How to Become a Data Scientist After 12th?

Introduction

Becoming a data scientist is among the most lucrative careers in today’s fast-paced and digitally growing landscape. In today’s dynamic world, data is king and understanding and interpreting that data to turn up the business’s overall growth and revenue is queen.

Having said this, you know the importance of data and the steep climb the industry is geared for in the coming years.

Companies have continuously evolved over the years and are more consumer-centric than ever. Global brands and even the smallest tech startups are constantly conscious of gathering and analyzing customer data to offer them better and more personalized products and services.

With trillions of data being shared across the globe daily, it has become crucial to convert large chunks of raw data into actionable insights that will directly lead to strategic decision-making, increasing brand growth and profitability and driving innovation.

In this article, you will get a brief overview of a data scientist, salary prospects and career growth in the field, how to become a data scientist in India after 12th, and the top skills you need to master.

How to become a data scientist in India after 12th?

Becoming a data scientist in India is no rocket science. Still, it needs a streamlined focus and a strategic approach towards mastering the right skills and getting acquainted with the practical knowledge required by the industry.

According to NASSCOM, India had a talent base of 416,000 data professionals as of August 2022 against a demand of 629,000. By 2026, the country is expected to host over a million data science professionals.

India is becoming a major source of revenue in terms of data science. According to Mordor Intelligence, the total market size of Big Data in India will roughly be around $2.17 billion and is projected to grow to $3.38 billion by 2029, at a CAGR of 7.66%.

Secondly, the Indian data science market earns 60% of its revenue by exporting data science expertise to the United States, which allows expansion in the field in India as the US market grows. (Source)

Being a pioneer in data science requires you to be fundamentally strong in 3 core fields - Mathematics, Computer Science, and Statistics, as these subjects provide you with the base needed to interpret, understand, and group data.

It will require you to be proficient in programming and data analysis, have a knack for playing with numbers and preparing comprehensive, easy-to-understand reports, gain the necessary certifications and credentials, and more.

Below are a few of the listed pointers highlighted to help you grow your career in data science and explore your roadmap to becoming a data scientist after 12th.

But before we proceed to the main jargon, let’s explore the primary difference between a data scientist and a data analyst - let’s clear the clutter.

Data Scientist vs Data Analyst

Just as alligators and crocodiles are not the same, but both are lookalike, data scientists and data analysts are not the same, though both may have overlapping roles.

Both data scientists and analysts work with large chunks of data. However, their roles and responsibilities differ due to the scope and complexity of the work.

Data Analysts are primarily involved in interpreting and extracting useful information from large chunks of raw datasets, identifying trends and patterns in the data, and then converting that information into reports that support strategic business decisions.

They use data analysis tools like Excel and SQL and data visualization software like Power BI and Tableau to make data more accessible.

On the other hand, data scientists work closely with analysts to build predictive AI models using advanced machine learning algorithms and big data to extract insights from raw data and forecast future trends.

They might also train AI and large language models (LLMs), identify existing business patterns, and suggest projected patterns to foster business growth and profitability.

Read more on the types of data scientists, learn the roles and responsibilities of each, and choose the niche of data science that you want to pursue a career in.

How to become a data scientist after 12th?

Before we begin, here’s the ice breaker - it is strongly recommended that you pursue a Bachelor’s Degree in any relevant field before you dive into pursuing data science as a career.

Why? A college education will help you build the foundational blocks, making it much easier to understand the complex terms and concepts associated with data science.

However, if you have already decided to become a data scientist after 12th and pursuing a college degree is not part of your immediate plans, you can still become one provided you gather the knowledge and skills from other sources, viz. Certifications and specialized degrees.

However, if getting a high-paying job is in your interest, getting a college education first is best, as employees with degrees are typically preferred.

Let’s dive deeper into the roadmap on how you can become a data scientist after 12th.

1. Complete your 12th standard with PCM & Computer Science

You need to complete your 12th standard with a strong background in Mathematics and Computer Science, as you will need this base to understand complex algorithms and statistical models, which form the backbone of data science.

Put the core focus on Mathematics, Statistics, Probability, Calculus and Combinatorics, and Computer Science if you want to pursue data science as a career.

Therefore, it’s crucial to pursue PCM as your stream in 12th and Computer Science as an additional subject in place of PCB.

2. Earn a Bachelor’s Degree in a relevant field

Try to earn a bachelor’s degree in any quantitative subject that emphasizes numbers, analytical thinking, problem-solving, and uses mathematical or statistical methods.

