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Is studying Data Science still worth it?

reddit.com › r › learnmachinelearning › comments › 1hc4jdd › is_studying_data_science_still_worth_it

Even with how the industry is changing, I still think data science is a good career path. The role is evolving, with more deployment work handled by ML engineers now, but there’s still strong demand for people who can work with data, analyze it, and understand real-world problems. What matters most today is learning practical skills, not just chasing buzzwords. If you’re just starting out, structured learning really helps. I found this article useful because it explains IBM’s Introduction to Data Science course on Coursera in a simple and easy-to-follow way. Answer from khureNai05 on reddit.com

reddit.com › r/datascience › why did you choose data science as a career? what's your daily life like? did you regret it?

Why did you choose data science as a career? what's your daily life like? did you regret it? : r/datascience

January 13, 2024 - Data science sits at the meeting point of statistics, software engineering and subject matter expertise. To become a (good) data scientist you should be a crack in one of the three fields and be passably good in at least another one - depending on the data science seniority and the company ...

reddit.com › r/learnmachinelearning › is studying data science still worth it?

r/learnmachinelearning on Reddit: Is studying Data Science still worth it?

December 11, 2024 -

Hi everyone, I’m currently studying data science, but I’ve been hearing that the demand for data scientists is decreasing significantly. I’ve also been told that many data scientists are essentially becoming analysts, while the machine learning side of things is increasingly being handled by engineers.

Does it still make sense to pursue a career in data science or should i switch to computer science?
Also, are machine learning engineers still building models or are they mostly focused on deploying them?

It's pretty much the only industry that has a future, don't listen to the lies on the internet, the layoffs are mostly due to a natural downturn in the market, it's been happening forever and it happens in every industry, only in tech you have almost guaranteed recycling, as opposed to others who once caught up in the crisis had no recycling

Even with how the industry is changing, I still think data science is a good career path. The role is evolving, with more deployment work handled by ML engineers now, but there’s still strong demand for people who can work with data, analyze it, and understand real-world problems. What matters most today is learning practical skills, not just chasing buzzwords. If you’re just starting out, structured learning really helps. I found this article useful because it explains IBM’s Introduction to Data Science course on Coursera in a simple and easy-to-follow way.

reddit.com › r/datascience › is studying data science still worth it?

r/datascience on Reddit: Is studying Data Science still worth it?

May 24, 2025 -

Hi everyone, I’m currently studying data science, but I’ve been hearing that the demand for data scientists is decreasing significantly. I’ve also been told that many data scientists are essentially becoming analysts, while the machine learning side of things is increasingly being handled by engineers.

Does it still make sense to pursue a career in data science or should i switch to computer science? I mean i dont think i want to do just AB tests for a living
Also, are machine learning engineers still building models or are they mostly focused on deploying them?

deploying models into production is definitly being taken over by engineers but I've seen several data scientists transition to that side of the house. data science, comp science, stats, engineering, it really doesn't matter that much. Your first job matters waaay more

I don’t think demand has decreased significantly, although competitive, the mid level market is decent. Entry level is just saturated because people were made to believe they can get into the industry with a boot camp or some self-studying. In reality, every single DS coworker I’ve had pretty much have at least a masters in a quantitative field, many PhD’s. It’s still a lucrative industry if you have the proper education to get in. As for the analytics vs ML. It’s company and team dependent and really up on the individual to navigate this nuance. Some DS do end up becoming MLE’s. I used to imagine my DS career will be just training fancy models everyday but at this point I’ve done a bit of everything and I’m open to learning whatever to drive impact for the business. Can’t optimize you job for everything and all else considered, DS is a pretty great industry to be in. Even among DS, only a small percentage of us get to work on the latest fancy AI models, and that’s ok.

reddit.com › r/datascience › is data science a good career path for me?

r/datascience on Reddit: is Data Science a good career path for me?

June 26, 2023 -

for starters i have yet to start university.

Over the years i exelled in math and even took a several college math courses. I disliked how presice college math was and really started gravitating towards statistics more. which was my main interest even before i took those math courses.

as for coding and i cant say i like it as much as i tolerate it. i find it to be extremly degrading and borring. the worst is having to learn more than one programming language. as for my understanding jobs like swe will have you learning alot of languages to put in your cv and sometimes have you learn languages as quickly as possible durring the job. I also understand that DS's dont really have that. i hear alot of people on here mentioning that only python and maybe one or two more languages. I am fine with that as long as i am not expected to learn more. or expected to be coding more than 40%.
ideally I would be codding at the job at least as possible but 10% to 20% would be great too.

I understand that cs and DS are big fields. contracting as much important info from the data as possible wtih anaylitics, a/b testings, metrics. or some people like to call it aggregate/label seem like the best for me. i am eager to see if you guys agree or not.

For a guy like me do you double majoring in cs and statistics or majoring in statistics/maths and minoring in cs are more vailable options. since if i dont get a job that invloves little codding but more statistics/analytics like:

data science
quantitive finance
quant research
graphics (more math statistics)

i cant see myself becoming a SWE i would rather get the lower pay as a financial analyst or statistion.

Honestly, you won't know until you try.

