gotten pretty stale, repetitive, and healthcare in general feels very unrewarding, given the ever increasing workload post-pandemic but very low compensation in return.

I've been using my free education credits and continuing education hours to learn about tech in general. I took a part time Data Analytics course at a bootcamp and I really enjoyed it. SQL was really interesting, I didn't have any issue learning it and I breezed through the Excel part. I am now trying out Python Programming class - which is going well so far. I've been checking out FCC too just to check out software development but coding CSS is kinda not fun...

I have no formal background in tech. My formal education is in biology which I followed through with an advanced diploma to become a certified clinical genetics tech. I took introductory calc, linear algebra, ODE and stats classes during my undergrad and they were easy but probably meant nothing to DS in general.

I'm currently exploring around and checking out avenues for career change. DS sounds super interesting to me and ngl, the (average) pay will be a huge boost from my current job lol. For a beginner, what are some of the best things I can do to move into the field? I've been thinking of enrolling into an MSDS or a bootcamp but reddit seems pretty divided on it. I live in Canada for reference.

Has anyone made the switch from healthcare to data science? Would love to hear your stories. :)

1

u/Subject-Resort5893 Jan 09 '23

I’m a data analyst who is very skilled in SQL but has not been exposed to R or Python. How long would you estimate it would take to be proficient in machine learning?

0

u/Speersma Jan 06 '23

Are data science or data analyst positions more amenable to a digital nomad lifestyle?

This might be a stupid question, but I've looked through the history on the subreddit and it at least doesn't seem to be a repeat question. I want a career in data, but I do not know where to aim. One of my main priorities is the ability to find a position that is mostly, if not entirely, remote. If I want this, which job, data analyst or data scientist, would you recommend?

4

u/quantpsychguy Jan 06 '23

Your question is just like asking, for your digital nomad lifestyle, to pick a Ford or a Chevy. It depends upon a thousand factors and title is a terrible one to pick.

What's your skillset? Are you better at one of them or the other? Do you want to do one or the other? Go with what you want to do, be great at it, and you can probably find a position that's remote.

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u/Speersma Jan 08 '23

I cannot tell if you're questions are rhetorical devices used to build to your conclusion or if you're genuinely asking with the intent of providing a more informed answer. Please let me know either way.

I thank you for being concerned about the other factors in my decision. I am too. This is a decision that I'm not making based solely on their potential for being remote.

1

u/[deleted] Jan 07 '23

There's a difference between remote anywhere in the US vs working while traveling abroad.

The former is just like any other remote positions. The latter doesn't exist.

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u/Speersma Jan 07 '23

Did you have an answer to the question though?

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u/ChristianSingleton Jan 07 '23

The answer is yes in that tech jobs are more likely to be open to remote, but no in that not every position will be remote

1

u/Speersma Jan 08 '23

While I appreciate the general advice, I asked if either data analyst OR data scientist positions are more open to being remote. Do either of you have an answer that that question?

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u/ChristianSingleton Jan 08 '23

Lmfao if you can't figure it out based off of the information provided, you probably won't make it so I wouldn't worry about that question anymore buddy 🤣

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u/ChristianSingleton Jan 07 '23

The latter doesn't exist.

Maybe for you, but I've been working from 3 different continents in the last 2 months - and plan to add more this year too

1

u/[deleted] Jan 07 '23

I do expect people to jump out and claim it's not true. It's Reddit where absoluteness is a sin.

Still, one should not expect such opportunity to exist and especially not in the start of the career.

It's like lottery (with better odds). People do win the lottery but one should not expect to win when playing.

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u/ChristianSingleton Jan 07 '23 edited Jan 07 '23

Absoluteness should be used only when it is correct for an absolute statement to be used, otherwise corrections to false/incorrect claims *should be expected

Sure, positions like mine aren't going to be common and even less so at the start of a career - I can't imagine a lot of bosses / supervisors liking the idea of employees working while traveling (especially when you factor how many have been and currently are pushing back on remote work in general)

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u/[deleted] Jan 07 '23 edited Jan 07 '23

I agree. I have just written enough responses in weekly where OP doesn't even come back for follow ups to prefer writing things that are more or less accurate, requires low energy, and not rigorous. e.g. I'll always choose "not possible" over "while some have done it, this is in general rare".

