Updated on 01st Mar, 23 781 Views

This blog will cover the following:

What is Data Science?

Many may argue, but data science is one of the hottest career options in the 21st century. In today’s technological and digital world, there are many questions that can only be answered through big data. Data science has made its mark on small companies and startups to leading MNCs around the globe.

Now, to clear your doubts regarding a career in data science, let us first understand the meaning of the term. It refers to analyzing past data and predicting future business growth with the help of deep learning and machine learning algorithms, mathematical and statistical concepts and techniques, probability, and numerous other advanced tools and technologies. The overall gist of data science is to look for hidden patterns and market trends from the given business data to make improved and well-informed business decisions for the future.

In data science, you not only need to have a good understanding of Python and SQL, but you also need to have the ability to use and combine your analytical and statistical skills to gain useful information and insights from the given business data and make necessary improvements in your organization.

You have understood in-depth what data science is and what can be done with its help. Now, you will now read about data scientists and their career paths.

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Who is a Data Scientist?

Data scientists are professionals who possess numerous skills. These professionals are a combination of statisticians, trend-spotters, mathematicians, and computer scientists. The main job role of these professionals is to decipher and decrypt large chunks of data and further analyze them to explore and extract patterns to gain valuable insights. Data scientists operate in the world of IT, business, and several other industries by analyzing complex sets of data to gain insights that can be leveraged into actions by organizations.

Data scientists generally collect and analyze business data. They do this with the help of a myriad of tools, technologies, and techniques with one goal in mind, reaching a sensible conclusion with the help of the given data. Usually, they present their organizations’ data in a visual format so that the other members of the organizations can visualize the data in a comprehensive format.

What Do They Do?

Let us briefly understand what these professionals do and what is demanded by a data science career. We will also take a look at their various roles and responsibilities.

The main role of a data scientist in an organization is to perform data mining, and data cleaning, and further represent the data with data visualization tools to the members of the organization such that they can read and comprehend the presented data. Organizations look for these professionals so that they can help store, manage, and analyze large amounts of raw organizational data, which is provided in both structured and unstructured format.

Data Science Career Path

Data science is now a term known and used across various industries. Hence, organizations expect data scientists to have a much more comprehensive skill set and a broader understanding of various business processes. So, choosing the right career path involves acquiring the right skills, knowing the job requirements, and exploring the correct career opportunities. To kick-start your career and be an expert in the area of Data Science I would strongly recommend you to go with Intellipaat’s Data Science training in Bangalore in Collaboration with IIT Madras.

Typically, the career trajectory of a data scientist involves working across various roles such as data analyst, data engineer, business intelligence developer, and data scientist. These are considered to be the main phases or steps in the career path of a data scientist.

We will now cover the basic corporate ladder and skill set of each of these roles:

Data Analyst

Skill Set: A data analyst is skilled in data modeling, data cleaning, visualization or business intelligence, and R and/or Python programming. A data analyst also having knowledge in tools such as Tableau is highly recommended.

Corporate Ladder: As an entrant, a professional start their career as a data analyst. After gaining two to four years of experience, they go on to become an associate data analyst and later move on to becoming a senior analyst and finally a product manager with approximately nine to ten years of experience.

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Data Engineer

Skill Set: A data engineer is skilled in database management, data mining, programming, business acumen, visualization, and more.

Corporate Ladder: A data engineer starts from the junior level and goes on to become the product manager in a span of ten to twelve years, based on the skill development over these years.

Business Intelligence Developer

Skill Set: The core skill set of a business intelligence developer includes programming, visualization, data modeling, business acumen, etc.

Corporate Ladder: A business intelligence developer starts their career as a junior business intelligence developer and reaches the level of a team lead in a span of twelve to fifteen years, depending on the skill development and experience gained over the years.

Data Scientist

Skill Set: The core skill set of a data scientist includes statistics, mathematics, programming, business acumen, and problem-solving.

Corporate Ladder: A data science aspirant can start their career as a junior data scientist or any of the above-mentioned roles such as data analyst, business intelligence developer, etc. After gaining the required skill set, they can make a transition to the data scientist profile. However, one point that needs to be considered is that organizations might rename some positions as data scientists. Hence, the role and responsibilities should be considered carefully when it comes to the profile of a data scientist.

Now, let us discuss the various skills and other requirements necessary to pursue a career in data science.

