What is the Future Scope of Data Science? Demand, Jobs & Skills

The world is shaping according to the latest trends and one such trend in the modern world is Data Scientists. It is one of the most sought-after career options for today’s youth. From multinational companies to small startups, every organization requires a Data Scientist for proper usage of the huge amount of data it generates and stores. There is a broad scope of Data Science in the present and future scenarios.

Most people are unaware of Data Science as a career option and even find it a bit mystifying. Here, one can unravel the mysteries and common questions, such as “is data science tough?” or “does Data Science have a scope?” or even “is Data Science a good career in India?”

The answers to these questions are simple and lie in one’s daily activity.

Introduction

The 21st century is ruled by data and in reality, it is turning out to be the ‘blood’ of this technology-driven era. The upsurge of data on a global platform foretells that it is going to dictate the world for upcoming years, all credit goes to IoT, digit media platforms, and smartphones.

While talking about Data Science future scopes, Eric Schmidt states, “the whole human civilization is producing such a massive amount of data in just 48 hours that it is compared with the data since the dawn of civilization until 15 years before”.

One of the most prevalent usages of Data Science is the recommendation engine. Most people may have noticed that shopping sites or an online series website often recommend series or product according to one’s past choices.

That is exactly what data scientists do. With the help of an algorithm and consumer behaviour, they manage to build customized recommendation charts. In today’s scenario, the huge amount of data is giving birth to great future scope for data analytics.

In case you need an explanation for just a specific topic, check out what all we have covered for you.

What is Data Science?

The word ‘data science’ was coined in 2008 when industries understood the requirement for data consultants who are experienced in analyzing and organizing a huge proportion of data.

The exact definition of Data Science is the potential to make data understandable and processable to heave quality out of it.

Data Scientists are experts in identifying related questions, extracting information from data sources, stacking the information, converting outcome into solutions, and interacting with the findings to elevate the business.

What is the role of a Data Scientist?

Data science has been called “the sexiest job of the 21st Century” by Harvard Business Review. The Scope of Data science is getting more popular in recent times.

Data scientists are professionals who can simplify big data through coding and algorithms and turn it into a problem-solving solution for the business. They normally have a great base in computer science, statistics, mathematics, modelling, analytics blended with an overpowering business sense.

Small startups are generating a massive amount of data every day, thus resulting in increased hiring. The pay scale of data scientists is well-groomed because of the never-ending demand. They generally work with the developers to deliver value to the end consumers.

The Function of Big Data

The function of a data scientist is getting more important for a traditional organization because Big Data is constantly transforming business strategies and marketing skills and data scientists are the core of that change. Big data generation leads to the huge scope of data analytics and DevOps.

Everything is happening because of the wide range of software, from human resources and marketing to R&D and financial forecasts. It’s never so easy to manage and interpret all the data extracted from these services.

Important Data Science Skills

Data scientists are experts in the use of software, like Java, Hadoop, Python, and Pig. Their chores include business exploration, structuring analytics, and data management. The main reason for Data Science’s future getting bright is its high-end demand because of digitalization.

Data scientists are the game changer for any organization. They can critically examine big data and get the solution for the enhancement process easily. The experts help in building marketing strategies as well as provide outstanding recommendations on the product front. Data science works as the building block of any organization.

Who can be a Data Scientist?

There is a broad scope of Data Science in India with the advancement in technology. Data science has emerged as one of the hottest career scopes. The young generation is witnessing a steep inclination towards data analytics, data science, and stream related to computer science.

There is no specific degree yet for anyone to graduate as a data scientist, though offers an extensive Data Science course that could help you be an expert in the subject, along with an industry-relevant certification. Most of the popular data analytics state that with time, a person gets a good grip on data science. It is a field where experience counts more than a degree.

