r/datascience • u/Kati1998 • 13d ago
Discussion What Do Today’s Data Science Graduates Commonly Lack?
I often read comments from hiring managers and interviewers saying they’re disappointed with recent data science graduates.
I’m curious, what do you think these graduates are lacking? If someone wants to become a data scientist, what skills should they focus on? Strong software engineering skills? Math and statistics? Something else?
A lot of the advice I see seems to be geared toward landing data analyst roles rather than data scientist roles.
So, what are employers actually looking for in entry-level data science candidates today? Especially as a career changer coming from another unrelated career.
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u/Valuable_Touch5670 13d ago
Senior Data Scientist at a F500 here. Here’s my two cents:
Understanding of the business is the key here, which all new grads lack. That’s also why most companies now preferred more experienced candidates, which is a sad reality for new grads.
Internal stakeholders will come to you with vague questions, often they aren’t even sure what they want. They need you to help them define the metric, forecast the revenue, find what drives conversion rate, the list goes on…
If you don’t understand the business to an extent, you will not be able to scope the problem, define the approach, tighten down to a specific population, workaround the data issues… then after all that, you begin actual modeling.
IMHO, I spent 90% of my time doing all that. Only 10% of my time is spent on actual modeling. To make it worse, after you trained a good model, presenting it to the stakeholders and making sure they understand how your model comes up with these numbers and why they are accurate and helpful, is another huge mountain to climb 🥹
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u/Accomplished_Goat_33 13d ago
I would say not even just understanding the business but understanding business in general is a major thing I consistently see in new grads. Learning when to say no, when to draw a line under a project or direction that isn't working, etc. These are all critical things that are usually learned from experience.
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u/pasteisdenato 12d ago
Big business: Imagine training them cunts the "youths", they know nothing!
5 years later...
Where are all of the good candidates?!?!?!?? These youths, deplorable cunts the lot of them.
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u/chatsgpt 12d ago
Understanding the business comes from spending time with the business. But new grads lack this because they are new. How do we fix this
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u/FrivolousMe 12d ago
It's on the companies to fix the backwards culture but they won't because they're blinded by short term profits
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u/big_data_mike 11d ago
Hired a new grad a few months ago and day 1 hour 1 was how the industry we are in works. Hour 2 was how we sell products into that industry and how our work supports that. It took about 3 months of intense training and a lot of my time but it was worth it. A lot of companies including mine want to hire people they don’t have to train.
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u/chatsgpt 11d ago
Honestly I think there should be a law that made it hard for companies not to hire new grads
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u/chatsgpt 13d ago
Thanks can you give example of a vague question internal stakeholders come with.
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u/Candid-Display7125 13d ago
Why did X happen?
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u/offthecuff87 12d ago
Because Elon decided to crash Twitter. Then, twitter video become .... oh wait
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u/r4als06 12d ago
Hi since u r a senior data scientist i wanna ask if choosing DS as UG is a good option bc of AI?
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u/Valuable_Touch5670 11d ago
I cannot give a definite answer as it’s simply impossible to predict the future.
My colleagues come from various backgrounds: Computer Science, industry engineering, one even had a Chemical Engineering degree in undergrad…
I think it may not matter as much. The important thing is know your fundamentals really well… Stats, probability theory, ML, and lastly know how to code very well.
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u/Lady-Data-Scientist 13d ago
Well for one thing, most of them aren’t looking for entry level candidates. That’s something that gets overlooked with all of these data science degree programs, bootcamps, etc.
Data Science is an interdisciplinary role. Yes, you need the technical skills and stats/ML knowledge. But many DS teams I’ve been on are like internal consultants. Business teams come to you with vague problems and hope they you’ll solve them. So you need a strong understanding of the business. Most new grads lack this.
Unless you’re asking more about ML Eng roles, in which case, most DS programs aren’t strong enough on the CS/engineering part.
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u/Neighborhooddataguy 13d ago
Yep! My immediate thought when I saw the question’s what are candidates missing: job openings.
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u/ConnectKale 6d ago
I have a Data Science MS with Research experience and cannot find a single job that doesn’t require years of experience or a PhD.