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Data Scientist Job Requirements

The following are the must-have skills that you need to acquire to make a career transition to data science:

  • Learn statistics and mathematics
  • Have excellent skills in R, Java, Python, or any such programming language
  • Understand the extract, transfer, and load (ETL) concept and its process
  • Have knowledge of machine learning algorithms and tools
  • Gain experience in data wrangling, data visualization, and data exploration
  • Learn concepts and techniques of deep learning
  • Gain industry experience in big-data-related problems

To have a successful data science career, you need to have the above-mentioned skills. You also need to have working experience in real-time projects; this will act as an additional benefit for you and highlight your data science expertise when you apply for jobs. You can gain these data science skills, along with the necessary experience, by signing up for one of the best online data science courses. This holds especially true if you get trained from the best courses from Intellipaat as in these courses, besides getting trained, you will also gain hands-on experience in working on industry-based projects that will help you enhance your skills and learning experience.

You will come across the possible job growth you can have in this domain.

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Data Science Career Growth

As per the anticipation of the Bureau of Labor Statistics, there will be over 13 percent growth in jobs in the IT field between the years 2016 and 2026.

As per Indeed, there will be about a 29 percent increase in job openings for professionals in this domain this year.

Glassdoor mentions that there are over 32,500 jobs available for data scientists only in the United States.

You have read about the demands and job growth in the field of data science. Now, let us discuss the numerous job opportunities and openings available for data scientists and other data science professionals.

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Skills Required to Start a Data Science Career

Having a successful career in data science demands prominent data science skills such as data analysis, machine learning, predictive modeling, visualization, and Hadoop. To keep advancing in this field, you need to possess skills such as critical thinking, problem-solving, etc. Now, let us understand some of these skills one by one:

  • Data Analysis: It is a process that involves data cleaning, data transformation, and data modeling for drawing out useful information and insights for business decision-making.
  • Machine Learning: It is the application of data science and statistical tools and techniques to build computer systems that can learn on their own. Algorithms are used to build models that can imitate humans. A data scientist must possess the required skill set to build these models. There are numerous opportunities under Machine Learning. To grab these opportunities one should go for Machine Learning Online Course and upskill themselves.
  • Predictive Modeling: It refers to a mathematical concept where current and historical data is used to predict future events, trends, and behavior. Possessing this skill is highly recommended as predictive modeling is routine for many data scientists.
  • Critical Thinking and Problem-solving: It refers to the ability of utilizing knowledge, facts, figures, etc., to solve problems and make judgements. How well a data scientist can identify the right solution to a problem is considered by recruiters.

Data Science Career Opportunities

There are various job roles and titles in leading organizations for data science professionals. Some of these data science careers opportunities include:

  • Business Analyst: These professionals are responsible for data extraction from various sources to perform business analysis and generate reports, dashboards, and metrics for monitoring organizational performance.
  • Data Analyst: These professionals build a cross-brand and robust strategy of data acquisition and analytics. They are also responsible for designing raw data transformation for analytical applications. 
  • Data Scientist: These professionals use data analysis and data processing to understand business challenges and offer the best solutions to the organizations.
  • Product Analyst: These professionals are responsible for analyzing large chunks of data to support the product team in their product decision-making.
  • Data Science Engineer: These professionals design, build, and integrate data to build the big data infrastructure for data science.
  • Analytics Consultant: These professionals are responsible for developing and carrying out the organizations’ analytical solutions.
  • Machine Learning Engineer: These professionals, with the help of several machine learning tools and technologies, build statistical models with large chunks of business data.

Even though the above-mentioned job titles and roles are distinctive, some organizations use data scientists interchangeably with them or even rename some of the above job titles to data scientists.

Next in this blog, we will go through the salaries earned by these professionals.

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Data Science Career Salary

Data science professionals are one of the highest-earning IT professionals today. This is due to the exponential rise in their demand. However, since there is not enough availability and supply of professionals in this domain, they are generally very well paid.

According to Glassdoor, on average, data scientists in the United States earn in the range of US$113,000 p.a. to US$154,000 p.a.

In India, the average salary of data scientists is about ₹992,000 p.a. This may rise to ₹1,978,000 with expertise and experience.

So, if you wish to learn data science and pursue a career in this field, then you must register for one of the best online courses in this domain today!

You can clear any doubts you have about Data Science by reaching out to us in our Data Science Community!

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