Is data science so easy that the market will eventually become saturated and jobs will be difficult to find?

Updated on : December 3, 2021 by Jolie Espinoza



Is data science so easy that the market will eventually become saturated and jobs will be difficult to find?

Unfortunately, most people have started to assume that data science equals computer science and math / statistics. If we take this definition, data science as a field can be considered relatively easier (than it really is).

Actually, data science equals computer science, math / statistics, and TOPIC EXPERIENCE (in a particular domain). Finding all three attributes in a person is extremely difficult and I would guess that there are probably fewer than 5,000 such data scientists worldwide.

Finally, it is not about data science or even computer science. It is about th

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Unfortunately, most people have started to assume that data science equals computer science and math / statistics. If we take this definition, data science as a field can be considered relatively easier (than it really is).

Actually, data science equals computer science, math / statistics, and TOPIC EXPERIENCE (in a particular domain). Finding all three attributes in a person is extremely difficult and I would guess that there are probably fewer than 5,000 such data scientists worldwide.

Finally, it is not about data science or even computer science. It is about the end customer having a business problem that they want to solve. In fact, most of the time, he / she is willing to pay for the solution and doesn't really care if we solve the problem using big data science, small data science, use a genius, or just get the correct answer from! God! Most of the time, the underlying problem can only be solved by using appropriate subject matter expertise. For example, even within an airline, the definition of "on-time compliance" can vary from department to department. So the key is to come up with consistent definitions for a particular industry and then solve the problem.

Hadoop, Spark, R, Python, etc. they will appear and disappear; Simply learning them will not make a person solve real-life problems, and therefore it is not clear that this will make the person a data scientist.

No, data science is not easy. Simply shapeless and not "professionalized".

By this I mean there are no standard toolkits, no educational curricula, no certification bodies, and no specific career paths that lead to becoming a data scientist; however, all the essentials are there, and they are not easy to acquire, assemble, or apply well.

Yes, one can learn R and Hadoop and "claim" to be a data scientist, but that is far from the truth. In comparison, one can also take some medicine classes and claim to be a doctor or watch some television shows in the courtroom and claim to be a lawyer. The difference is that the

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No, data science is not easy. Simply shapeless and not "professionalized".

By this I mean there are no standard toolkits, no educational curricula, no certification bodies, and no specific career paths that lead to becoming a data scientist; however, all the essentials are there, and they are not easy to acquire, assemble, or apply well.

Yes, one can learn R and Hadoop and "claim" to be a data scientist, but that is far from the truth. In comparison, one can also take some medicine classes and claim to be a doctor or watch some television shows in the courtroom and claim to be a lawyer. The difference is that the disciplines of medicine and law are "professionalized." As a result, they can keep an eye on your doors by setting rules about who can call themselves a "doctor" or a "lawyer." In data science, we still can't do that.

To the extent that R and Hadoop, they are just part of the data science toolkit. They do not constitute "data science" any more than a scalpel constitutes "surgery." In the same way that physics relies on mathematics, data science relies on statistical tools to handle large and small data sets, structured and unstructured data, etc. But the mathematics of physics is not a substitute for scientific thinking, analysis, approach, or method. And neither are Hadoop and R substitutes for understanding behavior in data.

Statistics, specifically, is largely concerned with methods for testing hypotheses using data; therefore, before one can constructively use Hadoop or R, it is necessary to know the statistics and know them well. Because, unlike statistics, which is primarily concerned with testing hypotheses and stops there, data science focuses on the implications of systematic deviations from hypotheses (as shown by statistical tests) and the most important conclusions. that we can get as a result of these deviations.

Also, aside from data science that requires a cumulative knowledge of numerous tools or sub-disciplines such as statistics, R, Hadoop, etc., one should be able to use those tools to answer important business questions and achieve business results, neither. whose initiative is derived directly from the knowledge of the tools. That skill, ability, experience, or talent is what the data scientist brings to the table, allowing him to rightly call himself a "data scientist."

This leads me to believe that the real question here is: "Can anyone BE a data scientist?" And to that I would say no, not at all, for the same reasons I just mentioned. In my experience, not even the best computer science or STEM specialists from a high school can easily become good data scientists, without additional training and some personal factors. Aside from its multidisciplinary nature, data science requires a deep love for the divergence between observed reality in data and the prediction of mathematical models. To do that, it takes more than just mastery of the tools. It takes love of imperfection.

