[HN Gopher] Good data scientist, bad data scientist
___________________________________________________________________
Good data scientist, bad data scientist
Author : ian-whitestone
Score : 109 points
Date : 2021-05-11 16:36 UTC (6 hours ago)
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| vinay_ys wrote:
| Good data scientist described here seems to have unrealistic
| expectations at super human level of know-it-all/do-it-all.
|
| I think there are more well-established job architectures like
| business intelligence analyst, data engineering, user experience
| designers, product manager, software engineer etc - these roles
| in combination serve to do a lot of what is described here as
| data scientist. These roles are easier to hire, have well defined
| career paths and good ways to get job satisfaction and can scale
| well as the business-problem-space/orgs grows.
|
| I think the scientist label should be reserved for those who
| actually do the scientific mathematical research - specialists
| who have done deep research in specific areas.
|
| For applying pre-existing sciences to solve practical business
| domain problems, we need lots of engineers, analysts and managers
| etc who are all trained with AI-first software development
| practices and just a few specialist data scientists.
| gyulai wrote:
| > Good data scientist described here seems to have unrealistic
| expectations at super human level of know-it-all/do-it-all.
|
| Hmm. Know-it-all/do-it-all is a useful standard to strive for,
| though, even when, in practice, one will often fall short in
| one area or another.
|
| One of my personal frustrations is that I have invested heavily
| in trying to be well-rounded and it doesn't quite pay dividends
| because of how often I find myself confronted with prejudice of
| the form "because he's good at X, that probably means he's bad
| at everything else". For example, if the first impression I
| leave on someone is that I'm good at math, they'll often jump
| to the conclusion "because he's good at math, that probably
| means he's bad at databases". If the first impression I leave
| is that I know a lot about finance & economics, they'll assume
| "because he knows a lot about finance & economics, that
| probably means he can't do projects in a technical domain" and
| so forth.
| [deleted]
| monkeybutton wrote:
| Agreed. The second point about pipelines stuck out to me:
|
| > [Good DS] will often build these pipelines themselves. Bad DS
| thinks it is someone else's job.
|
| In a small environment, sure, do the job so it gets done! But
| in larger more corporate settings the 'cowboy' approach to
| pipeline building is not sustainable or even feasible. Am I a
| bad DS because I can't provision VMs, open firewalls, replicate
| production DBs and build hooks in other teams' services to
| expose data? No, its not my job. A good DS collaborates with
| other teams and sysadmins to build a pipeline that is
| maintainable and monitorable, and doesn't do it all themselves.
| commandlinefan wrote:
| > seems to have unrealistic expectations
|
| Well, the expectations aren't unrealistic - if you were to
| grant the "good data scientist" a reasonable amount of time
| rather than demand that everything be done by this afternoon,
| which is what most "real data scientists" are up against.
| klmadfejno wrote:
| > Good DS starts simple, ships, and then iterates. Bad DS starts
| with the most advanced technique they know.
|
| > Good DS is constantly learning & evolving their toolbox. Bad DS
| stagnates and sticks with what they know.
|
| These are the big ones imo. But not super obvious. As a junior
| data scientist I never needed to use anything but regularized
| linear models and decision trees. Maybe a random forest but the
| explainability usually wasn't worth it.
|
| Recent explainability tools like SHAP have changed this somewhat.
| But for the most part I think its still ok for the average data
| scientist to be regularized linear models, decision trees, and
| then occasionally, idk, a LightGBM or Catboost + SHAP for
| explainability. A lot of people still don't know about these, and
| it's now a decent test for whether people are really trying to
| stay up to date.
|
| But if they're not, I don't really care.
| ska wrote:
| You can't model your way out of poor data.
|
| It's a near certainty that good data + basic modelling delivers
| the overwhelming majority of real value, globally.
| beckingz wrote:
| Turns out the right data makes logistic regression go a long
| long way.
| joncp wrote:
| Great list. The rules apply to knowledge work in general.
| tmule wrote:
| I liked the article, but realize that in a decade of work in
| Tech, I haven't meet a good data scientist!
|
| I'll also add: a good data scientist knows his/her strengths, and
| doesn't try to become a unicorn.
| sgt101 wrote:
| >Good DS thinks from first principles. Bad DS accepts everything
| they have heard or seen as the ground truth, or the best way to
| do something.
|
| Domain knowledge - and the humble attitude that can get
| stakeholders to give it to you is fundamental to understanding
| data and how models will be interpreted and used. There is not
| enough "listen to others" in this list (although I read the
| "listen to customers" at the end). Listening... listening listen!
