Making School Students Data Science-Ready

In the backdrop of the National Education Policy (2020), CBSE has announced that for the 2021-2022 session, it will introduce coding as a topic for students in classes 6th to 8th and data science for students in 8th to 12th. It has collaborated with tech giant Microsoft for this endeavour. 

Why such a move?

The role of data in today’s world is undeniable. Billions of data get generated every day from various sectors. The need for skilled professionals who can analyse this data and predict future outcomes and develop algorithms and models to solve big and complex problems is already on a high. This trend will only continue in the future. 

It has become critical to prepare the future generation for this rapid technological growth and align them with the necessary skills and analytical capabilities to prepare for the future job market.

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Now that CBSE has come out with this plan, the other boards in India should also take notice and prepare the students to get introduced to these in-demand necessary skills. How can this be done?

Focus on building strong analytical and critical thinking skills

A big part of developing data science skills involves thinking critically and analysing how to solve a problem. In their formative years, if students can hone this skill, it will help them immensely in their professional lives. Such skills can be developed by using puzzles, quizzes, games, and simple business case studies where students can be asked to come up with solutions. This will help them dive deep, use their analytical minds, and develop different kinds of solutions. Doing such mental exercises will only polish their critical thinking capabilities – which is key to a career in analytics.

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Keep it interactive

Though teachers may feel the urge to dive into the complex concepts from the get-go, they have to consider the age group of the students and plan the modules accordingly. One should start with the basics of the subjects, make students understand the core concepts behind algorithms and modelling techniques, and then gradually transition into complex concepts. This will make sure that students’ foundation is solid and enable them to tackle difficult problems in the future. Using interactive techniques like graphs, infographics, charts, tables, and citing simple real-world problems will also help build students’ interest levels.

Give students a flavour of new languages

Though schools already have a computer science curriculum, it is important to introduce languages like R and Python, widely used in data science. The programming sessions should be highly interactive where students are given real-world case studies and shown how to implement these tools to make sense of the data sets and derive logical conclusions from them. 

Tie up with universities and the industry

There is no point in just teaching students data science and coding in schools if the real-world implications of these methodologies are not shown. Schools need to team up with leading universities already teaching such courses and data science and AI firms who can give school students a picture of how the things they are learning in classrooms work in the real world. Frequent seminars with data science practitioners can also be conducted in schools to understand the space better.

Organise internships

Though high school internships are not that popular in India, times are changing. After collaborating with universities and industry, a logical step would be to provide internship opportunities to students in these organisations to work on projects and get hands-on experience. This is a crucial step as it will help build the confidence of students and give them the right mix of understanding of business acumen, programming skills, and mathematical knowledge that is needed as a data scientist.

Faculty is crucial

The guidance a student receives in the early stages of life can profoundly impact his career path. Suppose a school is starting a data science course. In that case, ideally, it should not just onboard a data scientist as faculty but bring in a data scientist who has teaching experience along with domain knowledge. It is crucial to understand that working in the data science field and teaching data science to young students are two different things. A data scientist will be working in a particular sector or company that requires a particular skillset and might not have complete in-depth knowledge of all the subdomains of the field. But a teacher with experience in training students can impart all the necessary basic knowledge to get started in the field.

Clarity is key

In India, career counselling during middle and high school is not very common. Often this makes students make uninformed decisions about their careers which might not be suitable for them. If a school implements a data science and coding curriculum, the teachers have to paint the complete picture of what a data scientist’s career looks like–from the start to advanced levels. Just teaching them concepts will not be enough. Students should know what specialisations and branches exist in the data science and AI space that can be pursued once they reach college or even start working. 

Changes in technology are happening and at a rapid pace. The way India will work will also witness a dramatic change in the future. It is our duty to gear up the future generations for this. And there is no time better than now to start. 

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Sreejani Bhattacharyya
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good. Reach me at

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