I have been in this field for almost 20 years, before the term "data science" existed, so I have seen a lot of things. In fact, I think that excellence in data science takes several years to apply before you can really understand data, how it behaves, how different models work, backwards and forwards, etc. Most importantly, however, excellence requires making mistakes and understanding mistakes. , in addition to appreciating the variations between the observed and predicted reality. Therefore, I affectionately call data science the science for imperfect people, like me.

I say that half jokingly. In truth, I believe that all good science is for imperfect people, people who become curious, not angry, when they see imperfections and variations. STEM majors that can't stand imperfection and variation will never be good scientists or good data scientists, just as fans cannot be good neighbors. Why? Because the world we live in is imperfect and variable and its beauty lies in that imperfection and variability. Furthermore, ignorance, not knowledge, drives science, and imperfection and variation are the hallmarks of ignorance.

So even though I love math, I don't find it incredibly interesting beyond a certain level for a simple reason - it always works. In this one and only way, I found a soul mate with one of my former teachers: John Nash (the "Beautiful Mind" Nash). I once asked him why he didn't stay in math instead of switching to economics. Nash replied, "Because the math is too easy." Now I can't say that I shared that understanding (I mean, really?), But after exploring a number of mathematical disciplines I have come to the conclusion that it is too "perfect" for me to be imperfect.

Data science, by contrast, takes these perfect models and builds them from actual data generated by humans, animals, and humans who sometimes behave like animals. These creatures rarely exhibit behavior that results in closed-form systems and solutions. In other words, data science gets to the heart of how we operate as humans in the world around us.

We have expectations (that is, mental models) that generally deviate from reality. In pursuing our goals, we display the drama or comedy of that game. So by doing data science we are doing something truly Shakespearean: we are wonderfully characterizing, with numbers, the drama (or comedy) of human behavior!

Okay, now that I've given my Good Will Hunting speech, let me offer a specific example. My typical consulting job involves independently validating business model sets produced by a client's data scientists or consulting team. If done correctly, I employ a toolkit of validation and sampling techniques (small sample, nonparametric, weighted / unweighted, etc.) that I apply in a kind of exploratory and stress test, like a CSI. (I add it to give myself sex appeal). However, due to my experience, I can usually see what has gone wrong with the models even before doing formal tests.

I'm not a genius now, but even when the models are exceptionally complex, I can do this, sometimes even more easily. So I have spotted the problems in highly nonlinear models containing more than 100 variables (which, in itself, is often the problem!) But all of that comes from experience in seeing mistakes, making mistakes, and understanding the reality versus anticipated perfection. If there is a divergence, I get very excited. Also, I have business sense and executive experience, so I understand well that the "correct" answer is often the one that should support some business outcome or goal.

So in all of these data science adventures, I've commonly observed two things: 1) I'm generally right (or no one would hire me again), and, 2) 99% of the people I'm validating (STEM, who they generally have PhDs in physics, mathematics, astrophysics, etc.) I didn't see it. Diplomacy therefore becomes a necessary and added dimension to the data science toolkit, as bad news often has to be revealed.

Also, it is important to understand that most of the time, the developers did not make any serious mistakes. Their models simply don't do what they were expected to do or ignore the business realities they are paid to watch for. That's when they called me again to train them to try to see what I saw so that they can later see for themselves.

And all of that is hard! Sometimes it is like trying to describe the taste of honey to someone who has never tasted it. Of course, that by no means means that it sounds condescending. I'm just getting back to the point that there is an essential field learning aspect for data science that is beyond the importance of learning tools like R and Hadoop.

But that brings me back, in full circle, to the confusion that surrounds one who is capable of "calling" himself a data scientist, even if one does not possess the full set of tools, the experience, and the love for imperfection and variability. As a budding profession, data science has a lot of work to do. We need a more standardized multidisciplinary curriculum, implemented by people with experience in the field and business (not just academics) and maybe a professional body or two who can watch the doors.

Until then, top decision makers will continue to hire any STEM or CS specialist who knows Hadoop and R and is willing to work for little money. That confuses things and probably frustrates them too. Because, in truth, it is much more difficult and complicated than that, and so is data science.

(I hope to establish some kind of "Data Scientist Association". If anyone is interested, please contact me.)