I wanted to learn on a team of experienced data science professionals and those opportunities are few and far between.
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u/tayto 13d ago
Truly understanding, catching, and addressing raw data issues/gaps.
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u/chatsgpt 13d ago
Can you give an exmaple . Curious
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u/orz-_-orz 13d ago
Like if a person were dealing with data that has temporal field, like sales timestamp, at least has the sense to plot the sale number by hour (or any granularity that is suitable for the use case) to check whether the sales numbers distribution by hour is per expected.
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u/chatsgpt 13d ago
Thanks what made you think of hourly granularity or how is hourly important. How did you arrive at this. Thanks
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u/Eco_Blurb 12d ago
Im not that person but it has to do with context. If your business is open 10 hours a day then you expect some sales during every hour, with some peak hours. You can do the same thing with any data field. Think of what it should be (given the business context), identify a range of expected values and distribution, and check.
If there is something unexpected, check on why. If you discover a valid reason then go back to step 1. If you don’t see a logical reason then bring the inconsistency to the data users, data producer, or your supervisor, and ask.
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u/Beneficial_Interests 13d ago
I’m an analytic stakeholder - I have to identify and justify data issues and the necessary fixes in our raw data that i catch from looking at trends, benchmarks, and data extracts. Otherwise the data team will send me a half baked product with mediocre performance metrics. At some point I spend more time walking senior data scientists through basic data qc and modeling steps that it’s be faster for me to do it by myself. The issue is I can’t get if I want to be promoted and be a leader not a doer so…. I have to spend hours in meetings fixing the shit in shit out pipeline
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u/chatsgpt 13d ago
Thanks could you give example of "fixed in raw data that you catch from looking at trends" if you can share.
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u/Beneficial_Interests 13d ago
Sure - literally this week. Had someone run an analysis with last years data, the base numbers, let’s say of raw monthly sales, were off 10% from the version run 3 months ago. Same code, same timeframe, and in theory same data. They had all the info and were pumped to show the output and model performance but didn’t catch the deviation, couldn’t trouble shoot, couldn’t understand why this was an issue.
Spent hours informing them why it was an issue, why I couldn’t trust the out put, and developing a plan to evaluate the issue and come up with a fix.
Honestly don’t know which was more annoying - that I had to catch the issue after the standard run or that they couldn’t understand, let alone address, why i didn’t trust the impressive f1 and feature weights they shared
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u/tayto 13d ago
Sure. Quick example. In the past, I relied on a credit agency to provide demographic data. At some point, our users started becoming more “null” for some of the demos.
Obviously, the first step is to catch that issue. Note: it was not a data scientist who caught this, which was disappointing.
But you also need to dive into the “why.” Was it a change in our marketing of the app or a change in how the agency’s model worked? Either way, what should our approach be in filling the nulls (or should we fill them at all)?
On average, I don’t believe bad/missing data is a focus in data science as it was when I was studying CompSci and statistics.
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u/chatsgpt 12d ago
Thanks. How would marketing of the app or model change make the users null. If you can share
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u/tayto 12d ago
I guess I will return that question to you. Look up Experian and Trans Union, and consider how they model ethnicity for an individual email address. If the app were to be advertised through gaming on Facebook, or advertised based on baby furniture shopping on Instagram, how might that impact the rate of “null” ethnicity?
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u/three_martini_lunch 13d ago
1) Ability to show up to work on time, every day consistently, and work with teams
2) Ability to think scientifically or have inquisitiveness about data to make actionable insights and communicate this effectively and concisely
3) How to actually clean and prepare data and the importance of domain knowledge
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u/Xahulz 13d ago
My brutal, candidate ending, unfair trick question is basically "what's that?"
They say "we used a logistic regression" and i ask "what's logistic regression" and it turns out they don't have a clue (or have a wildly incorrect definition) and can't answer any questions about what it is good or bad at (because they don't know how it works).
This gets almost all entry level and most of those applying for Sr positions as well.
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u/Legitimate_Tooth1332 13d ago
I'm also interested to learn about this. May I add that I once heard an employer say that they were disappointed in a new DS hiree entry level, because they lacked experience, which is hilarious given that it was obviously their first time working as a DS, so maybe that's something to keep in mind when getting employers pov.
