What will be the future of data scientist jobs in 10 years?

Updated on : December 4, 2021 by Gabriel Rogers



What will be the future of data scientist jobs in 10 years?

The simple answer will be "there will be more data to analyze", so be glad that this is the only field where experience and data grows.

Get ready to solve all the problems in this world with the data set you have.

Think about how to solve the "Problem of hunger in the world" when there will be no trees, no food, polluted air and the list grows ... the only thing that will be left with data.

Without a doubt, data science is becoming one of the most in-demand skills in the rapidly changing industry. Across major industries, recruiters are looking for data-driven professionals who can analyze it to generate useful information for the company. The growing demand appears to be endless, and the world is projected to face a shortage of at least half a million data scientists by 2025. Therefore, learning data science could be your chance for a promising career.

Now there are many ways to learn data science - You can enroll in a university, opt for a Boo

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Without a doubt, data science is becoming one of the most in-demand skills in the rapidly changing industry. Across major industries, recruiters are looking for data-driven professionals who can analyze it to generate useful information for the company. The growing demand appears to be endless, and the world is projected to face a shortage of at least half a million data scientists by 2025. Therefore, learning data science could be your chance for a promising career.

Now there are many ways to learn data science - you can enroll in a university, opt for a Bootcamp program, or even try your hands on short-term online courses offered by independent organizations and platforms. However, before we move on to the part where you start comparing courses and answer a call, let us understand what all the skills you would need to learn are.

Understand what skills to acquire

Becoming a data science professional is easier said than done. There are many skills that you would need to add to your bag to qualify as a data scientist. But for starters, there are three absolutely necessary technical skills that you must learn:

  1. A programming language like Python or R to use the data sets (I recommend Python). If this is your first experience with a programming language, I recommend starting with Python.
  2. For data handling and manipulation, SQL
  3. A little machine learning and the fundamentals of statistics. To be good and understand how the data ecosystem works, I would recommend brushing up on the fundamentals of statistics and learning a little machine learning (not necessary if you are enrolling in a college course or full Bootcamp).

Choosing the right course

Looking for the right course will confuse you even more, especially if you are a beginner and have no idea what is right for you. As I mentioned earlier, you may find hundreds of platforms offering Bootcamp and short-term courses. My advice: enroll in a course offered by a university. Learning in a classroom setting and working with peers is sure to generate the inner data buff in you.

However, if you are not comfortable with full-time programs, you can surely opt for part-time online courses offered by universities, which offer similar professional results.

Since I have seen several students become successful data professionals with these specialized programs, I highly recommend LEARNXT University accredited degree and diploma programs in the data science domain. Currently, the platform offers three courses:

  1. MBA in data science
  2. Master of Applied Data Science
  3. PG Diploma in Applied Data Science

One of the biggest benefits of choosing LEARNXT is its hybrid learning offering - you can choose the weekend part-time learning program if you are not comfortable with the full-time program. Apart from that, here are some other advantages:

  • You are taught by the best professors from IIT and world renowned universities
  • More than 50% of the time is spent on projects and assignments.
  • Lifetime access to learning materials on the artificial intelligence-based StudyNxt platform
  • Access to CareerNxt Career Portal services

Develop essential (non-technical) skills

Gaining technical skills is crucial to becoming a data scientist in the future. However, you must give equal importance to honing your critical thinking, problem-solving skills as attention to detail. When playing with data sets where even a single decimal place could have a big impact, you need to be careful, quick, and rational in your approach. I'm not asking you to flip a switch as these skills take time, but start working on them while adding technical skills to your bag.

Practice well and participate

Last but not least, PRACTICE. Practice as much as you can because data science is not a skill you can take from books; it can only be acquired and improved over time when you practice. The course you choose is sure to expose you to real-world data challenges, but from time to time, you can go to platforms like Kaggle and check out their forums to work on the latest issues and hone your data handling skills.

All the best for your learning journey and your future as a data scientist!

I'm a junior data scientist, so don't believe it when you hear "there is no such thing."

However, they are rare and really depend not only on what skills you have, but also what the company wants and how they fit.

So I don't know what you need, but this is what I needed:

  • A master's degree. Mine was in Operations Research, and I'm currently on another MS just for data science. My employer required this because we deal with clients so this may not always be the case, but it is quite difficult to enter the industry with just a bachelor's degree unless you have done something significant in the field.
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I'm a junior data scientist, so don't believe it when you hear "there is no such thing."

However, they are rare and really depend not only on what skills you have, but also what the company wants and how they fit.