As Jay pointed out, people in the industry today are abusing the term Data Science and Data Scientist. Generally, professionals working in the Data Analysis domain can be classified into two categories:

1. Data Engineer

Data engineers are people who can scale ML / Data Mining algorithms in big data. They are people with experience in Databases (both SQL and noSQL) and Distributed Computing. Required skill set is Hadoop, Spark, SQL. MongoDB and so on.

2. Data scientists

These are people with backgrounds in machine learning, data mining, statistics, computer science, and math. Required skill set is: R, Python, ML, CS.

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As Jay pointed out, people in the industry today are abusing the term Data Science and Data Scientist. Generally, professionals working in the Data Analysis domain can be classified into two categories:

1. Data Engineer

Data engineers are people who can scale ML / Data Mining algorithms in big data. They are people with experience in Databases (both SQL and noSQL) and Distributed Computing. Required skill set is Hadoop, Spark, SQL. MongoDB and so on.

2. Data scientists

These are people with backgrounds in machine learning, data mining, statistics, computer science, and math. Required skill set is: R, Python, ML, CS.

But this is a broad overview of what is the minimum required to enter data science. Data science is more than R and Hadoop, as you mentioned. And it is by no means easy.

You can't be a data scientist just by learning R and Hadoop. You must have a solid background in mathematics, statistics, and computer science. Many times you will be faced with real world problems where standard data analysis packages in R / Python will not perform satisfactorily or will not perform at all.

This is where your math skills and computer skills will emerge. You may have to write the entire model from scratch or hack existing models, you may need to dive into the domain you are working on and become something of a pseudo-expert on it.

And with such rapid growth in this field, to cope you have to read research articles related to data mining and machine learning, which is quite difficult.

So I don't think that in the next decade this field will be saturated, at least not with as much energy and money that all the big companies and major universities are putting into this field.

I hope this answers your question/

Data science is about much more than knowing how to code in R and Hadoop.

It really is more than just knowing how to code. Coding in SQL, R, Hadoop, Python, etc. are all the fundamental skill sets that a data scientist should have, but they are not the entire job. They are a component, not the whole.

I think this is where the misconception is; if coding is all that needs to be done, then yes, "data training grounds" would produce thousands of "data scientists" and the field would become saturated.

"Janitor to data scientist in 6 weeks guaranteed ..." (no)

However, that is the question, data science is much, much more

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Data science is about much more than knowing how to code in R and Hadoop.

It really is more than just knowing how to code. Coding in SQL, R, Hadoop, Python, etc. are all the fundamental skill sets that a data scientist should have, but they are not the entire job. They are a component, not the whole.

I think this is where the misconception is; if coding is all that needs to be done, then yes, "data training grounds" would produce thousands of "data scientists" and the field would become saturated.

"Janitor to data scientist in 6 weeks guaranteed ..." (no)

However, that's the thing, data science is much, much more than knowing how to code in X languages. The hard skills list is a bit misleading;

  • Coding
  • Modeling in Excel
  • Statistics

What lies beyond that simple list is the explanation of how each of those skills fits into the overall role of the job. Let's repeat:

  • Write a custom SQL program that extracts millions of gigabytes of user data from a dozen different sources.
  • Store this data in a custom database and then model in Excel
  • Use statistics, understanding of Company X's business, and deductive reasoning; give Company X action points to improve the user experience of the Y subset of website customers.
  • All this because Company X wants to improve the way its website leads customers through the buying process, making it easier for customers to buy things / more and increasing revenue.

In other words, the "hard skills" list is a bit misleading; It is not just coding, statistics or Excel. You are making recommendations and decisions based on data; millions and possibly billions of data.

Bootcamps can't teach you how to make decisions like that. Sure, maybe you can learn a programming language or two in 6 weeks. Possibly you can also learn everything else at the same time. However, you can't learn how to do all of that and how to make the kinds of business decisions / recommendations that data scientists are responsible for making.


Did I like this? Read about how to get started in the data scientist as a complete newbie.

Google image photo credit.

Foreword (01/20/2016): To put real context, let me quote the ACM: Advancing Computing as a Science & Profession - We see a world where computing helps solve tomorrow's problems, where we use our knowledge and skills to do advance the profession and generate a positive impact. Also, ACM Communications has this article that relates to the topic: Answering Enumeration Queries with the Crowd.