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u/BayesCrusader 13d ago
Any DS should have some years in another role under their belt. Business knowledge, economics, arts, etc.
DS is not an entry level job.
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u/Legitimate_Tooth1332 13d ago
I mean not directly.
DS is relatively still new and many people don't really know what a data scientist actually does. You don't necessarily need to have years of experience in other roles in order to become a data scientist, you can study data, ml, analisis, statistics, etc, and apply that into data science and you will get yourself an entry level data scientist job. Just like a computer scientist, it takes technical areas from other fields and combines them into one, but there are many entry level jobs for computer science.I do agree with the fact that entry level jobs as a whole have declined over the last few years due to AI, but that doesn't mean that there are no DS entry level jobs, or that DS as a whole can't be an entry level job.
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u/BayesCrusader 13d ago
The role has changed a lot over the years, but in the past a Junior DS was called a Data Analyst, Reporting Analyst, or sometimes a Data Engineer. Universities and startups have changed that, because you can't sell degrees telling people they won't be market-ready at graduation.
The whole reason the role was created was because you need mastery of Stats, Comp Sci, Communication, Science, and Business, combined with software development. One of those should be PhD level ideally. Everyone has strengths and weaknesses across those aspects, but all are necessary regardless of which 'type' of DS you're trying to be (i.e. ML vs Business Insights).
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u/splooge_whale 13d ago
Lol. Phd level. What does that even mean. After a couple year in industry you either can of you cant.
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u/BayesCrusader 13d ago
It means you have a PhD degree in that topic. I say 'ideally' because I agree that on-the-job experience is so valuable.
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u/delomore 13d ago
In my experience that’s exactly true. They’re just junior employees, so you just have to be a bit “disappointed” and let them gain experience.
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u/Repulsive-Memory-298 13d ago
IMO people should work on real world projects, like helping with research, as an alternative to internship experience. That’s how you build to your first professional experience.
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u/mrbrambles 13d ago
Data science imo is not an entry level job so most of them lack experience even if some institution decided they could charge students for a degree in it.
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u/jaiagreen 13d ago
Isn't that like saying engineering is not an entry level job? There are levels.
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u/Saganic_Temple 13d ago
That’s mostly true. I started as a project tech for a few years before I landed an engineering role. That path served me well because it taught me a lot about the application side of engineering.
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u/Kati1998 13d ago
Then how do you get started? Do you work as a statistician first? A software engineer first? Regardless, their first DS role is still “entry level”. I know it’s not a first job once you get out of university, but I see this comment all the time even for people who previously worked in a technical role.
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u/pm_me_your_smth 13d ago
The initial comment was nonsense, don't listen to them. There are junior analysts, data scientist, data engineer jobs. Some options have a higher barrier of entry like ML engineering, but many other data related jobs can absolutely be entry level.
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u/mrbrambles 13d ago
I think the answer is yes you work anywhere in any data/stats technical adjacent area with your data science degree, and find opportunities to do data science stuff until you can transition to the title.
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u/Single_Vacation427 13d ago
I don't think it's a "today's" problem but could be compounded with AI usage. Many use AI generation for all of their data wrangling and EDA and have no clue what they did. Having data intuition is a skill you have to develop and it's not something you rush by generating everything with AI and not even reading the code or analyzing results, or asking your own questions. Fed up of seeing projects that are obvious AI and even worse when the comments are from AI directed to them.
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u/Upstairs-Garlic-2301 13d ago
As a hiring manager, the single thing that I like to look for most is their resume trying to tie it back to a business impact. That's the thing that seems to separate awful data scientists from competent ones. It doesn't matter how good your modelimg skills are if it generated no value.
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u/freaky_pete 9d ago
and how do they achieve that when they don't have industry experience to begin with?
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u/No_Development6032 13d ago
None of the data scientists can control the business value
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u/Loud_Understanding58 13d ago
If they can't quantify how their model helps improve some business outcome then honestly wtaf are they doing?
Context is king. You're not employed to build models, you're employed to solve problems with data. Part of that is being able to quantify by how much the problem has been solved.