| waserwill wrote:
| This reminds me about a time when some geneticists tried to
| find genes associated with a particular disease, to try to
| unravel why it occurs. Complex trait, no single answer, so they
| genotyped thousands of people with and without the disease, and
| ran the stats. And... nothing.
|
| What has one common name is actually several similar diseases,
| and the geneticists would have known that if they paid
| attention to the clinicians. Listening and incorporating
| knowledge is key.
|
| [I'm thinking of an early glaucoma GWAS, IIRC, though there are
| similar cases.]
| evandijk70 wrote:
| I think this story is very, very common. Still, some complex
| diseases (eg. Cystic fibrosis, Down syndrome) do turn out to
| be simple on a genetic level, so there is some merit to this
| approach.
|
| Moreover, there is currently no better way to understand
| diseases genotyping thousands of people with and without the
| disease and 'running the stats', so it's worth the try
| _fullpint wrote:
| Oh man! Domain knowledge is absolutely HUGE. I cannot even
| begin to tell you how much I've had to dive into literature on
| topics well outside of my domain to begin to understand how to
| use my outside perspective to come up with solutions.
|
| Respecting stakeholders, and being able to be humble about
| asking for help understanding the domain is paramount.
| noodlenotes wrote:
| I would say that a good data scientist can quickly estimate
| where their time is best spent, either accepting what someone
| else has told them as-is or investigating themselves from the
| ground up. There's _always_ more to investigate so using your
| time efficiently is one of the most important DS skills. Like
| solving a multi-armed bandit problem.
| dudeman13 wrote:
| Sounds like something that is a function of your domain
| knowledge and your data science skills will have very little
| to do with it
| antipaul wrote:
| If there is a lot to build, like data pipelines or software apps,
| as opposed to just "analyze", I think it helps to add a word for
| the discipline of "engineering", eg software, data, backend
| engineering.
|
| The role mismatch between data and other engineers, vs actual
| (data) scientists, makes it difficult for decision makers to
| figure out which one they need
|
| References
|
| https://www.oreilly.com/content/why-a-data-scientist-is-not-...
|
| https://medium.com/airbnb-engineering
| analog31 wrote:
| "A human being should be able to change a diaper, plan an
| invasion, butcher a hog, conn a ship, design a building, write a
| sonnet, balance accounts, build a wall, set a bone, comfort the
| dying, take orders, give orders, cooperate, act alone, solve
| equations, analyze a new problem, pitch manure, program a
| computer, cook a tasty meal, fight efficiently, die gallantly.
| Specialization is for insects."
|
| -- Robert Heinlein
| sgt101 wrote:
| Data scientists take data assets that were not designed to be
| used for a particular task and set them to be used systematically
| and with integrity for that task. It's something that comes from
| having lots of data in enterprises which can be exploited to
| create value, but can also be used to make very bad decisions and
| confuse the hell out of everyone. Using data and using data well
| are two very different things.
| albertTJames wrote:
| I feel this extends to other field. Its basically describing two
| of the big five personality traits conscientiousness and
| openness.
| linspace wrote:
| I think there is this false stereotype of the DS obsessed with
| cool techniques and detached from the business. Most DS want
| their work to have impact, actually like most people. But
| successfully applying data science is hard. We have incredibly
| mature tech for other problems, like for example databases, a
| marvel of engineering, and in comparison DS is a kludge. The
| value DS provides per $ is much lower although is considered a
| competitive advantage (DBs are a commodity) and I think this is
| one of the reasons it feeds this stereotype.
| t8e56vd4ih wrote:
| most data scientist are just jupyter notebook and sklearn cowboys
| who know a lot of the buzzwords but lack even basic statistical
| understanding.
|
| and I've met a lot of data scientists.
| gyulai wrote:
| I agree with most of what he's saying but reading the first
| sentence almost stopped me in my tracks when I got to "obsessed".
| I wonder when exactly it was that "obsessed about this" and
| "obsessed about that" became a _good_ thing. ...it 's thrown
| around way too much these days, and I for one think that being
| obsessed with anything, regardless of how positive a thing it is,
| always speaks to a psychology that is defective in some way or
| another.
| autokad wrote:
| I guess you can't work for Amazon then.
