How to Become a Data Scientist: A Step-by-Step Guide for Beginners

Jaro Education
13, July 2025
4:00 pm

Aspiring to become a data scientist? Well, with lucrative salaries, it is a promising career path. Becoming a data scientist generally requires a combination of education, skills and experience. This includes developing a strong foundation in mathematics, statistics and programming, as well as hands-on experience through projects and internships. Further, pursuing a bachelor’s degree in a relevant field like computer science, statistics or mathematics, or a master’s degree in a specialised area like data science or machine learning, can be beneficial to becoming a data scientist. In this blog, we will show you how to become a data scientist, along with an overview of ‘what is data science?’ and ‘what does a data scientist do?’ So, let’s get started!

What is Data Science?

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Data science is a multidisciplinary academic field that leverages scientific methods, algorithms, processes and programming to uncover knowledge and insights from structured and unstructured data. It integrates disciplines like statistics, machine learning (ML), data analysis, and visualisation to extract hidden patterns, trends, and relationships in data.

Data science is crucial for informed decision-making, forecasting, and problem-solving across industries. This allows businesses to make better data-driven decisions, extract valuable insights and inform strategies, which leads to anticipating market trends, optimising operations, and improving customer satisfaction.

What Does a Data Scientist Do?

A data scientist works to analyse complex data sets deeply, utilise statistical and machine learning techniques, and build predictive models to determine data-driven decision-making. Data scientists perform several different tasks and take on various roles, including data acquisition, data cleaning, exploratory data analysis, modelling, and communication.

Data cleaning is crucial in the data science roadmap, ensuring accurate, consistent, and complete data for analysis. Without cleaning, data analysis results can cause unreliable models and misleading insights. Therefore, in order to clean and prepare data for analysis, data scientists utilise statistical methods and engineering skills.

An exploratory data analysis is an essential step in any data analysis project. It includes investigating a dataset to gain insights, uncover patterns, and find variances. Data scientists usually collaborate with other team members, such as data engineers, data analysts, IT architects, application developers, and business stakeholders, to collect, analyse data and derive actionable insights.

How to Become a Data Scientist in India

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The following are the steps on ‘how to become a data scientist in India.’

Step 1: Develop a Strong Foundation in Mathematics and Statistics

Mathematics and statistics are two important areas for data science that you must know to understand the data.

Becoming a good data scientist requires a basic understanding of mathematics. These include linear algebra, vector calculus, and statistics/probability. While you don’t need to be a math expert, understanding these concepts is a must for a data scientist, providing the foundation for understanding and applying data analysis methods, machine learning systems, and statistical inference.

Step 2: Learn a Programming Language (Python/R)

Learning a programming language such as Python or R is essential for becoming a data scientist. Python and R are the two most popular, open-source and versatile programming languages. While Python is widely used in various fields beyond data science, such as web development, automation and software engineering, R specifically focuses on statistical computing and data analysis.

One of the most crucial concepts in Python that all data scientists should know is file processing. A data scientist should be proficient in the basic operations of the file, like reading, appending and writing, as well as applying loops to the read data.

Translating statistical approaches to computer models is one of the most critical concepts in R that you should know. The R language has plenty of statistical packages that can quickly and easily be applied to datasets, helping build statistical models more easily.

Step 3: Learn Databases and Big Data Tools

Becoming a data scientist requires learning databases and big data tools. While you don’t need an in-depth understanding of databases, you just need to know how to work with databases and big data tools so that you can interact with and operate data effectively. Databases are essential for managing and storing data, while big data tools help process and analyse large datasets efficiently.

Understanding Structured Query Language (SQL), one of the most popular database query languages, is essential for retrieving and operating data stored in relational databases like MySQL or PostgreSQL. It allows users to add, store, modify and delete in database tables. Big data tools like Hadoop and Apache Spark are prominent software frameworks used to manage and process big data for analytics.

Step 4: Master Analysis Techniques

There are various techniques used to analyse a dataset, but it’s not necessary to know all analytical methods. Understanding the uses of a particular approach is essential. The commonly used analysis techniques in the industry include cluster analysis, regression, time series analysis, and cohort analysis. These methods provide a range of ways to analyse data for insights and decision-making.

Step 5: Keep Practising

To become a successful data scientist, it’s important to practice everything learned so far, focusing on theoretical understanding and practical application. This includes practising coding, analysing datasets, and building projects to consolidate your knowledge. Constantly update your skills by keeping up-to-date with industry trends and emerging technologies.

Step 6: Continuous Learning and Skills Development

As data science is a rapidly evolving field, continuous learning and skill development are essential to becoming a good data scientist. As a data scientist, you not only need to build a strong foundation in mathematics, statistics, and programming but also develop proficiency in machine learning, data visualisation, and communication. Further, developing your skills by earning certifications in data science, machine learning, and big data analytics is essential, which will help you stand out in the competitive job market.

Step 7: Networking and Job Search

To successfully get a data science job, you should focus on networking and a strategic job search. Networking includes building a professional network through online and offline connections and attending industry events & conferences, while job searching involves various platforms to find and apply for the right roles.

Step 8: Apply for Data Scientist Positions and Prepare for Job Interviews

After obtaining the essential skills and experience, you can apply for data scientist positions in different industries, including technology, finance, healthcare, e-commerce, and marketing.