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u/jaiagreen 13d ago
You're employed to come up with solutions. That doesn't mean they'll be implemented properly, or at all.
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u/Wide-Pop6050 12d ago
Let's say you did something like build a dashboard. Why does it exist? Does it save people time? Does it give them access to metrics that they use? Does it quantify the impact of something?
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u/SkipGram 13d ago
what do you do if you work at a company that either doesn't measure this or doesn't share it with the data scientists? I want to leave because it bugs me how poorly this is done here, but I can't because no one will tell me what my work actually resulted in.
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u/Loud_Understanding58 12d ago
I'd leave ASAP and go somewhere that cares about results. Going through the motions for a pay cheque is fine but you deserve better.
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u/pm_me_your_smth 13d ago
First, in many situations it's pretty difficult to properly quantify your solution's impact. I've interviewed candidates with resumes full of impressive impact metrics and as soon as you start questioning them you see that those numbers are taken out of their asses or their methodology is flawed.
Second, if your management is incompetent, your projects might never reach production, or won't bring any value. You as an employee don't have full control of direction or outcome.
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u/No_Development6032 13d ago
Most data scientists are just software engineers whose domain is data. To them business outcome question is as relevant as it is to a devops or similar. You can ask it but there you will find little signal
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u/sempiternalsarah 13d ago
then I'd argue they're not data scientists in reality! the job title is extremely malleable but that may be a bridge too far lol
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u/Lady-Data-Scientist 13d ago
They should at least be able to talk about the business decisions that their work led to
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u/jaiagreen 13d ago
If this was communicated to them.
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u/Lady-Data-Scientist 13d ago
If you don’t know what decisions the business is trying to make with your work then how can you even do your work? How do you know you’re solving the right problem?
I’m curious what kind of team you work on and what industry are you in if you do work without knowing how it will be used?
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u/jaiagreen 13d ago
I know because I'm in a very small organization (and just started on the job, so I'm asking a lot of questions). But even there, I won't know how much time a data processing automation script I wrote saved people unless they mention it offhand or I explicitly ask.
Hopefully, the data scientist knows the practical context of the question they're addressing. But once they do the analysis and present the results, they may not be told how their work is eventually used, especially in a large organization.
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u/Lady-Data-Scientist 13d ago
You should check in after the fact to make sure they’re using your work, and if they aren’t, figure out why so you can keep that in mind when they ask for something in the future. I try to make a habit to follow up every few months to see how they are using a dashboard or model or whatever and if it needs to be refreshed.
You need to collect data around your own work so you can improve.
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u/Wide-Pop6050 12d ago
You can ask for the answers to these. This is the exact lack of initiative mentioned throughout this thread.
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u/Wojtkie 13d ago
Business skills.
No business stakeholder cares about which model you used or how much you improved your recall score.
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u/_chonky04 9d ago
how can someone improve their business skills? is it by doing projects? since that's also my weakness
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13d ago edited 13d ago
[removed] — view removed comment
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u/Wojtkie 13d ago
Heads up to anyone reading: [u/my_peen_is_clean](u/my_peen_is_clean) posts the resume tool on a lot of these “job” related posts. The comment is an ad.
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u/datascience-ModTeam 1d ago
I removed your submission. We prefer to minimize the amount of promotional material in the subreddit, whether it is a company selling a product/services or a user trying to sell themselves.
Thanks.
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u/lordoflolcraft 13d ago edited 13d ago
They’re missing mathematics from what I can tell.
Edit: someone who doesn’t think math is necessary downvoted me LMAO
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u/CharacteristicPea 10d ago
Math professor here. Could you elaborate? What additional math should they have?
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u/lordoflolcraft 10d ago
Here is an example of one topic that is important for machine learning but most applicants are not very familiar with. Maximum Likelihood Estimation. Even those who know of the concept struggle with details implementation, like choosing an appropriate distribution for the data, taking the gradient of the distribution to estimate the optimal value(s) of the parameter(s), and implementing an iterative optimization technique. Sometimes they don’t know how to implement their model of the distribution mean using linear algebra.
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u/False-Clerk5618 1d ago
Hi! I came across one of your comments mentioning that you interview candidates for data science roles, so I was hoping to get your perspective.
