Does data science have a future in India? Is data science a demanding career? Where can you go ahead in life as a data scientist? All questions answered!
By 2025, it’s estimated that 463 exabytes of data will be created each day globally – that’s the equivalent of 212,765,957 DVDs per day! This statement comes from the World Economic Forum.
It’s known that the data around us is piling up at a rapid pace. And there’s always, therefore, a need for studying and analysing the raw data generated from various sources. A data professional is someone who studies chunks of data and analyses them to decipher information. The information is used by various sectors and processes run around the world. Basically, data is studied to convert it into vital information that in turn creates more data. This means that we are all caught up in a data circle! Did you think of that?
The scientific study of data using mathematical and statistical models, machine learning tools, and artificial intelligence is called data science. Simply put, any amount of data that is put to use by understanding its importance and areas where it can be implemented is used by data scientists all over the world. Data science is a vast field and includes concepts like data mining, data analysis, data visualization, and data wrangling.
Data analytics is just one part of data science. It involves the process of collecting, analyzing, and interpreting data. A data analyst should know how to understand data patterns and have the basic knowledge of statistics and data models. A lot of data visualization happens in a typical data analysis process.
Data wrangling is also one part of data analysis, wherein an analyst needs to clean and segregate data based on its complexity levels. Data wrangling is a time-consuming process and one of the trickiest parts of a data analyst’s job.
Data science is therefore a multidisciplinary field, which involves data engineering, machine learning, data analysis, data mining, and data visualization. This means the process of collecting, cleaning, analysing and transforming large chunks of data is called data science.
As discussed above, data analytics is just one part of data science. It is thus a niche area that involves the collection and analysis of data based on complexity levels. It focuses more on analysing raw data to draw insights using different algorithms and techniques.
You ask, Is data science a good career in India?
Currently, data science and artificial intelligence have slowly made their way into sectors like travel, healthcare, education, stock market, and e-commerce.
In India, if you have a job experience of working as a data scientist, you can move onto other roles like
Top recruiters that hire data scientists
The companies where you can work as data scientists are -
Is a certification in data science useful? If yes, then which certification in data science is the best?
Data science certifications can give you an edge over others, if you are looking forward to working with some top-notch companies. Below are the popular data science certification provider lists:
With a certificate in data science, you can get jobs as a principal data scientist, junior or senior data analyst, AI and machine learning engineer, and data manager.
You must check out Crampete’s data science course syllabus to decide for yourself if you want to take their course.
There are two categories of people who can become a data scientist -
1. IT students and professionals - these categories of students have either studied computer science or an IT course and have a bachelors or masters in a related field. Similarly, IT professionals who want to grow professionally and move up the career ladder take up data science courses to upgrade themselves.
2. Non-IT students and professionals - these categories of students are from completely different backgrounds and they have interest in working in fields like artificial intelligence, machine learning, big data, and data analysis. These people tend to choose data science courses because they want to switch careers for both personal and professional reasons.
Which category do you think you fall in? Let us know!
There are a few qualities that every person willing to be a data scientist must possess. These are -
If you want to be known as a data scientist, you must possess technical expertise in the following areas -
Check out the most important tools in data science that employers expect you to know!
There is a huge scope for the application of data science in almost any field in India, especially in 2023 and the years to come, be it banking, cyber security, financial institutions, education and healthcare sectors, big data organizations or even small-scale enterprises.
Various Indian sectors have embraced data science for their day-to-day businesses. It has become an integral part of all operational aspects and is also useful in understanding customer behavior.
Any form of audio, video, images, and graphs are considered to be data. Therefore, data plays a vital role in helping businesses become stronger and scalable. Data science is used currently in the following Indian sectors -
Banking and finance - Understanding transaction patterns has been a key metric to meet customer satisfaction, currently. It helps in forging meaningful customer relationships, and providing appropriate solutions to customer problems.
Banking sectors have gradually accepted the importance of using up-to-date technology for mitigating fraud risks, understanding credit and debit transaction patterns, internet banking data, and the mode of communication preferred by their clients.