My view is, to pursue a auccesful career in data science, you need to love coding. It is a core skill of the job and if you only 'tolerate' it you're going to be a very miserable data scientist. Something along the lines of operational or quant research may be more up your street. It leans heavily on stats and modelling but not necessarily on coding. I would also ask what language have you tried? Some high level languages like Python and R tend to be the ones used in data science and are easier to get into than something like C++ which (for me) is quite impenetrable and are more used in SWE than DS.

reddit.com › r/datascience › is data science a bad career long-term?

r/datascience on Reddit: Is data science a bad career long-term?

September 20, 2022 -

I work closely with professional engineers (electrical, mechanical, etc.) and am struck by how much practical experience is valued in their profession vs ours.

The 20 year engineers probably couldn’t integrate a complex function to save their lives (all the math they do is abstracted in their software systems) but the deep experience and wisdom they bring is highly valued. If one of those engineers was quizzed on undergrad math in an interview I am certain they would just walk away. And these guys are designing bridges, maintaining critical infrastructure, and keeping the lights on etc.!

(On a side note, I can’t help but note that no one is talking about how software that does engineering workflows will automate away the engineers…)

What is it about data science that causes companies to put experienced candidates through the ringer on undergrad level topics that don’t (in my experience) test for accumulated wisdom and ability to impact a company with data?

It is hard for me to imagine spending time brushing up on manual manipulation of equations or memorizing SQL trivia in 5-10 years. It’s not representative of the value I bring or what how data science can impact a large business. If the main skills companies select for in experienced data scientists can be obtained from a book or a YouTube video, or even demonstrated in a take home test, then it indicates to me that companies do not value experience and would just as well prefer a 3 year data scientist as someone who has 20. In fact, given the expected comp, it might be preferable.

Hoping to build out a long term career here and wondering if this is not the field to do it.

as well prefer a 3 year data scientist as someone who has 20 Good news is I'm willing to bet the 20 YOE one is still highly sought after just not for the role data scientist. We don't even need to go 20 year. I'm in a niche space of healthcare for 5+ years. The interviews I had focused more on the actual business problems than math and stats. However, if I apply for a data scientist position in a different field, I get asked on mostly math and stats.

Your whole argument is all over the place. The title is about data science as a career but then the text is about how you don't like the interview process. And it's apparently a bad thing that people can learn skills from books and videos? But it's a good thing when an engineer doesn't remember basic math? I have no idea what you're even trying to say.

reddit.com › r/cscareerquestions › is data science really the future? is it worth pursuing?

r/cscareerquestions on Reddit: Is Data Science Really the Future? Is it Worth Pursuing?

November 29, 2024 -

I’m still deciding on which degree to pursue and doing some research before I commit. Everyone keeps saying that data science is the future and will be in high demand, but I'm hearing mixed opinions. A lot of students are shifting their focus to data science, but there’s also a lot of competition.

I’m planning to pursue a master's or PhD abroad after my bachelor’s (US or Europe), but I want to know: Is data science really the career of the future? Will it be the highest-paying job in the coming years, like some YouTubers claim? Is it really as big of a deal as people make it out to be?

I’d appreciate hearing your thoughts, especially from those already in the field.

I'm a Data Scientist on paper and all I've been doing in the last 2 months is traditional software engineering applied to chatbot development. There are less DSs than there are engineers, but the need for them is much lower as well. Also, youtubers hyping the market can go eat grass. If all you're concerned about is some career, my advice is to follow a MS only if you can't land an internship or a first job out of college. And I'd favor Machine Learning MSs over DS if you are able to - they are much harder to land currently for a reason, but in case you can't, DS programmes are a nice alternative.

34 year veteran here. It’s just my opinion, of course. It sounds like you aren’t thinking about your career in a useful, yet. “There is no future but that which we create.” generally applies along with “nothing is permanent except the certainty of death”. You are going to have to reinvent yourself about every 3 to 5 years because of technology shifts. So asking “is data science really the future” is missing the point. There are core skills, industry skills, and context skills. Core skills are skills that last a lifetime and underpin everything. Examples are: Communications Business analysis Critical thinking Systems thinking Algorithmic thinking Interpersonal and relationship management Technologies will come and go but these will always remain central to your career. Industry skills begin to matter after the first three years because they are the deep industry implicit knowledge that differentiates you from a computer that can do task mimicking. Then there are context skills. Maybe data science is one set of skills that is MORE important now than other skills, but that will change and something else will be more important than Data Science. Context skills are interesting because you always need them but their emphasis in the job market is always temporal. Which leads me to the wisdom of the most important core skills: reinvention. And then there is one caveat I put on it all: if all you care about is getting a job, you’re likely focusing on the wrong thing. Also, if you are worried about hyper-competition like what happens in places like India… be a contrarian. Go where there is demand but lacks competition. And ONLY go where you have passion about the work, have SOME but not all skills required, and where you have identified what others see in you as your potential. This means finding mentors that can see you the way the employer sees you instead of how you see yourself.

reddit.com › r/datascience › is datascience a career worth investing?

r/datascience on Reddit: Is datascience a career worth investing?

September 30, 2018 -

I always been interested in statistics way before the trends of data science. collecting data, and turning them into insights and predict behavior is pretty rad to me.

However I feels that datascience/statistics has been bastardize with the trends.

I have work as BA working with SQL, and from my experience and what I've heard most data driven career datascience/BI/Data Analyst just played with excel and build nonsense dashboard and reports that no ones reads.