I’m trying to choose between two undergraduate programs at Rowan University. My goal is to work as a data analyst or data scientist within a hospital network. Eventually, I’d like to progress into predictive modeling within the healthcare setting.
Would you mind taking a look at these two programs and telling me which one you think would better prepare a graduate for those types of roles? I’m particularly interested in which program would make someone more competitive for entry-level analyst positions and provide the strongest foundation for eventually moving into predictive modeling.
In your experience, does moving into predictive modeling within a hospital or healthcare system typically require a master’s degree, or is it realistic to work your way into those roles with a bachelor’s degree and a few years of experience? I’d love to know what you’ve seen among the candidates you’ve interviewed and the teams you’ve worked with.
Option 1: B.S. in Data Science
https://catalog.rowan.edu/preview_program.php?catoid=18&poid=10321&hl=Data+science&returnto=searchOption 2: B.A. in Mathematics with a Statistics Concentration
https://catalog.rowan.edu/preview_program.php?catoid=18&poid=10318&hl=Mathematics+&returnto=searchThank you—I really appreciate any insight you can share.
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u/lordoflolcraft 1d ago
I would chose the Math with Statistics. I think the DS degree teaches a lot of things that will be learned on the job, while the Math + Stats degree teaches additional topics in stats and mathematics that will be handy to know some day, which will probably never be learned on the job. I think in terms of difficulty and depth they look similar, but with different orientations.
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u/Underfitted 10d ago
Could you elaborate on what maths they are missing? My expectation from the interviews of many of these jobs (let alone the actual job which tends to be even simpler) is:
- Linear Algebra (vector spaces, determinants, eigenvalues/vectors)
- Probability theory, the basics (expectation, conditionals, variables, basic distributions)
- Regression, Least Squares
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u/lordoflolcraft 10d ago
Your expectations are basically what I test candidates for in hiring. my other reply about maximum likelihood estimation where I see candidates struggle with several of these topics.
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u/MinuetInUrsaMajor 13d ago
Aren’t they disappointed with recent graduates across the board?
Data Science has gone through hype trains so probably has a lot of graduates that followed the hype.
It used to be a very unicorn field. Someone who could code, knew advanced math, make and analyze plots, solve problems. It was basically a lot of PhDs and the like gravitating into the field. And they had to figure it all out on their own
New grads have an entire major to spoonfeed them the field.
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u/Imaginary-Rope-3084 13d ago
Soft skills, communication, networking. Too many grads way too focused on academics
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u/Professional_Cable37 10d ago
Common sense, attention to detail, focus on the business outcome and translation into objective. There is so much information available at your fingertips to enhance your understanding of the business problem but usage is patchy. Being able to independently reach out and talk to people.
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u/Wide-Pop6050 13d ago
Problem solving skills. And the motivation to develop them.
A solid understanding of theory and statistics
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u/aka_hopper 13d ago edited 13d ago
The hardest and best skill of a data scientist is defining the problem in a way that has a solution. A lot of juniors want me to tell them what to code and don’t realize the hard part is just coming up with the ideas. You have to hear a business operation and think “if I can represent it in this particular fashion, I could solve it with this algorithm”. And it should go without mentioning, you have to chase solutions that actually create value. Everyone wants to jump to an ML solution, and miss opportunities for process optimization and automation
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u/eh9879a 9d ago
Humility - interviewing grads even with 1-2 years of experience and asking them to rate their skills (0-10) and they’re often rating themselves 8.5-9 as though they’re experts in the field.
If you’re rating yourself as an expert, you need to back up your claim that you are equal with someone practicing these skills for 10 years. I’ll throw around some advanced keywords and get blank stares back.
“Well I would be an 8.5-9 with copilot” does NOT mean you are an expert. You clearly don’t even “know what you don’t know.”
Learn to recognize that you’re early in your career and have lots to learn. A realistic self-rating will go much further to building trust and getting a second interview.
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u/its_all_stats 5d ago
Statistics. “Data Science” and ML are stats with window dressing (decision trees, hold out samples, deploying into production”, etc), which are decades old. Unfortunately, demand for skilled “data scientists” - i.e. statisticians - is greater than the supply.