Healthcare - Healthcare industry adopted data science long back because of the growing chunks of data generated on an everyday basis. Medical and billing history and clinical systems are all nowadays monitored using the latest technology.
Data science plays an important role in understanding the patient history or patent health. It can be used by experts to prevent chronic diseases and also provide expert advice to patients based on the data patterns observed.
During the Covid19 pandemic, thousands of people around the world switched over to using telemedicine. Telemedicine is a technological, data-oriented platform that helped several people make doctor appointments and consultations online during the lockdown. Therefore, healthcare sectors are already in an advanced stage of implementing data-backed science and tools for a smooth performance.
E-commerce and retail - E-commerce is one of the most advanced sectors when it comes to using data analytics and predictive analysis for customer acquisition and retention. E-commerce giants like Amazon and Flipkart use data-backed methods to run successful business campaigns. Machine learning is also increasingly used by these sectors to re-target ads and understand customer preferences.
The history of data science
The introduction of data science dates back to the 1960s, with its first mention in a book called the Concise Survey of Computer Methods in 1974. Shortly after that in 1977, the IASC - the International Association for Statistical Computing - was established. Since then, the discovery of database management and data science has come a long way. It was in 2001 that Software-as-a-Service [SaaS] was formed, and that was the beginning of using cloud-based tools and applications for businesses.
Hadoop became famous in 2006, and it was in 2008 that ‘data scientist’ became a buzzword. In the mid-2000s, Google voice search and speech recognition became very popular. Also, IBM shared a report stating that 90% of data in the world was generated just within 2 years of time.
Along with the journey of data science and data-supported technology began the journey of artificial intelligence. AI was slowly adopted and integrated into many industries and operations worldwide.
When one talks about the past, it’s quite usual that the future comes into play. One may wonder ‘does data science have a great future ahead?’
The answer is Yes, definitely. Data science, machine learning, and artificial intelligence have massive power to bring disruptive changes in the society and transform the world around us. Some of the areas where we can see the impact of data science and artificial intelligence in the near future are as follows -
Healthcare - Data-backed research in the healthcare sector can ease middle- and lower-income group peoples’ lives. with the help of data science and artificial intelligence, healthcare can be made more accessible and affordable to a vast section of the Indian population living below and at the edge of the poverty line.
Data scientists can help in providing authentic and scientific information to doctors and healthcare professionals. Some of the areas where data science and AI can be useful include - early detection and diagnosis, treatment, end of life care, etc. For example, early cancer detection is an area wherein data science and artificial intelligence have tremendous potential.
Agriculture - Agriculture is one of the most important sectors running the Indian economy. It provides food security to 1.3 billion people of India. AI and data science can help in solving many agricultural problems, such as unpredictable weather conditions, monitoring crop health and suggesting measures against common crop diseases.
In fact, currently there is a technology to suggest which crops could be grown depending on weather conditions. Farmers can also rely on data science to receive support in terms of training and skill development in areas that they are unaware of.
Transportation and logistics - AI and data science can be used in predicting traffic congestion and also avoiding traffic related deaths. Community-based parking, intelligent transportation systems, and travel and route optimization methods are other possible areas of exploration.
Education - There are several existing problems in the education sector that can be solved by the application of data science and artificial intelligence based tools and methods. For instance, the concept of online learning has been changing the education sphere tremendously.
It has made it possible for people from rural backgrounds to have access to education without the need for basic school infrastructure. AI has the potential to assist teachers in understanding the requirements of every child. This helps in developing effective teaching and learning materials based on the learning style of children.
Urban infrastructure - AI and data science are being used currently in smart city projects across India. Under this project, public safety, urban planning, citizen delivery services, and cyber attacks are areas of prime concern. Cyber crimes are one of the most common crimes affecting peoples’ lives. With the help of data science, it’s possible to track down such criminals.
Energy - Electric vehicles are slowly making their way in many households in India. With increasing awareness on renewable energy sources, there is a demand for storage and distribution of renewable energy, which is possible via artificial intelligence.
Environment - AI and data science are used in analyzing the air quality around us. Websites like India Air Quality can tell you about the quality of air that you breathe every day.