People says that there is need for qualified data scientist and I assume this qualification does not comes from taking random courses online, but years and years of career experiences. But when the market is flooded with people who wants to pursue datascience career, how do a newbie standout?

Also do we really need that much data scientist? And is there any other data driven career that is not datascience/BI?

from my experience and what I've heard most data driven career datascience/BI/Data Analyst just played with excel and build nonsense dashboard and reports that no ones reads.

"Data science" is a buzzword for anything data-related. Personally I'm not really a big fan of it, because people will call both some simple reporting as much as advanced modeling: "data science". If you like stats and modeling then there's plenty of interesting jobs, but you need to be careful with picking the right position and not get fooled by marketing buzzwords like "data science".

But when the market is flooded with people who wants to pursue datascience career, how do a newbie standout?

Indeed, the market is flooded by people who "want" to pursue data science career. In order to stand out, it's nice to actually know why would you want such a job. Being good at statistical reasoning is a great starting point.

I'm a mathematician. I see these data science guys as basically engineering technicians. Businesses need people who understand the math. Things like knime and azure ml lab are coming. Basically take my data, connect the blocks for the algorithm you want to run, produce result.

I would guess most of the non-math data scientists who don't become business process experts will be taken out by things like knime.

Now if you have a math background it's not about what language or format, is about actually understanding the problem and solutions. That's something that isn't going to go away.

I rarely write code anymore, part of that is having been promoted to more of a leadership role and part of that is because my time is better spent advising business practices based on actionable Intel. The "data scientists" do my grunt work. Worse, many of them have no idea why they do what they do.

Hence, they're like engineering technicians. Technically qualified to run the machines. You'll even trust them a little more than the production staff. But end of the day they don't hold decision making ability because they know the how, not the why.

My best career advice is math, math, math then more math. I taught myself SQL, I taught myself python, r and sas too. That stuffs surprisingly easy to reach yourself, it also changes every 5-10 years.... See cobalt, c, c++, etc etc.. but know what doesn't change. Math.

You understand the math, and you understand the business, it doesn't matter what technology trend is thrown at you.

This is my don't be a data scientist even though I am one speech. But really, if you do it, just go get a ms in statistics, applied statistics or bio statistics.

Then you'll understand what's actually happening and won't be left with a useless degree when knime takes all the technicians jobs.

With that said, industry always moves slower than everybody expects. Realistically people are going to have full and bright careers with their undergraduate data science degree because nothing moves as fast as it ever could

So maybe there is value in getting in and making some scrill then moving in to management consulting or a higher degree

reddit.com › r/datascience › is data science still worth it in 2023

r/datascience on Reddit: Is data science still worth it in 2023

January 25, 2023 -

Hello I have a degree in physics and I want to learn data science is it still worth it in 2023 can I find a job after I finish my studies or this field is dying?!

It is certainly not dying, but it is certainly very competitive at entry levels right now

I would say it’s highly competitive now. 3 years back you could have gotten in with a masters degree relatively easily. Now it’s hard to get in even with a PhD. But totally worth it when it comes to pay. I would say software dev would be a better track. It’s relatively less exploration work compared to data science since software dev has predefined goals.

reddit.com › r/learnmachinelearning › is data science worth it in 2025

r/learnmachinelearning on Reddit: Is data science worth it in 2025

May 4, 2025 -

I will be pursuing my degree in Applied statistics and data science(well my university will be offering both statistical knowledge and data science).I have talked with many people but they got mixed reactions with this. I still don't know whether to go for applied stat and data science or go for software engineering.Though I also know that software engineering can be learned by myself as I am also a competitive programmer who attended national informatics olympiad. So I got a programming background but I also am thinking to add some extra skills. will this be worth it for me to go for data science?

Anyone who has any question about whether a field is worth studying or not needs only look at this: https://www.weforum.org/publications/the-future-of-jobs-report-2025/infographics-94b6214b36/

do math instead

Find elsewhere

Google Bing Mojeek

reddit.com › r/collegemajors › should i go into data science?

Should I go into data science? : r/CollegeMajors

May 23, 2025 - Data science is currently in-demand domain. Companies are looking for ways to get insights from the data they receive every minute and every day to use it for their own good. So, data science is a good career to choose for you.

reddit.com › r/datascience › i don't want to be a data scientist anymore

r/datascience on Reddit: I don't want to be a Data Scientist Anymore

June 14, 2023 -

Wanted to thank everyone for taking the time to read my thread.

I'm almost 30 years old, and have been in the same career for 8 years. I'm a "Data Scientist" with a Bachelors degree in Mathematics & a Minor in CS. My day to day as a Data Scientist is a collection of building/testing/implementing SQL procedures, fitting simple models (Linear Models, Regression Trees, etc.), implementing model results as live models, delivering analysis to decision makers & executives. I work for a major financial institution.

Here's the problem : I feel so disconnected & unpassionate about my job. It's partially an imposter syndrome issue. Looking at my skillset compared to other data scientists & those who are passionate about Data Science - I'm losing pace. My peers who care about this type of work are deeply involved in improved learning - new Python libraries, new modeling techniques. Everyone in this profession have a strong desire to learn & improve. This has really never been me. I was able to succeed in the mid to late 2010's being fresh out of school & having an "up to date" skillset ... but I don't think I have the drive or desire to do what I have to do to keep up in the mid to late 2020's.

