How to Become a Data Scientist After 12th: Complete Guide to Mastering Data

Have you ever wondered how you get recommendations on Netflix, and randomly come across music you like? From predicting the weather to helping you reach your favorite memes on Instagram, data science does it all. It’s the behind-the-scenes formula for the online world to function. 

Data scientists study, analyze, and organize large data sets to identify problems, predict trends, and provide insights that connect people with businesses, destinations, and more. 

If you are curious about how to become a data scientist, a good mathematical foundation and knowledge of computers are great places to start. There are plenty of degree and diploma courses that you can enroll in for guided, professional training, and industry exposure that will kickstart your professional journey. 

Data science is one of the fastest growing fields with hundreds of new professionals recruited across industries each year.  There are multiple career options with high-paying jobs – data engineers, data analysts, statisticians, business intelligence analysts, and more – that are just right to start a promising and future-proofed career.

This article will give you a sneak peek into your dream career, guiding you through the skills, courses, and jobs in data science.

What is Data Science?

Data science, simply put, is a field that studies and analyzes the logic behind data that drives the online space and helps navigate the internet better. 

It combines mathematics, statistics, programming languages, Artificial Intelligence, and Machine Learning, allowing professionals to understand patterns and predict future trends. It lets different businesses, organizations, and individuals widen their reach, position themselves more precisely in a competitive space, and bridge the gap between potential consumers and the products/services they want. 

Who is a Data Scientist?

Data scientists are professionals who study mathematics, statistics, computer science, and programming to understand how the Internet works. They survey, conduct thorough research, and study and analyze big data to find gaps and problems and come up with their solutions. 

These are highly skilled professionals who have expertise in computers and numbers and can multitask. They work closely with machine learning engineers, statisticians, and fellow data scientists and analysts to understand potential risks and suggest ways that will have a positive impact. 

Essential Skills for a Data Scientist

If you want to understand how data science works and how to become a data scientist, working on developing some essential skills is a great start – 

1. Technical Skills 

Technical skills are related to the subject matter and deal with the core concepts of data science. These are integral to your understanding of what data is and how it’s used for decision-making in today’s world – 

• Mathematical Concepts like linear algebra, probability, and calculus, coupled with statistics help in understanding and visualizing data. 

• Elements of programming and familiarity with programming languages help with cleaning, organizing, scaling, and analyzing data for building statistical models.

• AI and ML are used to analyze large and complex data sets, get insights, and make predictions. 

• Data Cleaning and Preprocessing are crucial for accurate, reliable, and consistent data. They allow smooth analysis and help precisely forecast trends. 

• Data Analysis, Visualization, and Management simplifies data, understanding patterns and improving business decisions.

2. Non-Technical Skills – 

These are soft skills that complement the technical skills and allow you to work in a team, overcome professional challenges, and navigate the complexities in the industry – 

• Critical Thinking and Problem Solving will help you make informed choices, face challenges, and solve complex problems. 

• Time Management is a key skill for all data science professionals. It helps meet tight deadlines, stay on schedule, and juggle multiple tasks. 

• Communication skills are crucial for working in a team, it will help you to effectively convey messages to your team members, de-escalate conflicts, and maintain a collaborative work environment. 

Education and Training

If you are about to complete your schooling and are wondering how you can become a data scientist after 12th, formal training and courses are a great way to get an understanding of the discipline and pursue a career. Reputed institutes like IIT, NIT, and AAFT offer relevant degree and certificate courses that provide advanced knowledge, acquaint students with the field, and prepare them for the industry.

Here are some undergraduate courses (3-4 Years) you can pursue to get a headstart in your career – 

• B.Sc. in Applied Mathematics (3 Years)
• B.Tech in Artificial Intelligence and Data Science (4 Years)
• B.Tech in Data Science and Engineering (4 Years)
• B.Sc. in Data Science (3 Years)
• B.Sc./B.Tech in Computer Science (3-4 Years)
• B.Sc. in Statistics (3 Years)

You can also opt for online and offline certificate courses to master a specific niche. These are short-term skill-oriented courses, ranging from a few weeks to 6 months, that focus on deepening subject-matter knowledge and providing advanced training. 