Analyse the job description for key skills to tailor your resume and cover letter. Then, modify them to showcase your relevant skills, experience, and projects that match the requirements.

To crack a data scientist job interview, prepare by researching the company, reviewing the job post, and practising common interview questions. However, focusing on technical skills like coding, statistics, and machine learning, developing your communication skills, and demonstrating problem-solving skills are essential to prepare for a data scientist job interview.

How to Become a Data Scientist After 12th

Now, let’s understand ‘how to become a data scientist after 12th’ by following the below-mentioned steps.

Step 1: Complete Your 12th With Math and Science

To become a data scientist, excelling in mathematics, statistics, and computer science during your 12th grade is essential. These subjects provide the foundational knowledge and skills required for data analysis, programming, and machine learning, which are crucial for a data scientist’s role.

Step 2: Pursue a Bachelor’s Degree in the Relevant Field

After passing the 12th, pursuing a bachelor’s degree in a relevant field, like computer science, data science, and mathematics, is the next step. While choosing a bachelor’s degree program, focusing on key areas such as data mining, machine learning, and database management can be beneficial. These areas offer opportunities for specialisation and career advancement, helping you develop the essential skills.

Step 3: Enhance Your Data Science Skills

While a bachelor’s degree program can provide a theoretical understanding of the subject, enhancing it with practical skills in programming languages, like Python, R, SQL, and SAS, is crucial for careers in data science, analytics, and related fields.

Step 4: Gain Knowledge in Essential Data Science Tools

To succeed as a data scientist, gaining knowledge in essential tools, including Data Visualisation, Machine Learning, and Big Data, is crucial. Data visualisation helps present effective communication of insights, while machine learning enables predictions and pattern recognition. Big data skills help handle large datasets.

Step 5: Learn from Data Science Internships

A data science internship provides a valuable hands-on learning experience, boosting your career prospects. It helps you understand how to apply theoretical knowledge to real-world problems, enhance practical skills, and improve industry exposure.

Step 6: Upskill Your Career by Earning a Data Science Certification

Earning a data science certification can be a great way to upskill your career and enhance your job prospects, especially in the evolving field of data science. The certification highlights your technical skills and demonstrates your ability to apply data science in real-world business contexts.

Step 7: Apply for Data Scientist Jobs

Now, it’s time to apply for data scientist jobs. First, explore companies in fields like tech, finance, healthcare or retail, which hire for data science roles. Then, prepare a strong resume and cover letter, highlighting related skills and experience. Finally, use professional platforms and company websites to find and apply for open positions.

How to Become a Data Analyst

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Now that you know ‘how to become a data scientist after 12th’, let’s move on to ‘how to become a data analyst’

Step 1: Get a Foundational Education

To become a data analyst, pursuing a bachelor’s degree in relevant fields like Mathematics, Statistics, Computer Science, or Data Science is a crucial step. On the other hand, gain online certifications or explore professional courses focused on data analysis. A master’s degree in Data Science, Business Analytics, or a related field can be beneficial for advanced roles.

Step 2: Build Your Technical Skills

Becoming a data analyst requires building your technical skills, such as learning essential programming languages like Python and R, understanding SQL for database management and data extraction, and developing ability in data visualisation tools like Tableau or Power BI to effectively communicate findings.

Step 3: Gain Hands-on Experience

Gaining hands-on experience is crucial to become a successful data analyst. Hence, work on data analysis projects and consider internships or entry-level analyst roles to gain real-world experience.

Step 4: Develop a Portfolio of Your Work

Develop a portfolio demonstrating your data analysis projects, including visualisations and findings. Highlight your skills and show your capability to solve real-world problems.

Step 5: Get an Entry-level Data Analyst Job

Highlight your skills and experience in your resume and cover letter. Then, apply for entry-level data analyst jobs or internships.

Step 6: Gain Certification

Data science is a fast-evolving field, so pursuing certifications in data analytics will determine your expertise.

Final Thoughts

This blog on ‘how to become a data scientist’ highlights essential skills and subjects required for a rewarding career in the field of data science. Becoming a data scientist requires continuous learning, devotion, curiosity, skill development and practical experience. However, developing technical skills, like programming languages, machine learning and data visualisation, and gaining hands-on experience through projects or internships are some of the most important steps for a successful career in data science.

Now that you know ‘how to become a data scientist in India’, ‘how to become a data scientist after 12th, and ‘how to become a data analyst’, elevate your career in the field of data science by enhancing your knowledge and developing your skills with our course Professional Certificate Programme in Advanced Analytics & Business Intelligence by IIM Kozhikode. For more information about the programme and the enrolment process, visit Jaro Education now!

Frequently Asked Questions

How much time is needed to become a data scientist?

It usually takes between 6 months and 2 years to become a data scientist, depending on your education, how committed you are, and how you learn. If you are new to the field, then you should expect to spend some time learning programming, statistics, and machine learning. However, if you already know about these topics or are pursuing a structured path, then you might complete the journey more quickly.

What industries are most suitable for data scientists?

Many industries, especially finance, technology, healthcare and retail, look for data scientists. Data scientists are also needed in other sectors, like media, telecommunications, e-commerce and professional services. Moreover, the automotive, energy and government sectors are now hiring data scientists for different reasons.

Can I find online courses for those interested in data science?

There are many online courses available for people interested in data science, from basic to advanced. They cover topics like statistics, programming and machine learning, offering practical projects and the chance to earn certificates.