I feel like I fell into this career out of the start of my career, but now a few months away from 30 I'm questioning if this is what I want my next 25 years of work to be. I have friends, family and colleagues who are passionate & driven about work. I feel like this was the career I fell into because of my Math degree. I really don't know what it is I'm passionate about, but working as a data scientist in the financial sector for the next 25 years isn't it (or maybe being a data scientist at all).

At the same time, I don't want to start my career fresh at the age of 30. I think I'm a naturally analytical person, and want to be able to carry over some of the skills I've picked up from the last 8 years into a new role, that might bring more passion & joy back into my job.

So, I wanted to see if others on this sub have gone through the same thing. If yes - what were some stories you have that I can use as inspiration? Are there career options out there for ex Data Scientists who don't want to be technical code monkeys anymore, that can leverage some of our analytical backgrounds to thrive?

Thanks, look forward to hearing some stories

Become a data scientist for something worth fighting for! I am only a data analyst, but I have a data scientist on my team who left a big FAANG company to come work for a utility company because he wanted his work to actually impact the world. Now we are working together to build complex models to prevent outages and wildfires in our utility systems. Not saying other companies in the financial sector don’t matter, but if we’re honest, the smartest people in the world, the best engineers are working on products and things are more geared towards profit, rather than impact. Consider joining a company where your expertise will make a difference! We need more machine learning in the utility industry.

I am a phd student and 30 years old. Feeling exactly the same. I lost my desire for my area at all. I might be unable to finish my phd. I dont also have a desire for anything else that i could earn my living. I am happy though. I am looking for how i could earn enough with least amount of stress/effort/working right now lol.

reddit.com › r/datascience › do you like being a data scientist?

r/datascience on Reddit: Do you like being a data scientist?

June 4, 2021 -

I have the option to study two different courses, one is to become a Data Scientist Jr and the other one is the Google Project Management course. Right now I work as a PM but I also know how to code, so I don’t know what path to pursue now, I mean I can improve my skills as a PM but in the other hand Data Science looks a very cool and interesting thing to study.

I have some experience coding with Python and Django, so I think my profile match, however I’m still wondering what to do... Do you guys like being data scientists? What do you hate about the role? Is there professional growth? Do you consider that the demand for your profile is increasing?

No. A typical "data science" role is sadly just glorified data lackey / SQL monkey these days. You learn lots of exciting things at school, only to never use them in practice - advanced stuff simply can't solve real business problems for 99% companies out there, all they usually need is simple dashboards. If you are any good at "solving business problems", you immediately become a "go-to" person and get swamped with ad hoc requests, leaving you with no time to focus on bigger projects / build stuff. This can be very stressful and lead to long hours. I transitioned into DS from a SWE role a few years ago (was a bit bored with backend development), and I bitterly regret it - at least I had some technical growth back in the SWE days. Pay is good, but again doesn't beat that of a SWE - and the market is much worse, good luck finding a job

Oh, hell yes. It's tons of fun intellectual challenges. I like coding, I like the "journalism" aspects of chasing down leads, bugging people for data, and trying to figure out what it means. I love that "open the envelope" moment of finding out something about the organization that nobody else knows. I love getting to talk to people way up in the organization and tell them what the data says the smart decisions are. Like software engineering, a lot of what you do goes to waste, but when you find out something really good, you can provide information that helps the top level people figure out what to do: invest in one training program, dump another. Investigate transactions that pop out of the decision trees to bring the hammer down on fraudsters or narcotics traffickers. Provide drivers with these kind of vehicles, and accident rates will drop. You can make recommendations that speak to how important it is to educate, motivate, and retain your people. I've gotten to brief C-level executives, answer queries from congress, and speak to very high level government leaders. I'm pretty gray and senior now, but I'm in a position where I just get to do data science -- a minimal number of stupid meetings, lots of don't-mess-with-me time, and my pick of interesting projects. I get paid really well for it. I have lots of autonomy, get to constantly learn new stuff, and have loads of autonomy. Two thumbs up from me.

reddit.com › r/datascience › fyi: if you're new to the industry, the data science job market is saturated

r/datascience on Reddit: FYI: If You're New to the Industry, the Data Science Job Market is Saturated

July 20, 2021 -

For the billionth time, the data science job market for people with 0-4 years is so saturated.

There are 100s of university creating new masters degrees, certificates, under-grad majors. 100s of bootcamps, etc.

The supply of entry level workers is probably double if not triple the demand(made up statistic). Every job I apply for, there's 50 other people with masters or PHD degree trying to enter.

If you're new to the industry, just know that you may have a much longer road to breaking into the industry than you can imagine. Think twice before you decide to commit to this. But don't let this be a deterrent if it's something you love, I'm just trying to inform.

In my opinion, it is much safer to develop expertise in a domain (healthcare, insurance, banking etc.) and then apply data science principles to your domain. That's what I have done. I am not a data scientist and nobody will hire me for my data science "skills" (honestly I am not skilled like many people in this sub). Instead I have been able to cement my reputation (and get good raises) as I brought data driven insights and speed to decision making in my job. This may not be possible for everyone but developing domain expertise and then applying data science is easier that chasing a few pure play data science positions.

The supply of entry level workers is probably double if not triple the demand(made up statistic).