--- Original answer ---

Not to try to pour water on the fire or try to stop the party, after all, there are a lot of people who are making money (thankfully) in this area, but this

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Foreword (01/20/2016): To put real context, let me quote the ACM: Advancing Computing as a Science & Profession - We see a world where computing helps solve tomorrow's problems, where we use our knowledge and skills to do advance the profession and generate a positive impact. Also, ACM Communications has this article that relates to the topic: Answering Enumeration Queries with the Crowd.

--- Original answer ---

Not to try to throw water on the fire or try to put a brake on the party, after all, there are a lot of people who are making money (thankfully) in this area, but this all needs some serious rethinking.

Just yesterday, I saw that Google picked up a package from a Stanford professor emeritus that referred to bringing knowledge representation to the game where before everyone ran after the numbers (machine learning, statistics, etc.), which is the game from "big daddy dating" schemes of all kinds. In this case, the issue had to do with time management. Earlier, I saw where the symbolic people were hanging out with the number crushers. Ah, does that denote progress?

Aside: No set of traps, encapsulations, of activity during the last two decades that could have left digital traces of me, represent more than a minimal aspect of me and my being.

In another context and long before, I have mentioned, several times, that we could teach business and caring minds a little math. At the time, he didn't know exactly how that forecast would play out. Unfortunately, it has exploded far beyond what I would have imagined.

"data-driven" and "evidence-based" are two viewpoints that are creating a mesh that will have more than insidious results. There are underlying issues, unresolved as of yet, that have been covered up due to the ease of covering them with peanut butter through numerical approaches. For starters, that could apply to the bigger picture of cosmology.

--- Later note (01/20/2016) ---

Data people must assume their duties not to belittle the advanced computing aspects of their discipline. As such, boot camps that do data scientists would like some basic Army training (8 weeks or so) that spawns military strategists and more. Regarding the details covered in the article mentioned in the Foreword, the essence of the arguments covers a lot that is important.

Related Questions: Do You Really Need A Data Scientist ?, Is Data Science The End Of Statistics? Data Science: As a statistics PhD student, how can I convince employers that I can be a good data scientist? ... Why do people laugh when I say that R is my favorite programming language?

I suppose a company can give title to any function it wants. For example, several years ago I taught in a high school for project-based learning. The main subject I taught was AP Calculus, however my title was "Learning Facilitator." Each teacher, indifferent to the main subject they taught, was a "Learning Facilitator" from English to History, Greek and French and well, AP Calculus - all "learning facilitators." The same can be said for data science - there are many common names, but the main roles can be quite different in the common name.

I'm not usually one to say something

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I suppose a company can give title to any function it wants. For example, several years ago I taught in a high school for project-based learning. The main subject I taught was AP Calculus, however my title was "Learning Facilitator." Each teacher, indifferent to the main subject they taught, was a "Learning Facilitator" from English to History, Greek and French and well, AP Calculus - all "learning facilitators." The same can be said for data science - there are many common names, but the main roles can be quite different in the common name.

In general, I am not one to say that someone is a fake data scientist, as for me the main "duty" of a data scientist is to add value to the data. And they can do it in a number of ways. Every time I interview a junior data scientist, I look at their resume and look at the tools they have there and then I give them a hypothetical data set and ask them how they would get it back with the tools that they have on their resume. I wonder what the first steps would be, those are the first lines of code that they would write to that dataset. What are your favorite modules / libraries? How would you probe the nature of the data set? Then I give them another dataset, one that could tax the size limits (for example, if they had Excel or R) and see how they would handle that. Do you understand sampling? Then you could go back to your previous data exploration and see how they would do feature selection, etc. Do you understand how to choose models for this data set? Many people I interview are struggling and do not have 3, 4, 5+ years of data work.

So is data science too easy? No I dont think so. I think it's too easy to include tools on your resume. And it's too easy for bootcamps to take your money and you think you're ready for most data science roles. You may be ready for a few, but you probably don't have the skills and acumen (not to be confused with tools) to handle a good majority of data science roles.

With that said, I know some very bright people who do "Data Science" primarily in SQL and Excel. They have SQL on a 512GB 64-core server with a 10TB hard drive. They can extract well-indexed 50B data lines in less than 45 seconds. Hummm diggity! And they write a lot of algorithms in SQL that are listened to in SPSS or Excel for modeling.

The question may be: is there a 'technical' level throughout all iterations of data science that one must have to claim to be a data scientist?