A firm grasp of quantitative subjects is essential, so pursue a bachelor’s degree in Mathematics, Computer Science, Statistics, Data Science, Economics, or any engineering stream.

Here are a few undergraduate degrees that you might want to consider.

• BTech in Data Science (Duration - 4 Years)
• BSc in Data Science & Applications (IIT Madras, Duration - 4 Years)
• BCA in Data Science (Duration - 3 Years)

3. Be proficient with programming languages and data analysis

Learning programming languages and being proficient in them forms the foundation of being a pioneer in data science. Master the languages that are widely used in the field, viz. Python, R, SQL, Julia, MATLAB and/or Java and Scala (if working with Big Data).

Be good at analyzing numbers and playing around with data. Learn data visualization and manipulation techniques. Get familiar with data analysis tools such as Excel, SQL, Power BI, Tableau, SAS, RapidMiner, etc.

Lastly, get acquainted with the tools and libraries used for data science, such as Pandas, NumPy, SciPy, TensorFlow, PyTorch, Matplotlib, etc.

4. Learn machine learning and statistics

Understanding machine learning algorithms and methodologies is of prime importance in data analysis for the following reasons - pattern recognition, predictive analysis, classification and clustering, anomaly detection, data cleansing, etc.

Learn the major machine learning libraries used for data science, such as Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM.

Also, equip yourself with statistical methods and concepts relevant to data analysis, which is crucial for interpreting data, identifying patterns, and making informed decisions.

Some of the top statistical concepts that you can learn and master are descriptive statistics, probability theory, inferential statistics, correlation and causality, regression analysis, analysis of variance (ANOVA), time series analysis, nonparametric methods, multivariate analysis, bootstrapping and resampling, and survival analysis.

5. Gain certifications

It’s essential to keep up with the fast-paced and dynamic universe of data science and update yourself with the latest tools and technologies, whether data analysis tools or data visualization dashboards.

To achieve the same, you can enroll in data science online courses, typically short crash courses spanning 3 to 18 months.

These courses are usually online live classes taught by industry experts that help you gain practical experience, give you much-needed industry insights on what’s happening and in demand, and give you wide exposure.

Additionally, the course curriculum is designed to teach only the subjects and touch base on the topics needed to crack a data analyst job. The syllabus is designed to be dynamic, automatically incorporating emerging tools and technologies in response to market demand.

Besides live lectures, these courses are self-paced, which helps accommodate students' varied learning patterns and schedules.

Lastly, the low cost and EMI options available with these courses provide an added advantage compared to traditional and regular classes while equipping you with industry-standard and relevant education.

6. Gain practical experience through internships

Suppose you are a fresher and just starting in data science. In that case, it is highly unlikely that you will begin your career as a data scientist, as that’s a senior-level position requiring quite a bit of experience.

Instead, actively look for internships or entry-level roles as a data analyst, business analyst, operations research analyst, or other relevant domains.

By working in these positions, you will get hands-on experience handling data, solving real-world problems, and gaining experience working with cross-functional teams.

You will build the needed expertise for being a data scientist, such as extracting useful insights from raw data, converting complex information into easy-to-understand reports that can drive strategic decision-making using tools such as Power BI, training AI models using advanced ML algorithms to do predictive analysis, etc.

7. Build your portfolio

A portfolio will always be the cherry on top and help you stand out. Once you master the essentials of data science, use that knowledge to build projects that solve real-world problems involving machine learning, data analysis, and predictive modelling.

You can always segment your projects from beginner to advanced level, demonstrating your domain expertise and enhancing your job prospects.

For beginner-level projects, you can consider building a house price prediction model or movie recommendation system based on user sentiments.

You can build speech recognition or real-time object detection systems to upgrade to intermediate and advanced.

Showcase your projects on GitHub and contribute to open-source projects to establish work credibility and build authority.

Skills you need to be a data scientist

Suppose you aspire to become a pioneer in data science and wonder how to become a data scientist after 12th. In that case, you will need a unique amalgamation of technical, analytical, and soft skills.

Here’s a quick overview of the core skills that will help you become a data scientist.

1. Programming

Having top-notch programming skills to ace in data science is a no-brainer! You must be proficient in programming languages primarily used in data science, such as Python and R.