I think waaaay too many people are still hung up on the "sexiest job of the 21st century" title that was declared by the Harvard Business Review.

Remember, that was published almost 10 years ago. The market and the reality on the ground for companies have changed since then, and the article doesn't ring as much true any more.

reddit.com › r/datascience › does any of you regret getting into data science? and why?

r/datascience on Reddit: Does any of you regret getting into Data Science? And why?

July 10, 2024 -

And if it wasn’t for DS, what profession will you be in?

I don't regret getting into Data Science since I've always wanted to be able to parse data and make predictions. I hate how people are attaching Data Science buzzwords to everything

Nope. The beauty of the DS field is that it’s very broad with many options. You can go DS, or data engineering, data analytics, or business intelligence. These fields are interconnected and there’s a lot of overlap.

reddit.com › r/datascience › do you like your job?

r/datascience on Reddit: Do You Like Your Job?

June 21, 2023 -

It seems like nobody here is actually happy to be in data science. Is everyone here doing it for the money? Do you actually like your jobs, warts and all?

Vocal minority. Same way you’re more likely to leave a negative review than a positive one.

Yes, I like my job. Data Science is my second career. I used to work in marketing. I did not enjoy it. But I can honestly say that I enjoy working in analytics/DS. I like the work I do, I have good coworkers, I get to work on interesting projects. I like the industry that I’m in. I have great work/life balance. My pay is good. I really don’t have much that I can complain about. However, it’s still just a job. I don’t expect my job to provide all of my fulfillment. I also have friends and family and hobbies and whatnot for that. But I think having already had a career that I didn’t like and making a switch has given me a lot of perspective.

reddit.com › r/careerguidance › career guidance please! is data science a good career ?

r/careerguidance on Reddit: Career guidance PLEASE! Is Data Science a good career ?

April 2, 2026 -

Hi everyone!

need some input and a bit of guidance...

I currently have a Bachelor of Science in Marketing and am considering pursuing a Master’s in Data Science (possibly through WGU) to move into a more stable, long-term career path. I’m especially interested in roles within tech..

But if its best to stay in hospitality and retail because I have background in this already as far as marketing coordination and operation goes..I guess Im open to it.. doesnt seem as promising as tech though..

I also run my own business, where I help early-stage founders build structured marketing systems and foundations, so I’ve had some exposure to strategy, workflows, and systems thinking.

Given the current job market, I’m trying to be intentional about my next step and make sure I’m investing in the right direction.

I’d really appreciate insight on a few questions I have:

• Is a Master’s in Data Science the right move with a marketing background, or would Data Analytics be a more practical entry point?

• What technical skills or tools should I focus on before applying to a master’s program (Python, SQL, statistics, etc.)?

• Are there specific certifications or programs that would help me become competitive without immediately committing to a full master’s?

• How can I best position my marketing + systems experience to transition into a data-focused role?

• For those in the field, what does a realistic entry path look like today ?

I’m aiming for a path that offers stability, growth, and strong earning potential, and I want to make sure I’m building the right foundation before committing to a graduate program.

If any of you have went from Marketing into Data Science Ill love to hear about your career journey!

Thank you in advance for any guidance!

Personally I’d lean toward testing data analytics first before committing to a full master’s. It’s closer to your current skills and easier to break into. You could build a few small projects using real datasets and see if you enjoy the work. It might also help to mix your marketing experience with data since that combo is valuable. Since you’re thinking about switching things up, take a look at GradSimple. They share stories from people who’ve started over or changed industries entirely. It might give you some ideas for your next move.

reddit.com › r/datascience › realistic to pursue a career in datascience and the requirements needed.

r/datascience on Reddit: Realistic to pursue a career in datascience and the requirements needed.

March 10, 2024 -

Hi all.

I'm a semi "new graduate" with a bsc in cognitive science, with 1 year work experience in a job that never had any relevance to my degree whatsoever, hence i left. It was like being a bike mechanic asked to fix cars. Well that's what it felt like anyway.

I come from a background where people who continue and finish their masters, often go into datascience careers. In my country a bsc also means very little, it's unusual to "only" have a bsc to begin with, masters are an expectation. So finding a new and relevant job is already challenging.

When I look at this sub I've come to realise I have no clue what I'd be doing or expected to do in such a position. I find it unnerving and I feel completely incompetent. I can't work out if a background with strong knowledge of statistics, frequentist and bayesian, is enough to pursue anything related to datascience? I currently only apply for data analytics, and have been avoiding anything called datascience, and anything that mentions ML and/or MLOps entirely. I simply don't know enough.

I just find datascience to be interesting, but unlike analytics, more programming heavy?

My questions are:

Can I move onto datascience from analytics, will I gain enough insight?
Would learning by doing in a job be enough? I can do R, I realise I need to at least get familiar with SQL, and probably python. This sub dabbles in so many things, that's completely out of my depth.
Should I bail on this entirely, and go back to uni?

I just have an overall feeling of being completely incompetent. My time away from uni, doing a what felt like an unrelated job, has left me feeling even more useless and lacking in skills. Even data analytics feels too far gone, and I worry I can't remember anything if I managed to get a job again. I fucking love analytics, I love wrangling data, analysing outputs and results, trying to determine the best way to solve problems through models etc. Most analytic jobs just look to be the very basics. I don't think I'd be happy doing that long-term. I just don't know if datascience would be a realistic avenue to pursue, nor do I fully understand what would be expected of me on the jobmarket.

