Data Science As A Career


If you are here, it means you want to explore data science career options. Data surrounds everyone today. Be it professional data or personal data, raw data or organized data. For example, a couple needs to remember their first date, HR needs to find out how many hours an employee is working, fitness freaks need to know how many calories they burned today, F1 racer needs to know the car tire pressure in real-time and the list goes on…

Data science is a field that combines data-related analytical techniques and scientific theory to generate insights for business stakeholders. Its shape, elements and dimensions allow organizations to optimize their operations. Also, helps identify new business opportunities and reduces the functional performance of departments.

The Word “Data Science” according to Google Trends, is the most trending word from the last five years. Moreover, we can see from the graphs below that the interest and searches for this term are only increasing with time.


Simply put, data science creates a competitive advantage over competitors. By optimizing operations with unmatched data throughput. Also, according to the Forbes report, data-driven companies are 23 times more likely to win over customers. Furthermore, 19 times more likely to be profitable. So, let’s read more about data science methods, methodologies and the future of data science.

What is the global market value of the data science industry?

You can view several reports to measure the global value of the data science industry. Grand View Research estimated the global market for data science platforms to be US$ 3.93 billion in 2019. Other Statista data expects the global big data market to be worth US$ 64 billion by 2021. Data analysis is another key area of ​​ data science. Research shows that North America had the largest share of the market in 2019. It had a market value of just over $ 10 billion. Europe was the second-largest market in 2019 and was worth about $ 6.43 billion.

What does the data science labour market look like?

The role of a Data Scientist is one of the most demanding jobs. In both the United Kingdom and the United States. LinkedIn Emerging Jobs Report 2020 featured this role in both countries. Also, it ranked 3rd in the United States and 7th in the United Kingdom. In the United States, Lille’s annual growth rate is 37%.

The report also emphasizes that “as some industries, such as insurance, prepare for the future, data scientists can extend the responsibilities traditionally held by statisticians.” In the UK, it was down from 3,162 in the same period last year. The United States has also witnessed a similar trend. Where data scientist job listings stagnated between 2019 and 2020.

COVID-19 pandemic explains this decrease. Other data for 2020 show a shortage of about 250,000 professionals with security and data science skills. Thereby, clearly showing the need for professionals with the right skills.

Data Science and Analytics in Business

Data science for business decision-making is already a reality. It is the basis of the entrepreneurial spirit of the information age. This application goes beyond pure extrapolation of knowledge. Carefully selected results will help to maximize the effect. A common example is to reuse data to graphically represent a buyer’s persona. Marketing campaigns and brand building are some of its main uses.

Experts with data science skills know how to find meaningful information from the data they encounter. It leads the company in the correct direction. The company needs strong data-driven decision-making, for which he is an expert. Data scientists are experts in various fields of statistics and computer science.  

They use their analytical skills to solve business problems. Data science professionals bring various benefits to the business with their enhanced data science skills. For example, improved ROI, improved sales, streamlined operations, faster product lead times, customer retention and satisfaction. It is an improvement in degree.

Why Build a Career in Data Science or Analytics?

Over the last decade, the demand for data availability, data science skills and data-driven decision-making have skyrocketed. Also because of the dramatic changes in business operations and consumer behaviour caused by the COVID-19 pandemic. Analytics and data science have established themselves as indispensable navigation tools across industries and functions.

Earn A High Salary

According to data from the big-paying Robert Half, the median starting salary for data scientists is $ 95,000. Therefore, almost double the average salary in the United States. At around $ 70,000, even the median salary of data analysts is a more entry-level position. Significantly higher than the median salary in the United States.


According to the Burtch Works survey, work experience is the number one factor in data science payroll. Mid-career data science professionals with at least seven years of experience can expect an average revenue of $ 129,000. Experienced data scientists with leadership can earn over $ 250,000. But education, enterprise size and industry are also key factors in determining data science salaries.

Solve Complex Problems

If you enjoy solving complex real-world problems, you’ll never get bored as a data scientist. The main responsibility of your job is to find answers and insights. By analyzing and processing large amounts of raw data. Here are some examples of business problems you’ll have to solve:

  • Find ways to increase sales
  • Discover features that differentiate your target audience segments.
  • Look for potential opportunities in different data sets.
  • Identify undetected problems in the normal course of business.
  • Build an infrastructure that helps an organization ingest and centralize all of its data.

Philip Rigoletto, associate professor of mathematics and statistical data science at the Massachusetts Institute of Technology, said: “It also applies to data science. Rest assured that regardless of your area of interest, you have the data to enhance it. Being able to extract information from the data from marketing to health. We are in a very strong position to collect data in all aspects of society, from sports to entertainment.”

Avoid Job Automation

The role of data science, especially data analysts, has a very low risk of automation for several reasons.

  1. The demand for data science roles is increasing at an average rate of 50%.
  2. Few platforms can create advanced analyses.
  3. Data scientists are responsible for the majority of automation tasks.

From Analyst to Data Scientist

There are two main ways to use your data science skills to grow your data-centric career. Either become a data science expert (pursuing a job as a data analyst, database developer, data scientist, etc.) or move to an analytics role like a functional business. Analyst or data-driven manager. Both career paths require basic skills and knowledge of data analysis, programming, data management, data mining and data visualization.