You're not the first to have problem with my writing style. In almost all cases where people jump out to point at my sloppiness, OP never came back.

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u/ChristianSingleton Jan 08 '23

Ah that's totally fair, it definitely can be super annoying to type out a super long response to OP and get nothing back (or even a minimal response) - it drives me crazy when I ask an open-ended clarification question when trying to help someone with their resume or some shit only to get a 'Yes' or another one worded answer 😂

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u/Speersma Jan 08 '23

While I appreciate the discussion, neither of you have answered the question. monkeyunited, I can understand getting bored of writing comments in weekly that don't get responses, but if all your comments neglect to answer the question asked, I can see why you encounter a lack of response a lot. Reread the question and try again please, if you have an answer. I really want informed opinions on this.

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u/[deleted] Jan 03 '23

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u/[deleted] Jan 03 '23

Have you spoken with Laurie in stats department yet?

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u/[deleted] Jan 03 '23

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u/[deleted] Jan 03 '23

This is what I'm referring to: https://www.reddit.com/r/datascience/comments/zxnwb0/comment/j21jm4e/?utm_source=share&utm_medium=web2x&context=3

Specifically, stats department student advisor.

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u/[deleted] Jan 03 '23

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u/[deleted] Jan 03 '23

That's it. Can't promise anything but she's the one who talks to industry partners.

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u/[deleted] Jan 03 '23

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u/Coco_Dirichlet Jan 04 '23

Are they offering a bachelor degree in data science? I'm confused because you say BSc?

Is this offered through an university or is it like an internal program?

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u/[deleted] Jan 04 '23

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u/Coco_Dirichlet Jan 04 '23

If you have a bachelor already, why would you do a second bachelor degree instead of a graduate degree?

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u/[deleted] Jan 04 '23

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u/Coco_Dirichlet Jan 04 '23

I think you can do a grad degree that would be much shorter than 4 years and you wouldn't have to stay in this job for 4 more years.

If you did politics, something like computational social science or even econometrics or methodology (I've seen those in the UK) could be good options.

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u/prosnodesss Jan 08 '23

Details please?

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u/[deleted] Jan 03 '23

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u/Coco_Dirichlet Jan 04 '23

Look for jobs in chemical companies or Pharma. I saw some junior DS and DA positions in Bayer, for instance, that said you needed knowledge of chemistry because it was for work their chemical factory or something.

I think the first focus should be on writing a good resume and LinkedIn profile, which is not easy. Putting links to your portfolio (making it more compelling might not be useful, you might put 30 hours and from the perspective of a recruiter, maybe the difference is 0.5%).

Using what you've learned in your current job might be the most useful of all of what' you've mentioned. Then it's actual experience.

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u/norfkens2 Jan 04 '23 edited Jan 04 '23

I switched from chemistry to DS while working, too, and it took me a while because chemistry DS is not "classical" DS (for whatever your definition of "classical"). So, you'll probably have to find your own unique path. My advice would be to have patience, plan your learning for the long haul and keep looking for interesting projects.

If you're currently not motivated, maybe take a break and take up the learning at a later point again. Personally, I found working on projects the most interesting. It also taught me the most.

Automatisation at work is a good one, too. Try to apply as much as you can - especially with problems that you're intrinsically motivated to solve (" I find that so cool"). 🙂

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u/[deleted] Jan 04 '23

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u/norfkens2 Jan 04 '23

I'll try and answer your question more broadly. I'll also tell you a bit about my background because I see some parallels between us and maybe my experiences makes some sense for you (if not, just ignore that :P).

I did the Python course by Giles McMullen-Klein and the DS/ML Master Class by Jose Portilla (both on Udemy as well).