I apologise for the wall of text. I think I needed to vent a little.

edit: thanks for all the replies. They've been really insightful. I appreciate it!

You're overthinking this dude, just go do some science with some data.

Hey there!

First off, it's completely normal to feel overwhelmed when transitioning careers, especially in a field as broad and rapidly evolving as data science. Don't be too hard on yourself; everyone starts somewhere, and the journey is about continuous learning and improvement. Let's break down your concerns and questions:

**Transitioning from Data Analytics to Data Science:**
- **Yes,** you can move from data analytics to data science. The two fields are interrelated, and many of the skills in analytics are foundational to data science. Your strong knowledge of statistics, frequentist, and Bayesian methods is definitely a huge plus.
- **Gaining Insight:** Working in data analytics will indeed provide valuable insights and experience that could bridge into data science roles. Look out for opportunities within your analytics job to incorporate more data science tasks or projects.
**Learning by Doing in a Job:**
- **Self-Learning:** While learning on the job is invaluable, considering your interest in data science, self-initiated learning will be essential. Familiarizing yourself with SQL and Python is a great start as they are cornerstone tools in the data science toolkit.
- **Online Resources:** There are countless resources online for you to learn from, including free and paid courses on platforms such as Coursera, edX, and Udacity, which can help bridge the gap in programming skills and data science knowledge.
- **Projects:** Try to work on small projects or Kaggle competitions on your own or with a team. This hands-on practice is crucial and will also build your portfolio, which is important for job applications.
**Going Back to University:**
- **Weighing the Pros and Cons:** It's an option, but consider the cost, time, and what specific skills or credentials you would gain. Sometimes, targeted learning and gaining practical experience can be just as, if not more, effective.
- **Alternatives to a Full Degree:** Look into postgraduate certificates, diplomas, or short courses that are specifically focused on data science. These can often be more time and cost-efficient while still providing the necessary education and credibility.
**Feeling Incompetent:**
- **Impostor Syndrome:** What you're experiencing is quite common, especially in fields with high levels of expertise and competition. Remember, every expert started as a beginner, and the tech field is one of lifelong learning.
- **Community and Mentors:** Try to connect with people in the field, whether through online communities, networking events, or finding a mentor. Talking to those who've been in your shoes can be incredibly reassuring.

Don’t give up on your passion for analytics and curiosity for data science. It’s clear you have a strong foundation and enthusiasm, which are critical. The field of data science is vast, and there's room for many specialties and skill sets. Focus on building your skills one step at a time, and don’t be afraid to take on challenges - they’re the best learning opportunities.

Remember, every professional journey is unique. Be patient and persistent, and keep exploring and learning!

Hope this helps, and wishing you all the best in your career exploration! 👍

reddit.com › r/datascience › the confusing messages about the data science career

r/datascience on Reddit: The confusing messages about the data science career

February 3, 2017 -

Speaking as someone with a PhD in a quantitative field who'd like to become a data scientist (at least, I think I'd like to do that), it has been a challenge to figure out what the data science career is all about and especially how to get into it. I think I'm a pretty smart, discerning person, but the things I've read and heard about this career are, as a whole, so confusing and so contradictory it's hard to know what to think.

Several months ago, after doing some research, I thought this was a viable path for me and that I had a plan to get into the field. Today, I don't know whether any of the things I believed about this career months ago are true, whether it's realistic for me to get a data science position in the near future, or whether this is a career I would even want.

I thought I'd list paraphrased version of some of the confusing and often contradictory things I've read and heard about this career for your amusement. If anyone has thoughts, by all means please comment, although at this point I don't think I'll believe anything anyone says about this stuff without experiencing it firsthand.

Data science is a viable career path for people without a strong technical background who are willing to take a couple online courses.
It's very straightforward to get a data scientist position if you have an advanced degree in any quantitative field, especially if you can code.
It's almost impossible to get a data scientist position right out of (grad) school. No matter what field your degree was in, you probably need to spend a couple years working as a data analyst or BI person to get some domain expertise.
Data science is a field with a serious talent shortage; companies are desperate for anyone who's at all qualified.
The data science job market is flooded with applicants; any open job is going to get dozens if not hundreds of reasonably qualified applicants. It's really hard to get a job unless you seriously stand out.
The technical skills are the least important aspect. It's more important to work well with people and be a good communicator. You can easily learn the technical aspects on the job.
A data scientist just needs to know more statistics than a programmer and more coding than a statistician.
A data scientist needs to know all about optimization, numerical analysis, algorithms and data structures, and lots of advanced machine learning and statistics.
The only things you really have to know are the fundamentals: be able to write some code in Python and R and know about confidence intervals and linear regression.
In order to appeal to employers, it's crucial to have experience with the exact technologies the employer uses. If they use Hadoop, you must know Hadoop. Etc.
There's no way to be a data scientist without knowing some pretty advanced math.
Math isn't really relevant for data science in practice, and some of the most successful data scientists I know don't even know calculus.
Data scientists spend their time working on intellectually challenging problems that are just as interesting as those in academia.
Data scientists spend most of their time on boring data munging issues, usually with an ultimate goal of selling more shoes or some other horrible marketing thing.
The data science field is really hot and will surely continue to grow for many years to come.
Data science has hit its peak; it will continue to be useful in its areas of legitimate application, but companies are realizing it was oversold and they will be cutting back.
There is an amazing amount of value to be had in data science.
Most data scientists contribute nothing worthwhile to their employers.
You must have a portfolio of projects to show employers.
I don't know any data scientists with a portfolio of projects; my coworkers and I think that's a waste of time.

