The Anatomy of a Data Scientist [Infographics]
By Rohit Sharma
Updated on Apr 18, 2025 | 5 min read | 7.24K+ views
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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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What does it take to become a Data Scientist?
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.
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What are the required Technical Skills?
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.
- Statistical Modelling to understand and interpret the data sets strategically.
- Know at least one of the top programming languages like R, Hive, Python, SQL, and many more.
- Understanding of Statistics and Mathematics
- Understanding Software Engineering and Machine Learning to interpret and manage large data sets.
- Understanding Artificial Intelligence to help data science in getting solutions and results for specific problems.
- Data Visualization for graphical representation of data.
What are the Roles and Responsibilities of a Data Scientist?
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
- The work is to organize, store, and analyze data efficiently while viewing a company’s data system.
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Research and Analysis
- It is the process of systematically applying logical and/or statistical techniques for evaluating data.
Refine business procedures
- The old data is substituted with new data for more efficient business management.
Smart decision making and planning
- Data is combined with analytics for improving decision making and planning for any business.
Generating actionable insights
- Actionable insights are obtained by analyzing the raw data sets.
Simplifying the complex procedures
- The complex datasets are analyzed to obtain useful data that is useful for the business.
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Benefits of becoming a Data Scientist
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:
- Data Scientist is the person responsible for uncovering the secrets that data keeps hidden. So, organizations put a high premium on the person who helps them understand trends and customer behavior.
- Leading Global organizations like Amazon, Google, Apple, etc. are searching for Data Scientists.
- Assured employment in the future with further enhancements of technology.
- Data Science has various applications in different industries, including Consulting, Manufacturing, Healthcare, Marketing, Pharmaceuticals, Manufacturing, and many more.
- According to a survey conducted in 2011, 94% of Data Science graduates have found it a satisfying job.
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What is the Average Salary of a Data Scientist?
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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Conclusion
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/
Frequently Asked Questions (FAQs)
1. What qualifications do you need to become a data scientist?
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.
2. How is a data scientist different from a data analyst?
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.
3. What industries hire data scientists the most?
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.
4. Do data scientists need to have strong programming skills?
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.
5. What are the biggest challenges faced by data scientists?
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.
6. Is data science a good career for the future?
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.
7. How long does it take to become a data scientist?
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.
8. What are some common misconceptions about data scientists?
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.
9. Do data scientists work independently or in teams?
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.
10. How does artificial intelligence impact the role of a data scientist?
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.
11. What are the ethical concerns in data science?
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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