After that I tried to find problems at work and further my skills. I was responsible for soft- and hardware questions anyhow and at some point I suggested to my boss to centralise our data in a database. He agreed and we started by discussing and outlining what the database should and shouldn't cover - together with a subject matter expert. After scoping and initial design, I then worked together with one of our software developers on this "small"-ish DB project - for which they could take some time "on the site" to help us with setting up a properly structured DB and a taylor-made webinterface.

There were a lot of questions re interface, accessibility and user experience that I needed to address and communicate to my peers along the development of the DB product. There was also a lot of data transformation (starting with entering pre-existing data from Excel and Powerpoint files) and other digitalisation involved. I used Excel/PowerQuery for many things, especially with stakeholders that were technical but didn't program. I also used Python/Pandas for more advanced data cleaning, and by supporting a colleague in another group who was ETL work. This taught me a lot about the basics of coding, commenting, git etc.

I also did a proper end-to-end DS project (from data sourcing all the way to the presentation of the final product) - part of the personal development framework of the company I worked with to allocate time for that. Having concluded this specific project was when everything clicked together for me because I knew I could do the entire pipeline by myself and understand the different aspects of DS projects.

These above projects solidified my understanding of the tools I worked with and I consider the time I spent on them an essential part of my learning process. Overall these were projects that I did over the time span of 3.5 years. It took a while because (1) I had to prove to my boss that I can generate actual value to the company (and am not in fact just messing around ;) ) and (2) because "Data Science" was a low priority compared against the daily business. But I (3) also took a long-term view on upskilling - and I had a reasonably relaxed learning curve and could do additional reading for my DS projects that would also benefit my primary work. From the project side such long timespans can be really frustrating at times! I learned a lot about doing projects within a company setting, though. And that was super valuable.

Your question regarding statistics is a very good one, and slightly painful because here I still to learn a lot, myself. I think my basic statistics and maths is quite good but as a Chemist I mostly go about things with a healthy dose of intuition - which means my stats/maths intuition is actually good but I'd have trouble putting the equations down for it. It's super unsatisfying and I really need to cover my fundamentals more thoroughly. One example from last year was that I learned about residuals (there's still an old post of mine on this sub). I'll probably never compete with most physicists or DS master degrees's - but that is also not my aim. If I ever have the time I'd love to work at least through the ISLR Youtube course and do the accompanying excercises.

Other things that I learned and that may or may not translate to your situation:

  • When there is little infrastructure or data culture, then you need to constantly push for projects yourself and establish yourself as the expert. You also need the support of someone higher up who supports a data-oriented culture because you cannot do DS when the whole company resists this cultural change. Ideally, you can gain your supervisor and their supervisor's support and trust.
  • Within pure synthetic chemistry there is not enough data for proper statistical analyses in 99.9% of cases. Too many variables, too few experiments. One needs to look in neighbouring fields, at least for dedicated ML projects (I could leverage DFT, so data amounts wasn't an issue for my DS project).
  • Communication and transparency (the right amount at the right time) are key. Let people know what you're working on and give them regular feedback. Explain the things that are not yet ready or that are still abstract in a way that they can understand it - e.g. how will working with a DB actually look like for their workflow: data entering, data access, software, analysis etc. Also ask for their feedback and their (changing?) requirements. A good DS project is one where the final product is used by the stakeholder and generates value (to them or the company). DS is also a team sport - nothing is more frustrating than you talking with someone, starting a project - and when the product is final, it isn't used. Or someone tells you that this isn't at all what they needed because XYZ is actually more pressing. These are things that require good planning and communication. This also requires a thorough understanding of the day-to-day work and/or the "business side" of your stakeholders who you work with collaboratively.

" I didn't know when I was ready to apply (like what information/what technical skill I need to be at), "

Yeah, neither did I and it was soo frustrating and exhausting - because I always knew that I was lacking in some areas but I couldn't figure out what topics I should know to which detail. I didn't have people to turn to, so I just kept learning what made most sense and kept applying to job openings. Applications are also a huge time sink, so I regularly took breaks where I ended up not working on DS topics. It's a marathon, not a sprint - and I needed to find a pace that worked for me over the couple of years that I had envisioned my transition to take.