Here's another discouraging thing: looking at job ads for data scientist positions, there is such a crazy variety of skills and experience desired for each position. While I know that "Required skills" aren't always really required, it's pretty bewildering when:

one position says you must have NLP experience;
one says you must have cybersecurity experience;
one says you must have Javascript and web programming experience (?!);
one says you must have experience in the health care industry;
one says you must have retail marketing experience;
one says you must have bioinformatics experience;
one says you must have experience in the intelligence community;
one says you must have Django experience (?!);
one says you must have experience with the Microsoft products SSMS, SSAS, and SSRS;
one says you must know SAS;
one says you must have experience in financial analysis.

In the above list, when I listed specific technologies, it was because they seem somewhat outside the standard data science toolkit, as far as I know. But even when they mention "mainstream" technologies, they vary so much between the different positions, and there is such a massive list. Different ones want AWS, Spark, Hadoop, Hive, Pig, PostgreSQL, MySQL, D3, Tableau, MapReduce, Flink, Mahout, MongoDB, Cassandra, and many more.

Judging by the ads, there is probably hardly anyone in the world who is qualified for more than one of these positions... and yet they are all "data scientist" positions.

Actually, I think half these employers just copy & pasted a bunch of buzzwords into their ads, but that's depressing for another reason.

If anyone has thoughts, by all means please comment, although at this point I don't think I'll believe anything anyone says about this stuff without experiencing it firsthand. Credential-wise, I've got a MS in a DS field - I've worked as the principle prediction focused (ML) data scientist at two separate companies now for the last 3.25 years. Data science is a viable career path for people without a strong technical background who are willing to take a couple online courses. Wat. If one believes that a couple of online courses is all a non-technical person needs to get a senior level title with six figures then they're optimistic to the point of stupidity. It's very straightforward to get a data scientist position if you have an advanced degree in any quantitative field, especially if you can code. Why would this be the case? It's almost impossible to get a data scientist position right out of (grad) school. No matter what field your degree was in, you probably need to spend a couple years working as a data analyst or BI person to get some domain expertise. Mostly true for MS folks. Less true for PhDs who are interested in DS jobs that lean heavily on study design. Data science is a field with a serious talent shortage; companies are desperate for anyone who's at all qualified. There is definitely a shortage of qualified folks compared to the average field, but I find that companies are willing to wait rather than hire a dunce (my last company had been looking for 1.5 years before I came on) The data science job market is flooded with applicants; any open job is going to get dozens if not hundreds of reasonably qualified applicants. It's really hard to get a job unless you seriously stand out. Much of this is the classic HR problem. The people doing the resume screenings have no fucking clue what a good applicant looks like. Lean on networking (online and in-person) and recruiters are surprisingly useful in this field also. The technical skills are the least important aspect. It's more important to work well with people and be a good communicator. To say technical skills aren't important in a very technical field is asinine. Soft skills are important everywhere but they aren't trumping tech skills here. You can easily learn the technical aspects on the job. Why TF would I hire a good communicator that has no idea how to actually do the job? A data scientist just needs to know more statistics than a programmer and more coding than a statistician. This is an old (and pretty good) general definition, but I'm not sure why you (someone) has changed it to "just needs" - we aren't trying to fit some minimum requirement threshold here. A data scientist needs to know all about optimization, numerical analysis, algorithms and data structures, and lots of advanced machine learning and statistics. You need to have a foundation in all of these things. You will not be an expert in every DS area. The only things you really have to know are the fundamentals: be able to write some code in Python and R and know about confidence intervals and linear regression. Whoever said this was a jackass. In order to appeal to employers, it's crucial to have experience with the exact technologies the employer uses. If they use Hadoop, you must know Hadoop. Etc. Change "employers" to "Human Resources" and you've got it right. There's no way to be a data scientist without knowing some pretty advanced math. Some pretty advanced math does not equal full courses of Calc 1, Calc 2 as some folks have said here. I took them in college.... the concepts I actually use from these courses can be summarized in a week of learnings. Math isn't really relevant for data science in practice, and some of the most successful data scientists I know don't even know calculus. You need to understand derivatives to 'get' gradient descent. Knowing that the derivative of x squared is 2x is not "knowing calculus". Understanding probability and stats (especially concepts related to bias) are 100 times more important unless you're a ML researcher. Data scientists spend their time working on intellectually challenging problems that are just as interesting as those in academia. True IMO Data scientists spend most of their time on boring data munging issues, usually with an ultimate goal of selling more shoes or some other horrible marketing thing. I have no doubt that some DS have to spend a bunch of time on data munging, but Jesus their company is either extremely small or dumb as shit (invest in data management teams FFS) or they're in a niche that requires them to constantly be taking in new (unstructured largely) data types. The data science field is really hot and will surely continue to grow for many years to come. Hope so, we'll see. Data science has hit its peak; it will continue to be useful in its areas of legitimate application, but companies are realizing it was oversold and they will be cutting back. Hope not, we'll see. There is an amazing amount of value to be had in data science. I'm paid a pittance of the easily quantified value I've brought to my employer Most data scientists contribute nothing worthwhile to their employers. Definitely true for some. Sometimes it's someone pretending they're a DS, sometimes it's the companies fault. You must have a portfolio of projects to show employers. Nah, but you need to be able to talk about projects. I don't know any data scientists with a portfolio of projects; my coworkers and I think that's a waste of time.