So I don't know what you need, but this is what I needed:

  • A master's degree. Mine was in Operations Research, and I'm currently on another MS just for data science. My employer required this because we deal with clients, so that may not always be the case, but it is quite difficult to enter the industry with just a bachelor's degree, unless you've done something important in the field.
  • Project experience. I have been to a few Kaggle competitions and have learned a lot. I got the top 5% in active competition and made it clear on my resume. I joined a meeting, found a group of data scientists who wanted a side project and we have been working together ever since. Projects are always great talking points in interviews and if you've faced a problem that they can relate to, it's a huge bonus.
  • A portfolio. Links to all your projects, images, etc. Everything tangible they can see. Make sure it's polished.
  • Programming and data skills. Half of my interviews included relatively simple programming questions (merge ordered lists, encode knn, etc.). Some were data structure oriented, but most were algorithm-based. Personally, I didn't see anything too fancy, but the better you are, the more valuable you'll be. I also always had some questions about data manipulation. For example, grouping, calculating some statistics and showing the maximum of 2 for each group was one.
  • Professional experience. He already knew how to talk to clients, explain complex concepts, etc. This was important to my employer, not necessarily all employers.
  • Solid fundamentals. At the end of the day, you must pass the interview. The questions asked here will vary in complexity and topics covered. Some are basic statistics questions (design of experiments), some probability (Bayes, total probability theorem), and some machine learning. For ML, you are likely to be asked about an algorithm that you have on your resume (or derived from a case study question), so if it's on your resume, you have to know it by heart!
  • Finally, a well-written resume. I've seen amazing people with bland resumes. It's not hard to bring it to life and make your work sound as cool / awesome / relevant as it is. Take advantage of their experience, be detailed when you can, include a summary to sum up your rudeness, use action verbs, etc.

Good luck!

Five to ten years is not that long. Five years ago I was doing deep learning (in C # as there were no libraries yet). Ten years ago I was mainly using SVM. However, the kinds of problems he was solving were very similar.

One big difference is that analysis conferences ten years ago were small and very technical. Five years ago they were still quite small, but there was a lot of patronage and interested "parasites". Now they are crazy and I generally don't go. What happened is that the industry has become mainstream.

A side effect of going mainstream is that our ability to interact with the resolution

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Five to ten years is not that long. Five years ago I was doing deep learning (in C # as there were no libraries yet). Ten years ago I was mainly using SVM. However, the kinds of problems he was solving were very similar.

One big difference is that analysis conferences ten years ago were small and very technical. Five years ago they were still quite small, but there was a lot of patronage and interested "parasites". Now they are crazy and I generally don't go. What happened is that the industry has become mainstream.

A side effect of the mainstream is that our ability to interact with the rest of the business has become much easier. Ten years ago it was incredibly necessary to sell data science. Customers were skeptical of the whole concept. Even five years ago, clients were dabbling in, but there was little basic commitment. You see it everywhere now, although I think it's largely the companies that claim they do it because it's so much cheaper than actually doing it.

This brings me to the future. Over the next five years, I expect to see a lot of companies claiming to be involved and trying to use it on serious projects. I hope that a good portion of those projects fail and that the entire industry has generally matured with a greater understanding of what works and what doesn't.

Check out the number of GUI tools that support machine learning now. Things like Excel add-ins that automatically group data. Give it five years and I hope most people think only of them when they think of data science.

In ten years I think fashion will have really advanced. Data science will be a common and long-awaited skill in other disciplines and specialized data scientists will be viewed a bit strangely. You will also have a situation where it is common and normal for the data captured by the systems to be fit for data science, as opposed to what is happening now where most of the data is structured in a way that requires manipulation. significant.

I've written about this so often that people have complained.

Here's part of a crappy SAS definition of what a data scientist is:

“They are part mathematicians, part computer scientists and part trend watchers. And, because they encompass both the business and IT worlds "

The truth is ... the real world is about machine learning and machine learning is about programming.

The real world doesn't need more data scientists, we need more data professionals with programming experience.

So the future for most types of mathematics, PhDs, is bleak. Businesses are finding out the hard way th

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I've written about this so often that people have complained.

Here's part of a crappy SAS definition of what a data scientist is:

“They are part mathematicians, part computer scientists and part trend watchers. And, because they encompass both the business and IT worlds "

The truth is ... the real world is about machine learning and machine learning is about programming.

The real world doesn't need more data scientists, we need more data professionals with programming experience.

So the future for most types of mathematics, PhDs, is bleak. Companies are finding out the hard way that overly educated guys are technically inept. Companies that pay 200,000 people expect results. When a "data scientist" tells his boss that he can't get his own data on most real-world jobs, the flags go up. Are we paying you 200K to work through the whole machine learning process and you can't do the first step?

Businesses have caught up and are removing these guys from the corporate world. Also, I started recommending that companies select all candidates without 3-5 years of SQL experience. This simple screen will eliminate the types of education.

That is why the role of data science has fallen 3 years in a row and will soon be 4 years.

If you are hiring in this space, I recommend that you read this post. It will save you a lot of money.

How to become a machine learning engineer in the applied space.

In conclusion, the role of data science is dead. The elite in this space in the future will be programmers with great data skills.

Since a long time, there have been many rumors in the media about data science, big data, machine learning, deep learning, etc., a captivating decision based on data is not only intrinsic intelligence, but also well-constructed profitable sense. While every corporation is trying to transform itself into a data-driven corporation, many are feeling stressed to apply it due to a lack of consideration and lack of trained professionals.

