The Anatomy of a Data Scientist [Infographics]
By Rohit Sharma
Updated on Apr 18, 2025 | 5 min read | 7.16K+ views
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By Rohit Sharma
Updated on Apr 18, 2025 | 5 min read | 7.16K+ views
Share:
Table of Contents
It is estimated that nearly 150000 Data Science jobs will be created in India in 2020. Data Science has tremendous applications across industries, and it is emerging as one of the most popular career options.
With its ability to unlock valuable insights from vast amounts of data, data science is transforming industries like healthcare, finance, e-commerce, and more. As organizations increasingly rely on data-driven strategies, the role of a data scientist has become crucial, making it one of the most promising and rewarding career choices today.
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The first and most important thing needed to become a Data Scientist is education. It is essential to have a Bachelor’s degree. Approximately 85% of data scientists possess a Master’s Degree, while 45% of them possess a Ph.D. To become an expert in data science, check out our data science courses from the top institutions.
Another important aspect is the specialization or the course. For instance, 32% of data scientists have a background in mathematics and statistics, 19% in computer science, and 16% in engineering. A data science course from a recognized university or institution holds your resume higher.
Education is a crucial requirement, but it is not the only requirement. You also need to have the right set of skills as well as a high technical aptitude.
You must pick an area of Data Science where you feel comfortable before finalizing or entering the field. Identify your proficiency and do your research.
Data Management
Research and Analysis
Refine business procedures
Smart decision making and planning
Generating actionable insights
Simplifying the complex procedures
The plethora of job opportunities is just one of the many aspects of choosing this field. The high-in-demand job brings the following benefits:
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Harvard Business Review calls the job of Data Scientist as the “Sexiest Job of the 21st Century”. If you have a look at the average salary of any data scientist, it averages about $1,20,000 per annum.
Know more: Data Scientist Salary in India
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A data scientist is much more than just a number cruncher—they are problem solvers, strategic thinkers, and innovators who turn raw data into actionable insights. With expertise in programming, statistics, machine learning, and business acumen, they play a crucial role in driving data-driven decisions across industries.
If you are interested in learning Data Science and opt for a career in this field, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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References:
https://www.dqindia.com/data-science-jobs-india-increase-1-5-lakhs-2020/
A data scientist typically holds a degree in computer science, mathematics, statistics, or a related field. However, many professionals enter the field through specialized certifications and online courses in data science, machine learning, and AI. Practical experience with real-world projects is often more valuable than formal education alone.
While both roles work with data, a data analyst focuses on interpreting structured data to generate reports and business insights, often using tools like Excel, SQL, and Tableau. A data scientist, on the other hand, builds predictive models, applies machine learning techniques, and works with large, unstructured datasets to solve complex problems.
Data scientists are in high demand across industries, including finance, healthcare, e-commerce, marketing, manufacturing, and technology. Companies use data science for fraud detection, customer behavior analysis, predictive analytics, and automation, making it a critical function in various domains.
Yes, programming is a key skill for data scientists. Proficiency in Python, R, and SQL is essential for data manipulation, model building, and automation. Additionally, knowledge of frameworks like TensorFlow, PyTorch, and Scikit-learn is useful for implementing machine learning models.
Some of the key challenges include data quality issues, handling large datasets, data privacy concerns, model accuracy, and the complexity of machine learning algorithms. Additionally, communicating insights to non-technical stakeholders can be challenging.
Yes, data science is one of the fastest-growing careers, with high demand across industries. As organizations continue to rely on data-driven decision-making, the demand for skilled data scientists is expected to increase. It also offers competitive salaries and career growth opportunities.
The time required depends on your background. If you have a relevant degree, it may take 6 months to a year of additional learning and practice to become job-ready. If you’re switching from a non-technical background, it may take 1-2 years of structured learning, projects, and internships.
One common myth is that data scientists only work with AI and machine learning—while these are important, much of their work involves data cleaning, exploration, and visualization. Another misconception is that you need a PhD to become a data scientist; in reality, practical experience and skills matter more.
Data scientists usually work as part of a larger team, collaborating with data engineers, analysts, business leaders, and software developers. They work closely with different departments to understand business problems and develop data-driven solutions.
AI and automation tools are making some aspects of data science more efficient, such as automated model selection and feature engineering. However, human expertise is still crucial for understanding business problems, interpreting results, and making strategic decisions.
Data privacy, bias in algorithms, and responsible AI use are major ethical concerns. Data scientists must ensure fairness, transparency, and security when handling sensitive information and developing predictive models to avoid biased or unethical outcomes.
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Rohit Sharma is the Head of Revenue & Programs (International), with over 8 years of experience in business analytics, EdTech, and program management. He holds an M.Tech from IIT Delhi and specializes...
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