Take the previous case, Mr. SQL-Excel-SPSS, and the next two cases. A second person writes a MapReduce script and waits 4 hours to get it out of Hadoop and then runs a regression against that dataset in R. Meanwhile, a third person used Spark and a batch server layer architecture in Hadoop to get the same amount of data as the second type in 5 minutes. Then write a custom algorithm in Python.

Are any of the three mentioned less data scientist than the others? Who brought the most value and knowledge to your company?

So there are many different angles to looking at data science, from role nomenclature to responsibilities.

But as for the market getting too saturated? Well, if you can trust a top-tier consulting firm like McKinsey, here's what they had to say about the data field:

There will be a shortage of talent needed for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the knowledge to use big data analytics to make effective decisions.

src: Big data: the next frontier for innovation, competition and productivity

That's a great generalization. For one thing, data science isn't about R and Hadoop, those are just tools like hundreds of others. Data science is about making data usable for people and systems in products that abstract the idiosyncrasies of the data that powers it.

On the other hand, I agree that data scientist may be an overused title. That's one of the reasons we don't have data scientist positions at Miniclip yet. We have data engineers, data analysts, and potential clients. I can argue that most of us fit the current description, but honestly, it's not just our engineers and analysts that are better defined.

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That's a great generalization. For one thing, data science isn't about R and Hadoop, those are just tools like hundreds of others. Data science is about making data usable for people and systems in products that abstract the idiosyncrasies of the data that powers it.

On the other hand, I agree that data scientist may be an overused title. That's one of the reasons we don't have data scientist positions at Miniclip yet. We have data engineers, data analysts, and potential clients. I can argue that most of us fit the current description, but honestly, not only are our engineers and analysts better defined that way, we also don't care about titles and rankings. However, here is a definite description of the data scientist job. The day someone has that skill, experience, and produces those results, I will gladly promote it.

We don't care about titles, we do our job and move on. To be honest, you should too.

As for data science being easy, you are focusing too much on the technical side of things. Doing it for a year is insufficient for me to understand business needs and the impact of their work and decisions in any field, including data science. I'm not saying he's not a data scientist, but I wonder how deep his domain knowledge is and how challenged he is. The most difficult aspect of my team's work is not technical, everyone gains new skills every week. The challenge is almost always business related.

In general, I agree with you that people are choosing the title of data scientist too easily, but they are free to do it in the same way that we are free not to or even to worry about it. Data science is teamwork. A team sport in the words of DJ Patil. If the definition of data scientist is the unicorn data scientist, then all the people who acquire a couple of skills and label them data scientists are just a simplified version of the media hype, then ... why do you worry about it? It is the result that counts, not the job title.

The naivete in your assumption that data science is easy is staggering. Sure you can fit a good dataset into a sklearn model and get half-decent results, but that's just the tip of the iceberg. To understand models and the math behind them, you need to learn higher-level linear algebra, calculus, probability, and statistics. On top of that, you need to be able to produce efficient code that requires somewhat sophisticated computer programming skills. On top of that, you need to be able to develop a pragmatic approach to data science, which may involve designing your

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The naivete in your assumption that data science is easy is staggering. Sure you can fit a good dataset into a sklearn model and get half-decent results, but that's just the tip of the iceberg. To understand models and the math behind them, you need to learn higher-level linear algebra, calculus, probability, and statistics. On top of that, you need to be able to produce efficient code that requires somewhat sophisticated computer programming skills. On top of that, you need to be able to develop a pragmatic approach to data science, which may involve designing your own algorithms, cleaning up the dataset, and much more. In addition to this, you must understand the domain in which you work and be an effective communicator. If this sounds easy to you, So maybe you are a genius and need to choose a field that you consider more difficult than this, but for the vast majority of people these skills will take years to learn and only people with the perseverance and passion for mastery will succeed. There may be people who think they can take some courses online and think they are ready for a ds position, but they will be very wrong when it comes to placing themselves in the job market.

There are really two parts to this question:

  1. Machine learning and algorithms
  2. All the rest

As for n. 1, it's really VERY VERY easy to make machine learning, statistics, and algorithms (in general) wrong, and not even know it. This will keep you awake at night. So yes, it is very easy to just run an algorithm. But it is very difficult to know which algorithms to use and to use those algorithms correctly.