The discipline of analyzing information has been around for a long time. Corporations have made sense of data from business analysts, data analysts, statisticians, business consultants,

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Since a long time, there have been many rumors in the media about data science, big data, machine learning, deep learning, etc., a captivating decision based on data is not only intrinsic intelligence, but also well-constructed profitable sense. While every corporation is trying to transform itself into a data-driven corporation, many are feeling stressed to apply it due to a lack of consideration and lack of trained professionals.

The discipline of analyzing information has been around for a long time. Corporations have been understanding data from business analysts, data analysts, statisticians, business consultants, technologists, and business experts, together to solve evils and provide solutions.

Conventional business analysts were taught to create a belief based on agreed little data. However, today there is a need to analyze a lot of data, flow data, and shapeless data, and the new generation of data analysts, data scientists, are better equipped to do so. It is safer to say that data scientists are a much more evolved form of data analyst and business analyst. They deal with the study of Big Data and create data performance, computerized data analysis tools, dynamic visualization, etc.

There is a worldwide stipulation for data scientists who have domain knowledge and applied skills in various areas such as math, statistics, machine learning, deep learning, natural language processing, Python, SQL, SAS, Big Data tools such as Hadoop and Spark , visualization tools like Cogons, Tableau, click view, etc. While the demand for data scientists is growing at a rapid rate, Indian academia and industry are not equipped to meet the need. The application of AI, ML, deep learning, data science is growing in India and around the world in almost all industries such as telecommunications, IT, insurance, manufacturing, healthcare, banking, retail, media, consulting, e-commerce, oil, gas. , automobile, airline, government, NGO and startups and all functional areas such as marketing, finance, operation, human resources, etc.

There is a growing demand for solutions based on data science, machine learning and artificial intelligence, but there are not enough trained people available to execute these projects with skill and success. The fastest growing roles globally are data scientists and advanced analysts, whose demand is expected to increase by 28 percent by 2020. Compared to the market average, corporations need five more days to find scientists and data analysts. Employers are willing to pay higher salaries to professionals with experience in these areas.

Salaries for chief data scientists with more than 10 years of experience in mathematics and statistics are a minimum of one crore per year. Unicorn Data Scientist is the upgraded version of our daring data scientists, but they are a bit difficult to hunt down and are compensated at over $ 200,000 a year.

As India has the largest pool of employees with knowledge of math and coding, we are able to reach the world's largest center of qualified Data Scientists. With an updated curriculum and a trained faculty who have real-time industry experience, Indian students can prepare to become leaders in the data science industry.

To get to the answer to the question, we must first define: What is a data science project?

Data Science Project
To get a measurable boost from any data science activity, you need to follow a process. Here's a view of the process (evolved based on Jeremy Howard's article): Designing Great Data Products


An example of this process for a project that involves Improving Customer Retention could be the following.


Now the skills required in the various stages of the project are different. Here's a look at the skills throughout each stage.


Today, data science is primarily defined as the box that talks ab

Keep reading

To get to the answer to the question, we must first define: What is a data science project?

Data Science Project
To get a measurable boost from any data science activity, you need to follow a process. Here's a view of the process (evolved based on Jeremy Howard's article): Designing Great Data Products


An example of this process for a project that involves Improving Customer Retention could be the following.


Now the skills required in the various stages of the project are different. Here's a look at the skills throughout each stage.


Today, data science is primarily defined as the box that talks about modeling.

Fast forward to 10 years
My view is that the task of creating models is becoming increasingly automated and black box.

With new software like Torch 7, H20, MLLib, Wutpal Wabbit, etc., the people writing the modeling software will need high math skills. For people solving business problems like "Cross-selling" or "Propensity to buy models", the greatest need will be to frame the problem statement and orchestrate the correct data for the problem so that it can be fed into this modeling software.

Note that Kaggle focuses more or less on the modeling aspect only in the life cycle of a data science project. And, more and more contests are being won by people who master software such as Wowpal Wabbit, H2o, etc.

I would say harder. Data science is currently one of the most popular career paths. As a result, more and more people are entering the field. A few months ago I took an edX data science online course, the number of people enrolled was in the thousands. As time goes on, more and more people will enter the field, so the field will become very competitive in 5 years. The good news is that there are a lot of data science jobs out there.

Hello, Starting salary is above LPA 6 in most companies, but it depends on work experience and depth of knowledge. The average salary would be around 10 LPAs, but few product-based companies offer between 20 and 50 LPAs for those who have a master's or Ph.D. with 3 to 5 years of work, ex.

Speaking of the future, everything in the future will be solved through analytics and each company will use data science techniques to solve all their problems. So, I think the future is pretty bright. All the best!! Keep learning!

Yes, you can call yourself a data scientist.

In fact, you can call yourself president of the nation, no one can stop you, and being positive in life can help you achieve great success.

For now, both.

Perhaps a few decades later it would be data scientists, as big data will become the norm. There will be a great demand for people who can handle, analyze, and obtain insights from big data, but the demand for big data developers will eventually decline.

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