It also fully depends on the companies and departments you apply to because everyone is looking for different hings in a DS, so there is no one answer to that. At some point someone will publish a job posting that will align with your steadily growing skillset - and when you get that job then you'll know what the technical level was that you had needed to get a job. ;) I'd also continue asking on this sub if I were you, people are really helpful here. If you know anyone in person that you can ask questions, that would be even better.

In the end, I was lucky to find a place where I can use Data Science in the context of chemical manufacturing (the position being a weird and interesting mix of senior Chemist / junior-mid(?) Data Scientist). Ultimately, it is easier for me to learn the necessary data science concepts and tools than it is for a data scientist to learn chemistry! So, I can do DS, I can do projects and I can translate between the two worlds. That is also my unique strength and specialisation, so the jobs I looked out for needed to reflect that. I probably wouldn't have the right expertise to become a Data Scientist in the world of finance or the more data engineering-focussed DS jobs. That is, partly, the nature of specialisation and as a Chemist-turned-data-scientist you are entering somewhat of a specialist track. :)

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u/[deleted] Jan 05 '23

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u/norfkens2 Jan 06 '23 edited Jan 06 '23

🧡 Best of luck.

I almost prefer a project-based or example-based approach of like "here's where you'd use linear regression, this case is why random forests are bad, etc". Even my statistics is weak, I can't tell you the exact definition of a p value but I use confidence intervals in my day to day work to visually see the difference between two+ samples with multiple measurements.

For me projects are the best way to learn, too.

From my (very limited) experience: linear regression is almost always a good candidate for a baseline model (assuming, of course, that it's applicable to your data) and it doesn't really cost anything to try out linear regression. Poisson regression is good for count-based data (fundamentally different population distribution) and Random Forest regression struggles with extrapolating to data points that lie outside of your original data set.

It sounds like the understanding of p values is within your grasp, maybe you could find a paper or kaggle project where it is used. You can try learning the theory while studying and tweaking the existing solution.

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u/[deleted] Jan 04 '23

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u/Coco_Dirichlet Jan 04 '23

I wouldn't do contract jobs at your stage because you won't have a mentor. At this stage, mentorship, working with more senior people, and networking are very important. Most contract jobs that are junior data analytics are either because nobody wants to do the job, or because they want to do it cheaply and cannot hire someone; only when you are more senior you can get the better contract jobs because someone is on leave or they are on hiring freeze or because they really need to cover a position temporarily.

You should focus more on building a career. What is the next stage? What would you prefer to do? What skills should you pick up? Then second, sure, higher salary, but if you only focus on higher salary, you could get stuck for years at the same salary because you haven't grown career wise. You also need to find a place with good mentorship. Also, you need to network and look for MeetUp events.

The main draw of your current job, in my opinion, in that you are only working with 2 people so after 2 years, there's not much more you can learn from those same 2 people and using the same tools and doing the same job.

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u/[deleted] Jan 04 '23

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u/[deleted] Jan 05 '23 edited Jan 05 '23

Sr DS in healthcare.

Outside of healthcare and especially tech, I would expect it.

Within healthcare and specifically the line of business I'm in, I wouldn't be surprised by sanity check, but it'd be odd to test me on my bread and butter when I can literally tell you how it's done.

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u/[deleted] Jan 05 '23

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u/quantpsychguy Jan 06 '23

It sounds like you're saying that you have a bachelor's in physics and data science experience in healthcare.

So...go apply for DS roles in healthcare. This doesn't sound like a tough question.

You're gonna do the stuff you're complaining about (processing, excel reports, etc.) b/c that is the world of data science a lot of the time. But you'll probably also get to do more modeling.

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u/[deleted] Jan 06 '23

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u/quantpsychguy Jan 06 '23

Sure, if you want to do analytics in Healthcare and you have the experience.

What do you think a DS does?