|
| You'll never get passed the Customer Obsession LP
| [deleted]
| lhnz wrote:
| "excited by"
| SuoDuanDao wrote:
| An interesting description of obsession I've come across is
| that it's what happens when the will is frustrated. So maybe
| temporary obsession can be a good thing, if it's a sign
| someone's chosen a task so difficult that they need to expand
| effort to overcome a significant hurdle.
| concreteblock wrote:
| Doesn't it just mean that the meaning of the word has changed?
| xapata wrote:
| Is changing, not has changed. If it already had, no one would
| remark on it.
| gyulai wrote:
| > Doesn't it just mean that the meaning of the word has
| changed?
|
| ...I do feel a bit bad amount mentioning it, because it's
| pretty tangential to what the article is actually about. That
| said: Changes in meanings of words often go hand-in-hand with
| broad-based changes in the way people _think_ about
| something, and it 's useful to reflect on whether or not one
| wants to go along with that thinking.
|
| There is even a bit of a clichee anyway around sciency-
| engineeringy folk falling within the "obsessive" range of the
| personality spectrum in the very original sense of the word
| where it might be something that a psychotherapist might work
| on to try and rectify. So when I see it in this particular
| sphere being attached to a positive value judgment and even
| with slightly prescriptivist overtones, then it's something
| that to me really "pops" and it's been happening to me more
| and more lately.
| ska wrote:
| "focused on" is probably better terminology.
| ian-whitestone wrote:
| Obsessed may have been overkill :)
| ubitaco wrote:
| > Good DS understands the basics of web technology
|
| I'm not a data scientist but a portion of my job is creating
| pipelines, data analytics and such. I also only have a bare
| minimum knowledge of web technology. Why is knowledge of web
| technology part of being a good Data Scientist? Or is this point
| oriented specifically for data scientists working in web based
| companies?
|
| Genuinely curious. I could imagine myself working as a DS in the
| future and that's why I found this article interesting.
| antipaul wrote:
| Why web technologies? You may have to build a web app to
| display some data or results.
|
| But like some top comments say, data science is super broad and
| it just depends on your team.
|
| Mature orgs and teams have a clear idea what their focus area
| is, while others don't have a cogent conception of what
| constitutes "data science"
| jefb wrote:
| I don't think there is a single correct answer here, but I'll
| offer a few insights from personal experience.
|
| Firstly, valuable data tends to live in places accessible via
| web technology. Maybe you need to fetch a bunch of XML files
| from an FTP site? Having a clear understanding of all the
| nuances you're about to encounter will set you up for success.
|
| Secondly, valuable data tends to be generated by web technology
| itself. Understanding that lifecycle can inform analytical
| strategy.
|
| Finally, some data scientists add value by informing decision
| makers. One of the most powerful things you can do for them is
| give them a mobile friendly secure web experience that puts the
| data they need directly at their finger tips. While yes,
| Tableau et al. are an option here, you'll be ahead of your
| peers by knowing how to DIY it when it counts.
| jll29 wrote:
| A data scientist is someone that people wish was a unicorn but
| that is neither that nor a scientist, despite the name.
|
| People who are _actual_ scientists usually in industry go by the
| name "scientist" or "research scientist", although they just data
| just as much. You can recognize them by the peer reviewed
| scientific papers they publish, often preceded by filed patent
| applications, as their work is novel. A real scientist wonders
| why some people call themselves "data" scientists, because
| science has always been about data, modeling and measurement.
|
| But back to our "data scientist":
|
| On a good day, she is generating value from the company's data to
| increase customer retention.
|
| On a bad day, she is just doing the ETL prep work so the boss'
| other assistant can make that spreadsheet that aggregates the
| data that the boss' PPT slides will show.
| tmule wrote:
| Many (most) scientists are also not everything they're made out
| to be. Medicine, for example, has had a real replication
| crisis. It's important to distinguish between Science and
| scientists. Finally ...if you're running regressions, it's
| better to get paid 300K than 130K.
| borroka wrote:
| This sentiment is quite popular among those who would like to
| have the same popularity that data scientists currently (well,
| more a few years ago, since there are many more critical voices
| now) have, but they don't.
|
| Data science is a generic name. There are DS like me who have
| been "actual scientists" and others who until yesterday were
| working on dashboards and Excels files with 100 tabs open and
| pivot tables as far as the eye can see. Whatever, it is a name.
| What about "engineers"? It is a title with no legal value,
| people in the US can call themselves software engineers, but in
| many other countries, they could not. And who is a writer?
| Somebody making a living out of writing, somebody who has been
| published even if they got zero money for it and the magazine
| editor was their cousin, or else?
