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?
Videos
What is the difference between data science and data engineering degrees?
How much can you earn with a bachelor's in data science degree?
Are graduates of a bachelor's degree in data science in demand?
From what I know, computer science is the more respected and recognized degree and having a CS degree can get you every job a data science major is able to get. What is the point of doing a data science degree when you can do the OG degree instead?
Hello, I’m a currently a sophomore undergrad majoring in statistics. I’ve only really done “statistical” programming in python and R, mainly done data analysis/cleaning and sklearn/tidymodels model fitting in a notebook or rmarkdown. And some basic dashboarding in streamlit. My plan is to get a MS in applied statistics and then work in Data Science. My major kind of aligns along the classical statistician type of background, which I’m okay with because I wanted a strong math background for a career in DS cause that’s what I THOUGHT was important. But I’ve come to realize that it’s a lot less important than software dev/cs skills.
, I said to myself that I wasn’t going to complain about this because I thought that I would just try and learn data structures/algs and relevant cs topics to at least have some sort of software skills. But in doing so I’ve realized that I’m going to end up wasting my stats degree and never going to use it in industry.
Everywhere I’ve read about jobs in DS it’s that you need 75% software skills related to productionising and maybe 25% of statistical knowledge. These aren’t hard facts but basically from what I’ve read stats and math knowledge is not as useful as cs/software dev skills.
I’m just worried I’m going to go through all this schooling only to compete with software developers, and then learn all these various software tools only to become a software dev with a rigorous stats background and a “Data scientist” title when I’m not even doing any sort of statistical work, and my stats background will be a waste of money/time.
I want to be close to Machine Learning/statistical learning and building models, but it just seems like these software skills related to data eng/production tools are going to overshadow any statistical knowledge I have gained through my degree, thus making my degree a waste since I will be using maybe 5% of it.
I’m rambling here, but could any data scientists in industry here with an applied stats/stats background shed light on if their degree is really used in data science industry. And really how much statistical knowledge u actually get to apply relative to software dev knowledge? And if u felt like the degree was a waste compared to if you got a CS degree?
It just seems like industry doesn’t actually care if you have stat/ML knowledge but more so if you can use software skills.
Lot's to unpack here, but let's start with the big one:
Yes, software skills are in higher demand right now than pure statistics/modeling skills- however, the pool of jobs is large enough that there is still considerable demand for people with a strong modeling background - especially relative so supply.
This demand is also going to be very different by industry. If you want to get into tech, yes, overwhelmingly people are going to be looking for people with a strong programming background - because their core product is software. So everything you do eventually needs to make its way into software, and that means that the burden is heavily on being able to contribute without a ton of intermediaries.
But in a wide range of other interviews (finance, supply chain, marketing, sales, pricing, etc.), programming chops are going to be more of a facilitator than a core component to the job. And that is because their core product isn't software - it's something else. And that means that your time will be much more likely dedicated to support those departments in making better decisions, which is less about writing code and more about making sense.
Now, here's the catch: a "strong" modeling background generally is going to mean a master's degree with a research component. That is, a master's degree in which you were able to get really in-depth in some area of statistics. Maybe you can get there without doing research, but in evaluating candidates what I consistently find is that those who graduate from programs where the graduation requirement is a capstone project just don't have the depth to differentiate themselves - they normally end basically just knowing the core concepts that were taught in class, but they haven't had the time to really push that thinking further.
Assuming you're there, part of what you will find is that generally you're not competing for the same jobs - i.e., the jobs that have a heavy modeling component aren't generally going to be targeted at people who spent a lot of time building programming chops. Meanwhile, the jobs that have a strong programming component aren't going to be targeted at those with a stats background.
Having said that, the programming-focused jobs are going to pay more, and that's a function of industry (tech pays more), competition for talent (those people can also go be software engineers), etc.
However (and something that doesn't get talked about enough), one critical thing that people need to understand is that your career ceiling - both in compensation and level - is much more heavily tied to how good you are at something than what the something is. If what you enjoy doing is statistical, modeling work, then there's no purpose in investing your time in developing software skills you don't want to use. Your best bet is to study statistics and get really, really good at it - enough to differentiate yourself from other statistics grads so that you can land one of those top 5% statistics jobs that are going to be excellent. That is going to send you on a much better path than becoming an average programmer-type data scientist where you're competing for the "meh" jobs in that world.
if their degree is really used in data science industry
I read research paper and implemented the method into our business. My degree also got me into the door and gave me some credibility when I speak.
how much statistical knowledge
Not much. In most cases it's descriptive statistics such as mean and percentile. However, there's a noticeable difference in depth and complexity between my colleague (no training in stats) and I in our approach to uncertainty.
if u felt like the degree was a waste compared to if you got a CS degree
No. I was interested in stats, not CS.
If your goal is to get into tech and make big money in norcal, you should learn CS not stats. If you're interested in stats, then you should learn stats.
If it makes you feel better, most people in my master in applied stats program ended up in pretty good careers.
It sounds to me like you are enrolled in a program which emphasizes a classical liberal education. The program is intended to provide you a well-rounded education, which gives you more skills than an automaton who has only studied statistics and programming. Congratulations. This looks like an entirely reasonable course of study to me, similar to many data science programs that I have seen in American universities.