Here are some fundamental requirements for becoming a data scientist:

  • Having an undergraduate degree in computer science or an elated stream
  • Must know how to run programs and software, such as Python, Pig, Hadoop, SQL, and more
  • Should possess great business skills
  • One needs to have a great understanding of algorithms or mathematics
  • The person should possess leadership qualities so that in future they can lead the organization on the way of success

Anyone can have a great Data Science future if he/she has the quality to understand millions and millions of data and can analyze it to make a business successful. The role of the Data Scientists is vital because they need to find out both the problem and the solution.

Skills Required to be a Data Scientist

Skills play an important role when it comes to data science. Most of the recruiters need candidates who have experience in tackling real-life problems regarding data analysis.

For having a better scope of data analytics, degrees do not matter but both experience and skill matters the most.

It is not that freshers have a low chance of getting hired, though top multinational companies prefer recruiting applicants who are experienced and skilful at the same time.

There is no ‘idiot’s handbook’ that can turn a person into a successful data scientist. Students need to devote their time and effort to get a good hold on this subject.

Here are five skills for a data scientist.

Multivariable linear algebra and calculus

Majority of the data science model, machine learning is developed with various variables. A deep understanding of multivariable calculus is proven to be a boon while creating a machine learning model. Here are a few topics in mathematics that will be helpful in acquiring data science skills.

  • Cost function
  • Vector and scalar
  • Tensor and Matrix functions
  • Finding values of a function (maximum and minimum)
  • Stepwise function and Rectified Linear Unit Function
  • Gradients and Derivatives

Wrangling of data

Raw data is not ready for modelling purposes. So the scientists need to prepare the data for further examining i.e., transforming and mapping the data from raw to cooked form. For wrangling the data, one needs to acquire and combine them with the related area, and then cleanse it. Just by learning this skill, one can have a great data science future scope.

What is the importance of data wrangling in data science, you ask?

  • It helps data scientists concentrate more on the analysis process than the cleansing process
  • This solution is beneficial in revealing good quality data from multiple sources
  • It curtails extraction time, response time, and processing time
  • This leads to the solution that is data-driven as well as supported by accurate data or information

Cloud computing

The practice of data science comprises cloud computing. Data scientists need the products and services of computing to process data. The daily chores of data scientists include visualization and examination of data that is found in the cloud storage.

Cloud computing and data science go hand-in-hand because it enables data scientists to avail platforms, like Google Cloud, AWS, and Azure. This is helpful in providing access to operating tools, Databases, Programming languages and frameworks.

Basic understanding of Microsoft Excel

Microsoft Excel has become one of the basic requirements for any job related to the back and front office. It is the core platform for a defined data algorithm.

Excel proves to be the best editor in 2-dimensional data and also enables a live contact to an ongoing excel sheet in Python. It also makes the manipulation of data relatively straightforward than any other platform.

So, having a good understanding of Microsoft Excel can recoup someone’s data science future without much effort.

DevOps

Half of the population believes that DevOps has no relevance to data science and a person skilled in it can never switch to data science likewise. This is a myth because DevOps board nearly works with the developers for managing the cycle of applications.

DevOps team provides highly accessible clumps of Apache Spark, Apache Hadoop, Apache Airflow, and Apache Kafka for handling the collection and transformation of information.

Health care sector

There is a huge requirement of data scientists in the healthcare sector because they create a lot of data on a daily basis. Tackling a massive amount of data is not possible by any unprofessional candidate.

Hospitals need to keep a record of patients’ medical history, bills, staff personal history, and much other information. Data scientists are getting hired in the medical sector to enhance the quality and safety of the data.

Transport Sector

The transport sector requires a data scientist to analyze the data collected through passenger counting systems, asset management, location system, fare collecting, and ticketing.

E-commerce

The e-commerce industry is booming just because of data scientists who analyze the data and create customized recommendation lists for providing great results to end-users.

Conclusion

The Data Science sector witnessed a massive hike of 650% since 2012. As organizations are turning towards ML, big data, and AI, the market for data scientists is boosting. Data science has made normal lives easier by monitoring things near one’s home or workplace, enhancing the quality of online shopping, enabling safe online fund transactions, and many more.

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