Future of Data Scientists: Career Outlook

Data Science is the fastest emerging field in the world. It analyzes data extraction, preparation, visualization, and maintenance. Data scientists use machine learning and algorithms to bring forth probable future occurrences. Organizations analyze themselves to grow. Data Science in the future will be the largest field of study.

Future of Data Scientists: Career Outlook







What is Data Science?

Data Science is the field of study where a large volume of data is studied using modern tools and AI to ascertain their patterns.

Data Science is the study of data such that a pattern emerges from it. This pattern helps in making better business decisions. It’s not something new, but the application of Data Science has been tremendous in this age of the internet. Data Science combines business and mathematics by employing a complex algorithm to the knowledge of the business. As a result, you can have a prediction model for your business with just a dash of statistics.

Not only in business, but data analysis is also paramount in various fields like predicting disease outbreaks, weather forecasting, recommendations in healthcare, fraud detection, etc.

Before a data scientist can draw any conclusion, it goes through five stages, also called the lifecycle:

  1. Acquisition- This is the stage in which the data is collected. Here the data gathered is unstructured and raw.
  2. Exploration- This is the most time-consuming task of the life process. Here the data is cleaned and identified as useful or useless. Then, the data scientist brings it in the form where it is ready for the next step.
  3. Modeling- This is the part of the process in which a data scientist looks at the data and determines which model will best suit the required analysis.
  4. Analysis- This is the crux of the entire process. Various analyses are performed on the data to obtain the desired results.
  5. Reporting- The obtained results are shown in a readable format, be it a chart, diagram, or simply a report. Here data is presented in an understandable form.

Is Data Science a Beginning or an End?

So now that we understand data science, the question arises how viable is it in the future?

The answer is- very! Bill Gates once said, “Content is King.” But Data is the queen. Consider this… twenty-five years ago, when the internet was still a thing of the future, the local grocers were still using Data Science to analyze which products were selling more and which were selling less. Then, based on this data, they would order the next batch of groceries. This was data analysis, albeit at the crudest level, but it was still data analysis.

So, with the advent of the internet, this analysis is becoming increasingly sophisticated with the use of artificial intelligence, or AI and machine learning. Moreover, as the economy evolves, learning consumer behavior will be the chief tool for marketing. We are at the very cusp of the data collection explosion in such a case. There is currently a shortage of Data Science engineers.

The world is data-driven, and the need for qualified data scientists will only increase in the future. People now realize the importance of their data privacy on the so-called “free apps”. On the other hand, giant companies like Facebook, Amazon, Flipkart, etc., are collecting data at an alarming rate.

Data Science’s Contribution to the Future

As the data on the internet grows exponentially, the contribution to Data Science will also increase in the same way, and so will the Data Science job future. Whether fraud detection in a bank or finding a country’s happiness index, Data Science will be around for a long time. The industries that are sure to benefit the most are:

  1. Image Recognition – As more and more data are accumulated by a company, its clarity increases. For example, think about an automated vehicle, a Tesla, a self-driving car. How do you think it detects the road? When many people drive on the same route over and over, the image of this road becomes more precise. This better image will make the drive for the next person on the same route more comfortable.
  2. Health care advancements- With an increased patient database, the health care system will recognize any deficiency quickly, which can help the government immediately mitigate the oncoming health crises.
  3. Weather forecasting- With enough previous years’ data and powerful analysis tools, predicting oncoming storms could be possible soon, saving hundreds of lives and minimizing property loss.
  4. Fraud Detection- If algorithms and AI tools are in place, fraudulent transactions are rectified instantly. Such activities can also be shut down if that is the problem taken into consideration by an AI.
  5. Gaming- Video games have become at par with sports nowadays. The user experience is personalized when more and more data is collected. The habits, likes, and dislikes of a person can be taken care of when this data is collected.
  6. Logistics- AI systems have already become advanced, like Google Maps telling us which route to take or avoid due to traffic. This system can become more potent, and different problems like road accidents can be taken care of too.
  7. Recommendation systems- Entertainment industry has already benefited from all the data collection they have done with apps and websites like Netflix, Amazon Prime, Disney, or any other OTT platform. Your watch history is a rich data bank for these companies. So the more you watch on a platform, the more refined your suggestions would get.

The future looks like a place where data will rule our every decision.

The Road to Becoming a Data Scientist

When a field is as popular and emerging as Data Science, there is bound to be a lot of competition and opportunities. Therefore, there is a need for a data scientist in every industry. Self-analysis is vital if any business needs to grow and stand out. A data scientist does this analysis. So, the job of a data scientist is very high in demand and will remain as such in the near future.