|
| People in my team do causal modeling, use reinforcement
| learning for network configuration, NLP for chatboxes, computer
| vision for face ID, and (again) network configuration. They are
| all called data scientists. Thinking that what people who have
| the title "Data Scientist" do is "generating value via
| increased consumer retention" or "ETL for Excel files for the
| boss" is between misinformed and laughable, but mostly
| laughable. The world is much bigger than that.
|
| Then, I agree that "learning from data" as a specialty has been
| over-hyped, and most companies do not have the maturity to take
| advantage of ML prediction, causal and statistical modeling,
| etc., but that's the nature of the world: one can take
| advantage of it or being bitter about it. I took advantage of
| the hype and I am fine, happy, and with no regrets. If tomorrow
| someone would propose to use for the same job the title "Data
| Monk" and it paid more, were more visible, and led to more
| career opportunities, I would grab it as quickly as I would
| grab 100 dollars floating in and out of the sidewalk.
| didibus wrote:
| What would be the difference in role between a data scientist and
| a product manager in this case?
| minimaxir wrote:
| Data Scientists can provide PMs with data and analysis to make
| better-informed product decisions. Then you can get into more
| detail, such as DS building tooling/dashboards/models for
| PMs/stakeholders to self-serve and save time for everyone.
|
| Yes, there's some overlap with a Data Analyst position, but
| there's enough day-to-day work to differentiate.
| tpoacher wrote:
| I was hoping this would be a variant of Good Cop Bad Cop as a
| technique applied to datascience. It's not.
| beforeolives wrote:
| This is a good list... for one type of data scientist - the type
| that has heavy involvement in product and business decisions.
|
| Other data scientists are basically software developers with a
| very specific domain, a third kind focus a lot more on research
| and many data science jobs are some blend of all of these things.
| My point is that the author mentions in the intro how data
| science is very broad and then continues to focus on what's only
| a subset of all data science jobs.
|
| With that in mind, the list is actually spot on - it's just good
| to know that it isn't relevant to many data science jobs.
| ian-whitestone wrote:
| Agree with you that not all of these things will apply to every
| DS role - particularly research heavy ones. But my hope is the
| vast majority will.
| mturmon wrote:
| Yep, some research-oriented DS people are (rightly) obsessed
| (correct word) with a particular family of techniques
| (variational inference! random forests! adversarial
| networks!) and work to find problems to apply that family to.
| They literally do pattern-match on their techniques with
| every new problem they encounter, and move on if it doesn't
| fit.
|
| A lot of the other of your distinctions do still apply to
| such people, like knowing where the data comes from, knowing
| when to stop, and adjusting the message to the audience. So,
| still a good list.
|
| Also, even the research DS people need to evolve their
| techniques over time.
| SilurianWenlock wrote:
| Is data science for most businesses just bs?
| mywittyname wrote:
| No.
|
| But (and this is a Big But), the value of data science comes at
| the end of the data journey. Businesses need to be capturing
| data that is relevant and accurate before they can start
| analyzing it and deriving any value.
|
| My experience with clients is that they get a ton of value out
| of that first step of thinking about what information they want
| to collect about their customers, then actually collecting it
| (or, conversely, surfacing what they already collect in a
| meaningful way). So while they come in wanting some kind of
| neural network powered prediction engine or whatever, they are
| often really impressed by pretty basic dashboards about their
| customer behavior.
| ska wrote:
| Not bs. But there is both a real GIGO problem, and a problem
| with under specification. It's certainly easy to propose DS
| analysis that are unlikely to have much return.
|
| Thinking "data science is hot, we should do that" is different
| than "we have all this data and don't understand what it
| means". The latter is more likely to lead somewhere
| interesting.
| screye wrote:
| This highlights one of my main complaints about the DS role. You
| are expected to have strong business intuition, sufficient coding
| skills to hold down a SWE role, a strong background in
| stats/math, know all the ML/DS specific skills and lastly, have
| technical depth in the subdomain you are looking to solve. All of
| this, while being paid the exact same as someone on the SWE or PM
| track.
|
| No one can do it all. DSs that do 70% of these are the best of
| the best.
|
| Mature DS groups have figured out that you have to pick your
| poison, and focus on archetypes rather than a 'well rounded' DS.
| Here are a few DS archetypes that I've seen.