As for n. 2, I'd say this is most of the work a true data scientist does (or should be doing). And this is a LOT of work. Data science well done is exhausting because there is a lot of coding, a lo

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There are really two parts to this question:

  1. Machine learning and algorithms
  2. All the rest

As for n. 1, it's really VERY VERY easy to make machine learning, statistics, and algorithms (in general) wrong, and not even know it. This will keep you awake at night. So yes, it is very easy to just run an algorithm. But it is very difficult to know which algorithms to use and to use those algorithms correctly.

As for n. 2, I'd say this is most of the work a true data scientist does (or should be doing). And this is a LOT of work. Data science well done is exhausting because there is a lot of coding, a lot of research, a lot of thinking, a lot of interaction with the business, a lot of presentation, a lot of analysis, etc.

Yes, there are data scientist roles that are light-weight, or where no one really cares what you're doing, as long as you make it look cool or pretty. But those kinds of roles are exceptionally poor preparation for heavy-duty roles where a lot is expected of you.

I recommend preparing and seeking tough positions as a data scientist. You will work hard, but you will be a much better data scientist thanks to your efforts. You will be a data scientist that you will be proud of.

Let me start with this very interesting article from kdnuggets: 20 Questions to Spot Fake Data Scientists

If something is fashionable, it seems trivial; However, if something is trivial, then what is the use of doing it because everyone can do it?

We know what a scientist is "Oxford dictionary definition of scientist", but sadly no one clearly knows what a data scientist is or what data science is. The wide variety of opinions on this topic provides good evidence for how people understand data science in general.

  • How can I become a data scientist? (
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Let me start with this very interesting article from kdnuggets: 20 Questions to Spot Fake Data Scientists

If something is fashionable, it seems trivial; However, if something is trivial, then what is the use of doing it because everyone can do it?

We know what a scientist is "Oxford dictionary definition of scientist", but sadly no one clearly knows what a data scientist is or what data science is. The wide variety of opinions on this topic provides good evidence for how people understand data science in general.

  • How can I become a data scientist? (More than 100 responses)
  • What is a data scientist? (52 answers)
  • What is data science? (36 answers)
  • How can I start learning data science? (21 answers)
  • What experience is required for data science?
  • What do you need to know to learn data science?

I can call myself whatever I want, but it has no real meaning if the outside world doesn't recognize it or if it doesn't give me a sustainable career path or something tangible that I can gain from this knowledge. This can lead me to a 'lockdown' state, where I think I know but don't know what to do with it. I recently wrote a blog on these aspects, see below.

Machine learning 'block': I have a hammer but no nails

To answer your question directly, if you have a background in math, linear algebra, programming, visualization, statistics, etc., then it may be easier for you to enter and stay in this field, otherwise a lot of learning is required. For example, how do I learn machine learning?

One thing's for sure, learning few programming languages, mastering a few packages, doing cloud / cluster computing stuff is really cool, but it doesn't necessarily entitle anyone to become a data scientist, unless company X calls to that data scientist person.

There is a great demand for good data scientists. It's not about whether you know R and Hadoop. It's about extracting knowledge and separating the important things from the unimportant.

People are hired as software engineers with 100,000 salaries in SV every day. Despite the large number of people who know Java, those guys can still get a job, and if they don't like it, they can get another. The same applies to data science, if your analytics / algorithms don't add value then you're out.

In bootcamp they teach you how to report data sets and run regressions in R. Do they teach you how to determine

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There is a great demand for good data scientists. It's not about whether you know R and Hadoop. It's about extracting knowledge and separating the important things from the unimportant.

People are hired as software engineers with 100,000 salaries in SV every day. Despite the large number of people who know Java, those guys can still get a job, and if they don't like it, they can get another. The same applies to data science, if your analytics / algorithms don't add value then you're out.

At the bootcamp, they teach you how to report data sets and run regressions in R. Do they teach you how to determine if the metric the other team built the day before is good for measuring product success? You can load a large number of records into MySQL - did they teach you how to reduce the execution time of a critical complex query from 1 minute to 0.01 seconds? There are countless little things you can't get at bootcamps, and those little things are what give you real value as a stuntman.

Data science is a fusion of applied statistics, software engineering, and often domain knowledge, whatever that domain may be. It is not easy and it will not be in the future. Anyone who says they can be a data scientist after their "2 week soopa hurrdcore R crash course" is a scam.

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