Data Driven Decisions in Today's Digital Age

In today's digital age, data is becoming increasingly important across all industries. Businesses, governments, and organizations of all kinds, including finance, healthcare, retail, and technology, are collecting, and analyzing large amounts of data to make informed decisions, improve operations, give personalized information to consumers, and gain a competitive advantage.

As technology continues to advance and the world becomes more data-driven, the ability to understand, interpret, and make sense of data will be valuable and the demand for professionals with the skills and knowledge to work with data will be on rise.

According to the Bureau of Labor Statistics, the employment of data analysts is projected to grow 11% from 2019 to 2029, much faster than the average for all occupations.

In the Indian context, according to a report by Nasscom, the Indian IT industry is expected to generate 2.5-3 million jobs in the data science and analytics field by 2025. Additionally, the Indian government is also investing in data-driven initiatives, such as the Digital India program, which is expected to further boost the demand for data scientists and data analysts in the country.

Increasing Popularity of Data Science Degree Programs in India


As a result, data science degree courses have been gaining popularity among students in India. Over the last 5 years, as per Google Trends data, the number of searches for data science degree keyword has been on a rise with June and July of year 2022 reaching the peak and 2023 admission season is projected to cross 2022 search trends.

To meet this increased demand for the data science programs and accommodate an increasing number of students enrolling in such programs, many educational institutions government and private universities such as the Himalayan School of Science and Technology at Swami Rama Himalayan University are responding by offering data science programs.

A typical B.Sc. Hons Data Science Program is three years (6 semesters) long and potential candidates are required to have an aggregate of minimum 50% marks in 12th class in any discipline with Mathematics as an essential subject.

Internships and placements are also among the key differentiators between a 200 hours yoga training and a bachelor’s in yoga science program. For Example: The bachelor’s degree in yoga from Himalayan School of Yoga Sciences brings in therapeutical aspect to learning as students get to do internship with actual OPDs at in-campus hospital.

In summary, a yoga teacher training program will provide you with the skills and knowledge to teach yoga, while a bachelor's degree in yoga will provide a more comprehensive understanding of the practice and can open a broader range of career opportunities giving you an edge in the job market. It ultimately depends on your goals and what you want to achieve in your practice.

Career Opportunities available to students after a B.Sc. (Hons.) in Data Science Program

Graduates of a B.Sc. (Hons.) in Data Science program can pursue a wide range of career opportunities in a variety of industries. Some of the key industries that are currently hiring data scientists and data analysts in India include e-commerce, finance, healthcare, and technology. Companies such as Amazon, Flipkart, Google, IBM, and Microsoft all have a significant presence in India and regularly hire data professionals.

Some popular job roles and titles available to B.Sc. (Hons.) in Data Science graduates are:

  • Data Analyst: This role involves collecting, cleaning, and analyzing large sets of data to identify patterns and trends.

  • Data Engineer: This role involves designing, building, and maintaining the infrastructure and systems required to store, process, and analyze large sets of data.

  • Business Intelligence Analyst: This role involves using data and analytics to support decision-making and strategy development within an organization.

  • Data Scientist: This role involves using advanced analytical methods, such as machine learning and statistical modeling, to extract insights and knowledge from data.

  • Machine Learning Engineer: This role involves designing, developing, and deploying machine learning models for a variety of applications.

  • Big Data Engineer: This role involves working with big data tools and technologies to store, process, and analyze large sets of data.

  • Research Analyst: This role involves using data and analytics to conduct research and provide insights in areas such as market research, social science, and public policy.

These are a few popular job roles for the students today but the job market is constantly evolving and technological advancements such as artificial intelligence and machine based application and models are automating and changing the job marketing and opportunities at a fast pace. Hence, it is crucial to understand what the future demand for data science graduates will look like and what types of job role will be available to them.

Role of AI and ML and Expected Future Roles of Data Science Graduates

Artificial intelligence (AI) and machine learning (ML) are becoming increasingly prevalent in the field of data analysis, and it is likely that these technologies will lead to some changes in the role of data analysts. However, it is unlikely that they will eliminate the demand for data analysts, data scientist and data engineers in the future.

AI and ML can automate certain tasks that data analysts currently perform, such as data cleaning and preparation, and can also be used to make predictions and identify patterns in data. This can lead to increased efficiency and accuracy, but it does not necessarily mean that data analysts will no longer be needed.

Data analysts will still be needed to interpret the results and insights generated by AI and ML models, and to communicate them to non-technical stakeholders in an understandable way. They will also be needed to verify the accuracy of the models and ensure that they are being used ethically and responsibly.

Furthermore, AI and ML are not yet able to completely replace the human intuition, creativity, and critical thinking that is often required in data analysis. They also require large amount of data to be trained, which data analysts are responsible for collecting, cleaning, and preparing.

In short, while AI and ML may automate some tasks that data analysts currently perform, they are unlikely to fully eliminate the demand for data analysts and data engineers. Instead, they are likely to change the role of data analysts, making them more focused on interpreting and communicating the results generated by AI and ML models.


Overall, based on the above stats and data, making a career in data science is an excellent choice for those interested in working at the forefront of technology and innovation. But the field of data analysis is constantly evolving, which means that there are always new tools, technologies, and techniques to learn. This keeps the job interesting and provides opportunities for career advancement.

In summary, pursuing a degree in data science in 2023 is a good idea because of the high demand, high salaries, versatility of the role, and constant evolution of the field.