|
| 1. The NLP/Vision/RL domain expert: High depth, low breadth
| people. Not very concerned with business intuition. Strong grasp
| of math for their domain. Moderate coding abilities, but
| pipelining for their field is fairly well defined. What is SQL?
|
| 2. The Generalist : Comes close to the 'good data scientist'
| outlined here. Never publishes, solves DS problems, will probably
| struggle to reach principal IC level in any specific product
| group because they lack the prerequisite depth. Will often become
| a manager down the line though and can also become an excellent
| PM at some point. SQL is their life blood. The less business
| savvy people see them as MBA-adjacent. But, they are super
| important.
|
| 3. Mr Maths or the Statistician : Pairs excellently with #4
|
| 4. The MLE who doesn't want to be an MLE - Excellent coding
| skills. Sufficient ML/DS skills. Just hasn't found a way to get
| their foot in the door to transition to a DS role without taking
| a pay cut.
|
| 5. The Researcher : Hiring a researcher in the wrong team can
| lead to a completely ineffective team. Also, not having a
| researcher in a team that needs it can lead to everyone going
| around in circles.
|
| Top DSs will manage to host a max of 2 archetypes in them. Trying
| to get your DS to host >2 archetypes, is a losing battle. This is
| as good as it is going get. Also, most teams don't need all
| archetypes.
|
| Identify the archetypes you need. Get some coverage over them
| through your hired DSs and let them continue growing along their
| selected archetypes.
| 6gvONxR4sf7o wrote:
| > Top DSs will manage to host a max of 2 archetypes in them.
|
| This ignores experience. Top DSs will manage to have maybe one
| archetype per some number of years on the job. You can find
| unicorns, but they all have many many years experience and
| you're going to have to pay for them.
| whatshisface wrote:
| > _All of this, while being paid the exact same as someone on
| the SWE or PM track._
|
| Why not pay top quality DS roles more than SWEs?
| IdiocyInAction wrote:
| As always, it's supply and demand. DS is often not needed as
| much as SWE and there is a lot of supply for DS, due to hype
| and ease of transition from people in other fields.
| huac wrote:
| often (usually?) DS are paid less than SWEs of the same
| level!
|
| I have plenty of cynical thoughts as to what drives that
| compensation gap. Maybe the simplest is just that there is
| high supply of people with these baseline skills and it isn't
| easy to distinguish if somebody is good or not.
| alexgmcm wrote:
| I think there is just more demand for SWEs. Nearly every
| company will have software engineers, but not every company
| has data scientists and even the ones that do will almost
| certainly have more engineers than data scientists.
|
| After all, you can't use data science to optimise your
| product or service if you don't have sufficient engineers
| to build it and maintain it in the first place.
| Godel_unicode wrote:
| %s/not/don't companies/
| chudi wrote:
| I'm a swe that moved from backend to a ds role and then as a ds
| manager at my company and this is spot on. If I advertise a job
| por a ds position I have to mix all these archetypes and get
| used to at best have a solid 4 that wants to pivot to ds as
| this is the archetype that knows that we are creating real life
| data products not just using the latest model or beating some
| metric.
| omgwtfbbq wrote:
| >All of this, while being paid the exact same as someone on the
| SWE or PM track.
|
| Actually at FAANGs especially they are usually paid less,
| sometimes substantially.
| hackton wrote:
| Sadly on point. Some additions to your list of skills, from my
| exp.:
|
| - Sufficient engineering skills to hold down a Data Engineer
| role
|
| - Excellent at explaining and presenting your results/work to
| all sort of audience (users, other DSs, management, etc).
|
| - Very good at Data Viz
| martingoodson wrote:
| Learning all this is not really that difficult. No more
| difficult than a biochemist training in subjects as diverse as
| organic synthesis (making stuff in test tubes), Raman
| spectroscopy (prediction of chemical structures using
| vibrational signatures) and DNA sequencing (computational
| analysis).
|
| It's only because data science is much newer than biochemistry
| as a field that it seems beyond the grasp of an individual.
| It's perfectly possible to learn (and to teach) all of the
| things you've mentioned.
|
| And what has pay got to do with it? Since when is pay
| correlated to how much you need to study (see, for example,
| musicians)?
| jltsiren wrote:
| Data science is a role, not a field. It's similar to but
| wider than the applied statistician role that is well-
| established in many fields of research.