A data scientist uses various tools and recognizes the pattern in data. So, the most logical next question is – How do you become a data scientist?

First, let’s talk about the skill set required to become a good data scientist. A data scientist works with quantum computing. Therefore, the most important thing to know is programming languages like Java, Python, R, SAS, SQL, etc. Additionally, a data scientist understands Big Data frameworks like Pig, Spark, and Hadoop. Finally, deep learning and Machine learning can help take your career forward.

A data scientist can take these skills forward and become a specialist in an industry with hands-on experience to become highly sought after. A certification in a Data Science course is highly recommended to develop the portfolio. Additionally, hands-on experience is a must to build up that resume.

Degree and Qualification for a Data Scientist

As we have seen that a data scientist uses an amalgamation of several subjects, higher qualification is generally preferred for a data scientist. An advanced degree in Mathematics or Statistics is seen as a plus point in problem-solving. As many programming languages are required, a degree in computer science is also appreciated.

However, the most important of all in any job is knowledge. And knowledge of the technical aspect of programming and business acumen is fundamental. The skills that a data scientist should focus on are

1. SAS

SAS stands for Statistical Analytics Software. This is used for the management of information, analysis, and reporting.


This software is used for analyzing, cleaning, and analyzing complex data.

3. R programming

This programming language is used for statistical computing and graphic support.

4. SQL

This is a programming language that is used for managing data.

5. Hadoop

This is a java-based language used to process extensive data. It has growing popularity; however, it is not required to become a data scientist.

These technical skills are necessary for data scientists to excel in their field. However, if a data scientist wants to leave their mark, they should work on the following non-technical skills.

6. Business Acumen

Understanding the business is very important for data scientists if they are willing to take the organization to the next level. Mitigating an organization’s problems should be a data scientist’s primary goal.

7. Communication Skill

Soft skills are a relevant requirement in every job. A data scientist should be able to communicate effectively. The data findings must be aptly communicated to make better business decisions.

8. Statistics

Statistics is one of the essential parts of data science. The analyzed data is represented in one of two forms, inferential or descriptive.

9. Mathematics

Topics like probability and linear algebra play a vital role in the study and practice of data science.

10. Analytical Reasoning

Finding a solution to complex problems is an everyday task for a data scientist. Training your brain to think logically is a skill a data scientist can acquire.

Various courses online are also a great way to upskill. For example, KnowledgeHut Data Science Bootcamp is one of the most reputed online courses to improve. Whether at a beginner, intermediate, or even an expert level, upgrading yourself is always a good idea because Data Science is the future.

Data Science Careers

Data has applications in almost every field. The Data Science future is studded with career opportunities. Future of Data Science 2030 is estimated to bring opportunities in various areas of banking, finance, insurance, entertainment, telecommunication, automobile, etc. A data scientist will help grow an organization by assisting them in making better decisions.

There are three types of Data Science careers:

  1. Data Analyst – A data analyst collects data from a database. They also summaries result after data processing.
  2. Data Scientists – They manage, mine, and clean the data. They are also responsible for building models to interpret big data and analyze the results.
  3. Data Engineer- This person mines data to get insights from it. He is also responsible for maintaining data design and architecture. He also develops large warehouses with the help of extra transform load.

These roles are closely related and sometimes overlapping. For example, a data scientist can perform the role of a Data Scientist or a Data Engineer.

AI and Machine Learning are Leading the Way

This all seems like a rosy picture with the ever-growing opportunities in this field. However, the reality is that every industry is bound to be automated. There is already software that can efficiently perform the analysis.

Artificial intelligence and Machine learning are bound to take the place of human beings in this field too. So, the Data Science demand in the future be fulfilled by AI? The answer is yes and no. The data scientist will become increasingly qualified as a quantum theorist to take advantage of this highly evolving technology.

The future of Data Science jobs will look like the middleman who can communicate with computers and humans.

AI and Machine learning are just tools that a data scientist uses to deal with big data. Data Science and Machine learning go hand in hand.


The most hush-hush question about any job is the salary. When you become a data scientist and excel in your field, you can earn upwards of 100k USD.

If you are choosing a career option or are considering a change in your career, becoming a data scientist is a viable option; if you are passionate about computer languages and statistics, Data Science is the way.

Just remember always to update your knowledge and keep up with the current trends

Source: Knowledghut.com

By Kelli Delfosse
Kelli Delfosse