As per PayScale reports, the average salary of data scientists in India is around INR 8 LPA. This also includes bonus and profit sharing. An entry-level data scientist’s salary ranges somewhere around 5 to 6 LPA, and an early-career data scientist [1 to 4 years of experience] can earn close to 7 LPA. A mid-career data scientist can expect to earn around 1.38 LPA, and with over 5 years of experience, a data scientist can earn close to 1.73 LPA.
Skills that can influence a data scientist’s salary are artificial intelligence, machine learning, apache spark, deep learning, etc.
Popular employers hiring data scientists
Amazon, Mu Sigma, and Accenture are the well-known employers who hire a large number of data scientists every year across the world.
Know the maths - you need to be good at maths and stats if you are an aspiring data scientist. Areas like exploratory data analysis, machine learning, statistical analysis, and regression analysis are included in the topics that you need to focus on if you want to excel in data science.
Choose a specialization - a specialization in other areas of data science like artificial intelligence, machine learning, data analysis, database management, etc. might be useful if you are interested in knowing more about a particular field and want to increase your earning opportunities.
Know the trends - as a data scientist, you must be aware of the basic programming languages like Python and R. Data visualization, data munging and data reporting are other areas where you need to focus on if you want to be good at your work. Keen interest in big data also helps if you want to stay updated about the field.
Get a degree or certificate - There are several long- and short-term courses on data science. Websites like greatlearning and upgrad are great places to start learning about data science.
Start working - The best way to get hands-on experience in data science projects is to start working with an organization that deals with analytics, machine learning, or artificial intelligence. Internships are a great way to begin your career if you feel you need some more time to land up with a full-time employment.
According to a NITI Aayog report, the global contribution of artificial intelligence to the economy would be approx. 1 trillion by 2035. The usage of this disruptive technology is predicted to transform lives and industries soon enough. Not just that, a study by EY and NASSCOM indicates that by 2022, around 46% of the workforce will be involved in completely new jobs that do not exist today.
Therefore, as we can see, data science and artificial intelligence have been increasingly dominating the lives of people. Particularly, data-based research has numerous opportunities for various sectors. Industries can capitalize on this opportunity and change their business models for the benefit of the economy at large.
Are you interested in data science and the related fields? Our advisors can help you get started.
Websites to follow
Trends to follow
Also check out the books that every aspiring data scientist should read.
According to Glassdoor reports, data science jobs are the most popular on their platform. Also, it’s been found that the jobs remain available on the platform for close to 45 days. This shows that there is still a huge skill gap in the market.
It’s been also found that in the last two decades, almost 90% of the world's data was generated. This means that in the next few years, the amount of data would double up in number.
All these indicate how popular this field has become and is yet to become one of the most aspiring tech-oriented fields in the near future. So to answer the question ‘Is data science a good career in 2023?’, I think the straightforward answer is ‘Yes, it is’, and it will continue to remain a good career option for a long time.
It’s possible to learn the fundamentals of data science within 6 months. But the kind of job that you get after completing the course depends totally on your skills and what you learn in those 6 months. Crampete’s data science course can teach you the basics of Python, statistics, exploratory data analysis, machine learning, and regression and anova within 6 months.
There are so many providers who can help you get certified as a data scientist. Websites like greatlearning, upgrad, crampete, and simplilearn are some of the best ones to start with a data science course. However, identifying one of them as the best for you is totally subjective depending on what your requirements are.
A non-IT person might find it difficult to understand the basics of data science and programming languages. Also, for an IT person, it could be hard to learn data science completely on their own. If you're seriously looking forward to making a career in data science, then a degree or certification helps. But to start with, you can always look for some MOOCs and hackathons and online lectures to get an idea about the subject.
There are two answers - yes and no. Elon Musk, the CEO of Tesla Motors, says that a degree in data science doesn’t add any value; what’s really needed is the desire to learn coding and master it.
This statement may not hold much importance to someone who is from a different country and is not aware of what Elon Musk does. This also means that a person who strongly feels that a certification in data science can add some value to his or her profile must go for it to gain more confidence and add more credibility to the profile.
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