Pure R&D roles where you can just explore data like an academician and where your minions will collect, process, organize and store that data for you, are fairly rare, at least in the corporate world. I would probably call that role a "pure data scientist". This is where the academic skills of advanced statistics and computer science come in handy. Most people working in this field have to do some to a lot of data wrangling. Collecting, organizing, storing, processing, cleaning, and managing the data you might work with. This is where hands on experience working with data systems and programming languages become necessary. Most people doing this work have to be deeply plugged into their business and have a strong understanding of the business vision and strategic goals, as well as how specific tactics might be deployed to further that vision. This is where experience in a specific industry can really help. Every business, and every industry has a huge glossary of strange terms, acronyms, and lingo that are unique to them. Often these same words can mean very different things in other industries and businesses. You need to know them well. Finally, once you have a good sense of what sorts of things will be of value to the business (and therefore justify your salary), and you are neck deep in the data with it all over your fingers, then you can pick up some of those fancy ML and AI and Tensor and Symbolic Algebra tools and put it all together to do "data science". Some data science problems, once solved, still require a transition to a production system. Rarely is the data static, and the question only answered once. Usually you will be involved in the project to "productionalize" the techniques and processes so they can be used in an ongoing basis in the organization. This is where some engineering and operations skills come in handy as well as a sense of data architecture, systems architecture, and formal process management are very helpful. You may get to do all of the above. You may only do a piece of it. You may find any part and/or all of it carrying the title of "Data Scientist". You may also find some of it with other titles "Data Engineer", "Data Architect", "Data Administrator", "Data Analyst", and these all overlap and intersect. There is probably some sort of n-dimensional venn diagram that would visualize how it all fits together, but it isn't really necessary. The trick is to find the role and industry and coworkers and boss that suits you best and soak it up while you can.

reddit.com › r/datascience › do any of you regret becoming a data scientist?

r/datascience on Reddit: Do any of you regret becoming a Data Scientist?

September 21, 2023 -

A Data Scientist is a dream job for many people who love data and statistical analysis but things are often less enjoyable once you do them for a living. Do any of you wish that you went down a different path, and if so, which one?

Nah. I work remote and live a super good life. Why would I regret that?

The only thing I regret is the number of other people who became data scientists through the DS degree mills that popped up after the job started getting a lot more public attention. It’s very hard to find people with actual training in science now because you have to sift through a million identical resumes with the same online degrees. It just seems harder to build an effective DS team when most of the people on the team have the exact same cookie-cutter approach and the same, boring, tired, marginally useful ideas.

reddit.com › r/datascience › is the traditional data scientist role dying out?

r/datascience on Reddit: Is the traditional Data Scientist role dying out?

May 22, 2025 -

I've been casually browsing job postings lately just to stay informed about the market, and honestly, I'm starting to wonder if the classic "Data Scientist" position is becoming a thing of the past.

Most of what I'm seeing falls into these categories:

Data Analyst/BI roles (lots of SQL, dashboards, basic reporting)
Data Engineer positions (pipelines, ETL, infrastructure stuff)
AI/ML Engineer jobs (but these seem more about LLMs and deploying models than actually building them)

What I'm not seeing much of anymore is that traditional data scientist role - you know, the one where you actually do statistical modeling, design experiments, and work through complex business problems from start to finish using both programming and solid stats knowledge.

It makes me wonder: are companies just splitting up what used to be one data scientist job into multiple specialized roles? Or has the market just moved on from needing that "unicorn" profile that could do everything?

For those of you currently working as data scientists - what does your actual day-to-day look like? Are you still doing the traditional DS work, or has your role evolved into something more specialized?

And for anyone else who's been keeping an eye on the job market - am I just looking in the wrong places, or are others seeing this same trend?

Just curious about where the field is heading and whether that broad, stats-heavy data scientist role still has a place in today's market.

I think data scientist has split into two directions. The more technical ones have been absorbed into the engineering world (AI/ML Engineer), while the less technical ones have been absorbed into more data analyst / BI roles.

The "traditional" (I'm not that old and remember before businesses knew what this was, how is it a tradition already?) role barely ever existed. The number of businesses with the type of problems and data where you need data science are very few. But data was the new hotness and FAANG uses it, so businesses started hiring data scientists. So 'data science' as a business term often was shorthand for something like 'guru rockstar ninja business intelligence person'. Or maybe 'full stack data person' in some cases. And now we're on the wrong side of the hype cycle and businesses are being more careful about how they throw money into data, and hirers can be more picky and don't need to use a flash term like scientist to entice applicants. So the fake data science roles aren't as common. I doubt there's a dropoff of actual data science getting done in those companies that have a genuine use for it. But there are definitely fewer roles advertised for data science, which is why I think it's mostly the 'fake' data science roles that have gone.