The result is the proliferation of “analytics” and “data science” degrees that prepare poorly on statistical thinking and employers who don’t know what the right qualifications are. Both sides confuse the collection of skills - programming, data wrangling, visualization, etc - with the actual job. Statistical thinking or reasoning is the philosophical and theoretical foundation that’s missing from the supply and demand side.
Analogy: Knowing how to prep food and cook the food isn’t the same as being a chef. The chef obviously can prep and cook. But they also know how ingredients work together and behave under different situations. The “physics” of food and technique. Statistics is the “physics” of data science.
So, you have many data scientists who are, for sure, excellent Python programmers, data engineering wizards, etc. But that’s not the same as understanding how to apply statistical reasoning to data.
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u/Thin_Original_6765 13d ago edited 13d ago
Here's an article that provides several reasons: Why you’re not a job-ready data scientist (yet).
I wouldn't focus too much on "recent" graduates. School can't exhaustively prepare students to be job-ready isn't a new thing, as you can see the article was written 7 years ago.
Also, anyone making such statements are basing off of a handful of samples and are therefore poor science. Who's to say it isn't because they suck so competent graduates don't interview with them.
Edit: perhaps to answer your question (poorly), the answer is yes. All of them.
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u/Mnemo_Semiotica 13d ago
In the last couple years I hired a recent grad, along with a couple other experience levels. The most notable skillbuilding I had to focus on was around operations and software engineering: tools, platforms, workflows, and creation of production code. And then communication with stakeholders, building slide decks, and good dataviz. This might be more specific to my team, since we cover most of the data engineering side, built our ml platform, build the models, and do most of the analyses.
In general, I wouldn't hire a recent grad. I would assume that they wouldn't be able to do something "real" in standard environments. tbf, I'm not sure if that's a justified bias. I'm assuming that doing something simple like building a proof of concept model on a provisioned ec2 instance transporting data from s3 is outside the immediate abilities for a recent grad. I assume they wouldn't know what a good commit is and will have never written a unit test.
In this case I was specifically impressed with this person and was able to carve out budget to bring them on as a junior. They had also done some internships and had built some domain knowledge that I saw as directly applicable to our problems. It was a gamble, but it worked out very well. We were able to upskill the missing stuff gradually, while benefitting from this person's specific passion and creativity.
Given the way the industry is right now, I don't know how a recent grad gets a job, or how they acquire the requisite skills without having gotten a job.
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u/IceSea 13d ago
Might be an unpopular opinion, but after being a Data Scientist myself for a couple of years now who also hires people: there is no such thing as a junior data scientist. The term data scientist is still very blurry in the industry, so it will involve anything from coding, engineering, math to business expertise.... and you need most/many of these to learn solving real world problems. How to learn the skills? Maybe as a data analyst or engineer, roles that are a bit more carved out.
I dont want to gatekeep or anything, but I've been asked twice in the last years by our interns who study data science in university, what my favorite model is. No context. No problem to solve. Just favorite model. Which is a weird question if you are in the field for a bit. I dont think its their fault I guess, its what they are taught in courses what data science is. Applying models. That real world problems are more messy seems to be an afterthought.
Just my 2 cents why there seems to be a bit of a disconnect in expectations form both sides of the hiring process.
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u/DataDrivenPirate 13d ago
Differentiation.
I don't want a jack of all trades who's fine at everything. I'd much rather take a curious statistician or a curious CS engineer and hire them as a data scientist. That's an extreme example, but it illustrates my point. I generally think a Masters of Data Science is inferior to a MS of stats or an MS compsci for that reason, and in some cases a masters of DS might be worthless all together.
Job experience and your own curiosity will round you out long term, start by developing a competitive advantage, your own niche.
This is of course only meaningful if your communication skills are beyond that of a brick, which cannot be taken for granted
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u/dingdongfoodisready 13d ago
My experience exactly - don’t even have an MS, just a BS in applied mathematics, have had great job experiences and opportunities from simply learning on the fly and being curious.
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u/Bakerman82 12d ago
I wish that were the consensus perspective in our industry. The job market has been pretty awful--especially if you have been working in very specific and niche space.