|
| You have a background in one field, but you are working to
| solve problems in another field (e.g. biochemistry). To do
| that, you must understand biochemistry well enough to be able
| to contribute. You are probably far from the best biochemist
| in the team, as you were hired for your methodological
| skills. In order to solve the problems, you may need tools
| from a number of fields, including statistics, machine
| learning, software engineering, data engineering,
| mathematics, and theoretical computer science. No matter
| which field your original degree was in, it's insufficient in
| both depth and breadth. You must keep learning new things and
| rely on others with complementary skills.
|
| I work in bioinformatics, which is basically a more
| established flavor of data science. I have worked with people
| from a variety of backgrounds from electrical engineering to
| genetics, and everyone has had obvious gaps in their skills.
| Except maybe one or two people, but they are world-famous
| experts who are unnaturally curious about everything.
| alexgmcm wrote:
| Pay has a lot to do with it because if you can switch to an
| engineering role (SWE or Data Engineer) and have more focused
| responsibilities and a higher salary then that's what most of
| them will do.
|
| Although given the demands made for a DS role are often
| unicorn-level I don't even think increasing pay would help.
| martingoodson wrote:
| The parent comment says 'while being paid the exact same as
| someone on the SWE or PM track.' Not 'less than a SWE', as
| you imply.
|
| Why should a data scientist be paid more than a SWE?
| Because they have to learn several different topics? That
| is not such a big deal in my opinion (I work as a DS).
|
| This language of 'unicorns' has been highly damaging to the
| field. There is nothing magical about a job which requires
| a lot of varied technical knowledge. Try looking at a
| syllabus for some other scientific subject. It's fairly
| normal.
| alexgmcm wrote:
| I work as a DS as well. I don't think there's such a
| thing as "should be paid more" - the market shows us that
| SWE's are more highly valued presumably because there is
| more demand for those skills.
|
| However, this will lead to people migrating from DS to DE
| and SWE roles if the compensation is relatively better.
| Yet we see articles about a 'shortage' in DS when they
| just aren't paying as much as a similar skill-set can get
| in a different role.
| travisjungroth wrote:
| > the market shows us that SWE's are more highly valued
| presumably because there is more demand for those skills.
|
| I think it's that it's that a tech company can more
| consistently make money from a SWE than any other role.
| You can always roll together an app and sell it. For
| every other role[0], you provide value to the
| organization, which eventually makes its way to the
| customers.
|
| This is why the software bootcamp grads have fared better
| than the DS bootcamps (and ML bootcamps). A company can
| get a lot of value from a pretty crummy SWE and is
| willing to pay for it. A crummy Data Scientist, not so
| much.
|
| [0] Sales is also similarly direct, depending on the
| industry. They enjoy a similar status.
| v8dev123 wrote:
| According you, Dyslexic person can't become a DS person just
| not because they love data but because ...
|
| You are expected to have a strong background in stats/math
|
| But waaaait
|
| How come you forget about Philosophy?
|
| Math and Stats based on Philosophy. You will have to learn
| Philosophy to become Super DS person!
| [deleted]
| SilurianWenlock wrote:
| I'm struggling to understand what people think is so difficult
| about all this data science stuff. The maths is very basic,
| even in "advanced" ml. Nor is it hard to learn backend software
| engineering for the purposes of 99% of companies.
| sdenton4 wrote:
| It's all about epistemology. How do we know what we think we
| know? How do we come to know things we didn't know before?
| And how can we trust those conclusions?
|
| Even if the math is basic, it's really, really easy to draw
| bad conclusions, look at the wrong problems, not realize that
| your data is more incomplete than you might think, etc etc
| etc. Guarding against these bad results - figuring out how to
| actually manufacture new knowledge - is the heart of the
| problem.
| visarga wrote:
| By the same logic what is so difficult about programming
| computers - it's just a bunch of zeroes and ones, very basic
| operations.
| v8dev123 wrote:
| I spent 15 years of my damn life to become a dev and you
| don't know what's it like to be a beginner.
|
| If you can re-read what you wrote with a beginner's mind, you
| will see how wrong you are.
| amcoastal wrote:
| 99% of companies? Definitely not. The skills needed to do DS
| in business or healthcare are not very correlated with doing
| DS for the physical sciences. Which is the whole point of
| this comment thread, sure you can understand DL, but you also
| have to have an understanding of the field to know what type
| of DL to use. For example, in my role, I came with knowledge
| of machine learning but had to learn complex fluid physics to
| be able to know what type of DL techniques to apply or
| develop.
| willdearden wrote:
| https://www.uptake.com/blog/good-data-scientist-bad-data-sci...
|
| done here too
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