An added advantage is that you can pursue certificate courses in your areas of interest alongside a course or an internship and aim for better opportunities. Some popular short-term courses in the field include – 

• Certificate Courses in Programming Languages
• Certificate in Data Analysis
• Certificate in Data Science and Artificial Intelligence
• Professional Certificate in Data Analytics
• Certificate in Advanced Data Processing

Steps for Building a Strong Foundation In Data Science

Building a strong foundation requires regular practice, staying updated with what’s new in the field, and getting acquainted with the latest tools. 

So, how do you become a data scientist who’s technically proficient and has a deep knowledge of the subject matter? 

1. Understand Data – As a data scientist, you must be familiar with data structures to manage and organize data and develop algorithms that further simplify complex data and analyze transactions, browsing histories, interactions, and other online activities. 

Learn to visualize data using dashboards, graphs, and master tools like Spark and Hadoop that help scale, store, and process large amounts of data. Work on your analytical skills and practice collecting and consolidating data from a variety of sources. 

2. Master Programming Languages – Learn the relevant programming languages that are predominantly used in the industry. Familiarize yourself with Java, JavaScript, C/C++, Python, R, and SQL. These are the foundation stones of software development; mastering them will allow you to kickstart your career in data science.

3. Get A Good Grasp of Mathematics – Mathematical concepts are super important in data science. They help understand, visualize, and predict patterns in data sets. Having a good understanding of linear algebra, statistics, probability, and calculus allows data scientists to handle huge amounts of data, organize them, and come up with effective solutions. 

4. Deep Dive Into The Field of Data Science – Practicing the skills and concepts you learn along the way and applying them to solve problems enhances your understanding and lets you get a glimpse of the professional world. Building data-driven models, working on data cleaning projects and analysis and processing tasks, and participating in competitions related to the field are some ways you can perfect your skills and network with your peers.

How To Build a Data Science Portfolio?

As a new professional with little to no industry experience, your portfolio is going to be your best friend when it comes to finding jobs. Let us go ahead and understand the basics of building a data science portfolio that is strong and impactful.

Presentation 

As an aspiring data scientist, you want your work to be easily accessible and showcased in a way that makes it easy for recruiters to browse through them. 

You can create your website with a home page and dedicated pages for different personal projects and work samples. A website is a great way to highlight your professional achievements, showcase your work, and create a space where you can be contacted. You can also use this platform when approaching clients for freelance projects.

Platforms like GitHub will let you collaborate with other data scientists, manage different projects, and store your work. You can neatly organize your work and showcase it in a detailed way – with documentation for each step and code. Another way is to use Google Drive and Dropbox links and provide the recruiter access to your work which is organized in folders and subfolders. 

Projects to Consider 

The portfolio is a presentation where you compile your best projects. Select 4 to 5 projects that best showcase your skills and also align with the sectors you want to work in. Ensure the projects included are different from one another so you can portray your versatility through them. 

Take projects that solve realistic problems that companies encounter in their daily functioning. Data cleaning, Data visualization, exploratory data analysis, machine learning, and forecasting are some key areas that will help the recruiter gauge the depth of your knowledge and skills in the field. 

Consider designing a program that analyses real-time data, or replicating a game that you like. You can also provide links to blogs you’ve written or videos you’ve posted explaining the nuances of data science and its various aspects. 

Unique Aspect 

Data science is a competitive field, which means, your portfolio needs to stand out for you to land well-paying and growth-oriented jobs in the industry. Your projects must be relevant to the current industry needs for the recruiters to notice and consider you for the job. 

Diverse projects that address real-life problems and creative but practical presentations are key to a strong portfolio. You can also add to your credibility by participating in hackathons and contributing to open-source projects. 

Networking 

Networking with fellow data scientists and acclaimed professionals in the industry can be helpful if you want real-life, practical tips on how to become a data scientist. With the right networking, you can leverage the global prospects in the field and land your dream job. 