It is essential for data manipulation, statistical models, and building machine learning models. Python is largely preferred due to its rich ecosystem of libraries, such as Pandas, NumPy, SciPy, etc.

2. Mathematics & Statistics

A core foundation in statistics and linear algebra is essential to understanding data distribution, hypothesis testing, and the mechanics of machine learning models.

It allows data scientists to identify patterns in the data and build predictive models.

3. Data Visualization

Data visualization tools like Power BI or Tableau help create compelling and easy-to-understand visual representations of complex data, aiding in communicating critical insights to non-technical stakeholders, which helps in informed decision-making.

4. Data Wrangling

Data Scientists may need to clean and preprocess large amounts of messy, incomplete, or unstructured datasets to make them suitable for analysis. This ensures that the raw data is converted into meaningful insights.

5. Machine Learning

A thorough understanding of machine learning algorithms, models, and supervised or unsupervised learning is important. This is to predict outcomes in predictive analysis models and uncover and identify patterns in raw data.

6. Deep Learning

Deep Learning frameworks like TensorFlow and PyTorch are essential in handling complex tasks such as image or speech recognition.

7. Big Data

When handling large-scale datasets, you must be familiar with big data technologies such as Hadoop, Spark, or Hive. This will allow for efficient data processing and analysis in distributed environments.

8. SQL & Database Management

Expertise in SQL and database management and optimization is required as SQL is needed to extract and manipulate structured data stored in databases, which is crucial for data retrieval.

9. Cloud Computing

Not everything can be stored on a physical database or servers, especially when working with large-scale data. Therefore, proficiency in cloud platforms like Google, AWS, or Azure is critical for deploying models and working on large-scale projects.

Cloud computing enables scalable solutions for data storage and processing.

10. Data Ethics

Data, be it of a company or customer, is always critical. Therefore, data scientists should ensure the responsible and sensible use of sensitive data and compliance with legal regulations, such as GDPR or CCPA.

11. Version Control

You need good command over version control systems like GitHub or GitLab, which is crucial when collaborating with other team members on a single project.

Also, knowledge of git is important for maintaining code-versioning, which helps keep organized workflows.

12. Data Engineering

Data Scientists are not in standalone roles but must work with diverse teams, including analysts, engineers, and other experts.

Basic knowledge of data pipelines and ETL (Extract, Transform, Load) processes allows data scientists to collaborate seamlessly with data engineers, allowing for smooth data flow and accessibility.

13. Time & Project Management

Data Scientists work on a varied diversity of critical projects at a single time. Handling multiple projects with varying deadlines requires exceptional time management and organizational skills.

Familiarity with project management tools such as JIRA or Trello is a bonus.

14. Business Acumen

You must have business acumen and an in-depth understanding of business problems and objectives to align your analytical approach with organisational goals.

15. Problem-Solving

Problem-solving is the core of being a data scientist, as you must think critically and creatively to tackle complex challenges. Problem-solving ability forms the core of extracting actionable insights from data.

Data Scientist Salary in India & Career Opportunities

Data Science is a booming industry in the Indian market and is expected to grow at a CAGR of 57.5% from 2023 to 2028. Data science jobs are also expected to grow by 57% from 2023 to 2028.

Companies increasingly rely on data to make strategic decisions that foster growth and profitability. Data Science is still in its infant stages in India and is an untapped industry with exponential potential to create employability opportunities.

According to Glassdoor, the average salary of a data scientist in India is roughly around 13.5 LPA, with a salary range starting from 7 lacs and going up to 19 lacs. With more experience in your bucket, you can pocket a salary of up to 70 lacs or even more.

Experience plays a huge role in this industry. Therefore, starting your career in data science as an analyst works just as well.

You may begin your data science career with many roles - data engineer, operations research analyst, business intelligence analyst, data quality analyst, data architect, and more.

If you want a breakdown of the industry-standard salary for each role, read here on the different types of data scientists and the roles and responsibilities associated with each.

Takeaway

You might have understood by now why having a college degree might be quintessential to becoming a data scientist. However, it’s more important to be acquainted with practical knowledge and skills if you want to become a data scientist after 12th.

Data Science is grabbing a cemented foot, not only in India but beyond international borders and across the globe.

The Bureau of Labor Statistics has projected a 36% increase in employment in data science from 2021 to 2031, which is much higher than the average for other occupations.

Now is the time to leverage the booming technology and skyrocket your career. Hop on to the data trend and become a part of the global data workforce.