To give some further context, I am going to once again cite Matt Might's fantastic image:
In the classical Western tradition, a bachelors degree is not intended to make you an expert. It is intended to give you a broad foundation of knowledge and skills, with some specialization into a particular field or topic. After completing a bachelors degree, you should have a little bit of knowledge about a lot of different things, some specialized knowledge, and a set of skills which should enable you to learn new things more easily.
That being said, you are also just completing your first year. This often involves taking a significant number of "general education" courses, which are designed to ensure that you leave the program as a more well-rounded person. It looks to me like you will be taking more specialized courses later on (after Cycle IV, if I understand the program map correctly).
Having explained what I think a bachelors education is, let me also outline a couple of things that a bachelors degree is not:
A bachelors degree is not job training. Generally speaking, if your goal after a bachelors degree is employment outside of academia, the degree demonstrates that you are teachable. Most employers will not expect a person with a bachelors degree to know everything they need to know in order to do a job—some amount of on-the-job training will be expected (indeed, many employers would rather that you don't have too much specialization, because then there are fewer bad habits that they have to train you out of).
A bachelors degree is not a graduate degree. In the process of completing a graduate degree, you are likely to be pushing at the fringes of what is known. You may or may not be required to expand human knowledge (that is generally the difference between a masters and a phd), but you are expected to at least walk right up to the line. A bachelors degree, in general, is not nearly that specialized. You should be walking along well-trod paths (mostly).
Your program looks quite standard to me, compared to (say) US program. In a continental European program you would be taking far fewer "general education" classes, which reflects a fundamental difference in what secondary and tertiary education is supposed to encompass between the two systems.
Re only three statistics courses: it has long been a particular annoyance to me that data scientists do not know enough statistics and in particular do not have enough visceral understanding of randomness. Unfortunately, a rather narrow grounding in statistics is one of the hallmarks of most data science courses. So your statistics ingredient seems par for the course to me.
I agree that the programming courses (not computer science; but in data science, you need programming skills and not CS knowledge) seem rather thin. On the one hand, a data scientist should really understand a little more than "what is a variable" (but then again, they presumably also need to teach people who have never coded before, so they need to start at the very beginning)... and on the other hand, it looks strange to learn R and Python, but not SQL. Then again, there are thousands of online courses or bootcamps where you can pick up most of what you need, and in the course of your career, you will need to learn additional languages at some point, anyway.
Regarding those courses you find "irrelevant"... I could hardly disagree more. One could quibble with the precise courses you list here, but accounting is absolutely fundamental knowledge for a data scientist who wants to work in industry. The better data scientists stand out by understanding the business and economic impacts of their analyses and recommendations! Far too many data scientists believe their job is done when they have improved prediction accuracy by 5%, and perhaps written up a requirements document for the software developers to turn their ideas into production code... but the important decision then is whether these 5% are actually worth putting into production, and for that you need a solid grounding in business and accounting, and in the language that the business people use. Data science is not an end in itself.
In the end, data science - like many other fields - requires you to stay self-motivated and go above and beyond what you learn in school or at university. If you find you already know what your courses are teaching you, great! That gives you free time to delve more deeply into aspects where your courses only touch the surface. Read statistics books, learn more programming languages, or do Kaggle competitions in your free time, and do internships to see how data science is actually applied "out there".
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?!
I was planning to post this in r/datascience but I don’t have another comment karma yet to do so.
I’m currently a senior in high school planning on going to community college post-graduation despite getting accepted to every school I’ve applied to as a CS major (CPP, SDSU, CSUSM) in order to save money. After taking a course at school and a program online, I’ve decided that Data Science is the branch of CS that I’m most interested in pursuing at the moment. I’m not entirely sure what career I want specifically yet, but something along the lines of Data Analytics, Data Engineering, Statistics, and Healthcare seems up my alley.
I’ve come across mixed opinions on the Data Science degree. Since it’s still a fairly new degree, there’s not much consensus yet as to whether it’s just as valuable as earning a B.S in Computer Science or Mathematics. While I’ve heard more people who have gotten into Data Science jobs with a Computer Science degree, it is currently very difficult to transfer from CC to University as a CS major due to how impacted it is. My initial plan with choosing CC was to complete my lower division requirements and IGETC courses via community college so I can transfer into University. The classes I’m required to take as a transfer for CS are very math heavy and much more difficult than typical high school classes. The acceptance rates for transfer students while slightly higher than college freshman are very low to the point where even students who have a 4.0 GPA are getting rejected.
I was told I’m better off majoring in Data Science or Mathematics instead because of competition. But given how saturated CS currently is, does this mean Data Science degrees will become redundant in the near future? If there are thousands of Computer Science students who aren’t getting interviewed for jobs, then how bad will it be for Data Science majors in a few years?
I’m still certain this is the field I want to pursue, however, I’m not sure if I’m making the right choice by going this route. I’m planning to transfer from CC within 2 years, but I’ve got to play my cards right. Will choosing Data Science as a degree be a mistake? Should I still apply to some safety schools with CS as my main major? Or is it still going to be nearly as employable as a CS degree if I put in the work (do internships, projects, etc.)
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?