• Join alumni groups of your institute to connect with professionals employed across sectors in different parts of the world. 
• Optimize your LinkedIn account to join online communities and participate and learn from the meaningful insights shared by the members.
• Attend webinars, conferences, industry events, competitions, and hackathons to connect with potential recruiters, collaborators, and other data scientists.
• Join Reddit forums and follow relevant subreddits to get tips and suggestions for jobs, interview preparations, latest articles, and to keep up with the dynamic changes in the industry. 

Career Paths After Studying Data Science

With an increase in vacancies in Artificial Intelligence (AI) and Machine Learning (ML), the job market in India is expected to grow further in the coming years. The industry in 2025, as the experts predict, is all set to hire more freshers with specialized skills and accommodate them into the larger projects that are just about starting. 

As a new professional, you can approach the industry right after finishing your graduation. All you need is a solid portfolio to apply for suitable positions and reach out to recruiters. Although the salary structure varies in these jobs, depending on the company, location, skills, and daily responsibilities, the average yearly pay for a fresher varies between ₹5,00,000 and ₹8,00,000. 

Here are some of the most sought-after entry-level jobs that you can apply for – 

Junior Data Scientist – With an average yearly salary between ₹4,00,000 and ₹7,00,000, junior data scientists are required to collect, clean, and organize large sets of data, analyze patterns and trends, create predictive models, and test new algorithms. 

Data Analyst – Data Analysts are responsible for collecting, organizing, and analyzing data to identify patterns and understand trends. They present the data in readable formats – charts, graphs, etcetera – and prepare detailed reports about their findings. The entry-level salary in data analysis varies between ₹4,00,000 and ₹5,00,000 per annum. 

Business Analyst – These professionals use available data to identify problems and help businesses make informed, data-driven choices that result in positive outcomes. With proficiency in statistical concepts, programming languages, and AI and ML, you can work in finance, retail, healthcare, e-commerce, and education. The average pay in entry-level positions is around ₹3,50,000 – ₹5,00,000 depending on various specifications. 

Data Engineer – With an initial salary of about ₹4,00,000-₹8,00,000 Per Annum, Data engineers are hired across industries to work closely with other data science professionals like data analysts and data scientists. Their primary role is to build databases, pipelines, and other related infrastructure that helps in efficiently storing and processing data. 

Machine Learning Engineer – A Junior Machine Learning Engineer can, on average, make around ₹6,00,000 – ₹8,00,000 Per Annum. They are skilled in programming languages, AI and ML, and data structures. Employed in multiple industries, these professionals develop algorithms and models that analyze large data sets to predict trends in consumer behavior, flag cyber-security threats, and improve online decision-making for businesses and institutions. 

Tips for Success

If you are reading this and want to know how to become a data scientist After 12th with a successful and rewarding career, we have some tips for you. The field is rapidly evolving, and dynamically changing, and requires you to keep up with every new trend, tool, and software. 

Even though it might seem challenging, with the right approach, you can effortlessly navigate the industry and make your way to the top – 

• Start with building a strong base in data science. Understand the core concepts – programming and mathematical – and take specialized courses to master the technical skills. 
• Learning is a constant process in data science. Keep up with the latest technology and tools, be aware of what’s happening in the industry, and train in relevant niches that you are interested in. 
• Practice your skills – work on personal projects, take part in contests, and apply for internships.
• Leverage your professional network for guidance, mentorship, and collaboration. Networking will let you match pace with the dynamic changes and better understand the nuances of how the industry functions. 

Conclusion

Understanding how to become a data scientist After 12th and creating a career route map will open up new opportunities for you. It is a fast-growing industry with dynamic changes that require professionals to keep up with the latest trends, multitask, and be experts in spotting trends. 

You can study data science, statistics, computer science, or a related course to kickstart your career. Ensure you have the necessary programming, mathematical, data analysis, and management skills and build a strong portfolio that speaks on your behalf. 

Networking, participating in contests, and constantly upskilling and practicing your skills will let you effortlessly navigate the industry and make your journey memorable. Stay in touch with the basics of your discipline and make good professional relations along the way to grab the advancements in your career through lucrative jobs like data analyst, data architect, data engineer, and junior data scientist. 

Check out the Data Science courses offered by AAFT for access to industry-standard resources and materials and have a smooth transition into the professional realm.