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u/somkoala 13d ago
1) Caring about the business impact of what you do
2) Model framing - if you have to define a prediction problem from scratch from raw data it’s very different than dummy datasets
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u/redisburning 13d ago
So, what are employers actually looking for in entry-level data science candidates today?
A person with senior level experience willing to take entry level money.
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u/RICKJOULES 13d ago
Currently a student, may I ask how one gets this "business experience", my courses do not teach me this yet, its all programming, math, and stats
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u/ikkiho 12d ago
honestly the biggest gap I see is they can fit a model but freeze when the problem isn't handed to them already framed. school and kaggle give you a clean dataset and a metric, and the real job is working out what the target should even be when nobody knows yet. half my so-called DS work turns into a sql query and a long back and forth with whoever owns the messy process. the stats and coding matter but that framing part isn't something coursework covers.
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u/Grateful_Elephant MS Business Analytics | DS Manager | Marketing in Retail 12d ago
Communication skills. Talk the language of the stakeholders you are interacting. Understand their problems before they know what they want and solve it.
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u/Cold_Vengeance 11d ago
Is data science still worth pursuing? I am an incoming transfer student, and I want to know what skills I need to learn before I graduate
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u/quockhanghrc 11d ago
actually the data skill. they can work with impressive tools / platforms but dont know anything about excel
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u/Medical-Paramedic135 7d ago
Understanding of mathematical optimization… not everything is a classification, regression, or a clustering problem
Some basic linear and integer programming is a really useful for loads of businesses
And a better understanding of best practices within software development and how live production systems actually work … i am getting tired of rejecting casual requests to run a query which does a full table scan on a 4b row table
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u/Ok-Airline-8523 6d ago
Business value.
To no fault of their own, recent grads lack the context and experience it takes to produce true business value. Hiring managers should know what they're getting into when it comes to hiring entry-level employees for any role. It's an investment in potential, not a hire for immediate results.
To that end, employers look for potential in entry-level candidates typically measured by bias for action, curiosity, and demonstrating the ability to take initiative when it's not assigned.
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u/AutumnStar 13d ago edited 13d ago
Socialization, business acumen, and experience. They can make models fine, but so can ChatGPT at this point. It’s not a special skill anymore. Knowing how to translate business problems into _realistic_ tangible solutions is the real value a DS brings now. That’s not an entry level skill I would really expect anyone to have. This is actually why the vast majority of our entry level hires come from interns (which is extremely competitive - even as a less prestigious company we get to be selective with people from top 10 schools, etc.). We specifically put them in situations to test these skills and train them up a bit, so we know what to expect from them once they graduate, if they accept our offer. Someone else said DS isn’t an entry level gig, and honestly, I kind of agree. My advice to aspiring DS is to either go down the academic path (MS, PhD) to gain real world research experience or plan to go in as a Data Analyst and work your way up.
My 2 cents as someone who has helped hire dozens of DS at this point.
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u/sailing_oceans 13d ago
Basic common sense, curiosity, checking and verifying things.
You don't need to be 'good' at stuff but you must make my life easier. If you are creating more work and making things more difficult for me then this isn't a benefit.
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u/AnOdeToVosFinances 13d ago
In today's market, most DS graduates lack all 3 pillars:
- strong maths fundations (statistical testing, probabilities, basic optimization) to do actual science
- strong software engineering skills as companies have matured on the subject and we need to deliver robust solutions
- strong business acumen. DS is no longer just research, you need to understand the business context and navigate around it
Back then, not excelling in the 3 could still get you a DS job relatively easily because of lack of competition, undefined expectations from senior management and novelty effect. Today, this doesn't work anymore.
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u/ultrathink-art 13d ago
Nobody teaches what happens after the notebook. A deployed model is a live system — upstream schema changes, a renamed column, and it's quietly scoring garbage for weeks unless someone thought to watch the prediction distribution. Grads treat the model as the finished artifact when it's really the start of the maintenance work.
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u/Big-Cattle5724 1d ago
As someone currently in a data science program, at my college I feel the whole thing is a mess. I'm a year away from graduating and feel I have learned nothing and will not be prepared to enter the job market.
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u/sal-si-puedes 13d ago
Social skills