Despite the dual track, the evolving nature of the relatively new field means that the career path is flexible. Data science professionals, such as data analysts, can focus on the role of data science or data system developer. (Depending on where they are deepening their expertise.)

But by expanding our knowledge of artificial intelligence, statistics, data management and big data analysis, data analysts can move to the role of data scientist.

By building existing technical skills with Python, relational databases and machine learning, data analysts can become data system developers. Many of these skills can be learned independently through hands-on experience. Also through online data science courses. This guide focuses primarily on the career path of data science.

What does a Data Scientist do?

Data scientists answer business questions based on the context of the data. They tend to use the data to develop new product features. Also, do more modelling and open research. We spend a lot of time cleaning up our data. To ensure its compatibility with models and machine learning algorithms, it can be used. If you watch Netflix and see a personalized list of recommended shows, machine learning algorithms and data science are working.

In addition, a subset of data science work is predictive analytics. “Predictive data analysis, as the name implies, is more complicated. Because it predicts what might happen in the future based on historical data or data crossovers between multiple datasets and sources,” Rafael said. Amazon Web Services Partner Solutions Architect and Instructor to Get Started with Data Analysis on AWS. “In short, we try to predict the future based on past behaviour. The use of neural networks, regression and decision trees is widely used in diagnostic analysis.”

Core Data Science Skills

Big Data

All data is large or complex datasets that traditional data processing software cannot manage. For this reason, data scientists should be familiar with the open-source distributed processing system Apache Hadoop or Apache Spark.

Data Modeling

It is the process of formatting specific data in a database.

Data Visualization

This is a graphic representation of the data used to present trends and insights.

Machine learning

Machine learning is a set of techniques used to predict and predict data.


Knowledge of programming languages ​​such as Python and R is essential for automating data manipulation.


Being a statistician is not a requirement, but you need to understand the format of the statistics that will be applied to interpret the data.


Data scientists do not work in silos. They are often part of a large data science team of data engineers, software developers, and more.

In Short, A Data Analyst is responsible for:

Answering questions about the data. Unlike data scientists, data analysts are not intended to use data to identify trends or understand the future of the business. Your job is to analyze historical data and create and run A / B tests on your product or design system. They should possess an understanding of how tools like Tableau store and consume data.

Core Data Analyst Skill

A / B Testing

It is a statistical approach used to compare two versions of a variable in a controlled environment. It is utilized to determine the performance of different variable versions. Thereby identifying the one that performs better.

Domain Knowledge

Domain knowledge can be considered a specialization. For example, if you have extensive retail experience and retail domain knowledge.


Microsoft Excel is often used to manage small datasets.

Data Visualization

Like data scientists, data analysts need to know how to use data. So as to tell their stories to interested parties using data visualization tools such as Tableau.


Data analysts must have skilled programming skills in languages ​​such as R and Python.


SQL is a database language used to manage data and build database structures. It is often used instead of Excel. Because it is suitable for processing large amounts of data.


As a data analyst, you need to report data insights. In short, you also need good communication and presentation skills.

Data analysts are generalists.

That is, work across multiple teams and roles. I enjoy working on well-defined and structured issues. Use this data to extract and create reports that are valuable to your business. Successful data analysts generally enjoy some complexity. But not as much as data scientists. Here’s how to determine if you are eligible to become a data analyst:

  • You are a generalist
  • You enjoy working beyond functionality
  • Enjoys solving specific problems

Whereas, Data scientists love complexity.

You enjoy answering a wide range of amorphous questions. Also, you are successful in project-based tasks and enjoy providing insights. Data scientists are less likely to tackle different tasks than data analysts. Therefore, you may be suitable for your career as a data scientist if:

  • Enjoying the complexity
  • Enjoying ambiguity
  • You like delving into a single question
  • You`re okay with not finding an answer to a problem

Demand For Data Scientists

Data scientists are still in high demand and in low supply. According to IBM, this trend will continue strongly over the next few years. The United States Labor Statistics Bureau also agrees with this statement. The Bureau has achieved strong growth in the field of data science. And it predicts that employment will increase by about 28% by 2026. Given that 28%, there are about 11.5 million new jobs in this area.

In the long run, it would be unwise to oppose data science as a career. Especially when expanding into relevant positions. Such as research engineers and machine learning engineers. So, is data science still an ambitious career in 2022? The answer is certainly yes! Global demand for data scientists remains unabated and there is no competition for these jobs.

Therefore, making data science a highly lucrative career path option. There are many indications that the data revolution has just begun. According to the latest big data and AI executive surveys, only 39.3% organizations currently manage data as an asset. Many people are aiming for it, but only 24.0% have created a data-driven organization. Although these numbers are to increase in the coming years.


Technologies such as AI and machine learning are now commonplace. But when it comes to data science, there is plenty of room for improvement. Demand for data science jobs will remain high for the next few years. This is evidenced by the projected growth of various related industries. In addition, all the data science statistics presented here represent a thriving industry with great potential.

Naman Singhal is listed as a co-author of the above article with guidance from our trainer Bharti Goel, one of our trainers who takes training in Data Analytics.

We have self-paced, 1 to 1 Personal + Doubt session plan for various certificate training. For MORE INFORMATION, drop us an email at or call +91-95.5511.5533.


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