Data rules the world we live in, and in fact, has been dubbed the “oil” of the 21st century. In the past few years, the world has witnessed a steep and continuing upsurge in data. Thanks to the growth of social media, smartphones, and the Internet of Things, the amount of data at our disposal today is beyond imagination. As Alphabet’s Eric Schmidt claims, every 48 hours, we generate the amount of data humanity produced since the dawn of civilization until 15 years ago. So, how then, are we able to make sense of such massive amounts of data?
What is Data Science?
To put in simple terms, Data Science is a combination of mathematics, programming, statistics, data analysis, and machine learning. By combining all these, Data Science uses advanced algorithms and scientific methods to extract information and insights from large datasets – both structured and unstructured. The advent of Big Data and Machine Learning has further fuelled the growth of Data Science. Today, Data Science is being used across all parallels of various industries, including business, healthcare, finance, and education.
Uses of Data Science
The most common use case of Data Science that has crept into your everyday life is a Recommendation Engine. Whenever you’re on Amazon or Netflix, do you see those personalized recommendations saying “Things you may like”? Well, that’s a classic example of Data Science algorithms tracking and understanding user search and buying patterns and then curating customized recommendation lists.
Since data is the omnipresent force ruling our lives now, jobs in this arena are booming like never before. Big Data Engineers, Machine Learning Engineers, and Data Scientists are the top three emerging jobs on LinkedIn. Ever since 2012, the job positions for Data Scientists have increased by over 650%, thereby making Data Science one of the hottest professional fields at present. It’s no surprise that professionals from various career streams are upskilling their knowledge base to make the transition into the emerging field of Data Science.
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Future Scope of Data Science
Before the digital revolution came into being, the data at our disposal was mostly structured and relatively small in size. As a result, traditional BI tools were enough to analyze these small and structured datasets. However, the exponential growth of data in recent years has changed the entire equation. How so?
Contrary to the traditional datasets (that were mostly structured), the data generated today (from different sources like social media, financial transactions, and logs, multimedia files, online portals, etc.) is mostly semi-structured or unstructured. At present, more than 80% of the world’s data is unstructured.
With each passing year, the data will only continue to increase and add to the already massive pile of data. It is not possible for traditional BI tools to analyze such a vast volume of unstructured datasets – they demand more advanced and intelligent analytical tools for storing, processing, and analyzing data. This is where Data Science has helped make a difference.
As more and more organizations are opening up to Big Data, AI, and ML, the demand for skilled Data Science professionals is ever increasing. In fact, the Harvard Business Review even hailed the job of a Data Scientist to be the Sexiest Job of the 21st century.
Thanks to Data Science, new and exciting possibilities are opening up, continually changing the way we see the world around us. Data Science’s contribution to changing human lives for the better has been immense.
For instance, when you connect your smartphone to smart devices and the IoT hub, you can monitor what is happening in and around your house even in your absence. Online shopping has gotten so much easier, thanks to advanced algorithms that can understand the taste and preferences of individual users and create recommendation lists for them. Online financial transactions have never been so safe, courtesy of the Fraud and Risk Detection algorithms of Data Science.
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Not just these, Data Science has also contributed immensely to the healthcare sector. Data Science algorithms and applications can be found in Genomics, Drug Development, Medical Image Analysis, Remote Monitoring, to name a few.
Since Data Science is still an evolving field, there’s much more to expect from it in the future. Let’s look at some of the exciting Data Science trends that may soon become a reality in the upcoming future:
- While the IoT is already a reality that connects smart devices, in the future, we might be looking forward to being a part of an Intelligent Digital Mesh – a connected hub of apps, devices, and people working together in sync.
- Product marketing and customer service will be revolutionized by advanced chatbots, Virtual Reality (VR), and Augmented Reality (AR). We might be looking forward to a time when personalized customer experience will include live simulations, interactive demos, visualization of proposed solutions.
- Blockchain might just go mainstream – it will not only be limited to the finance sector, but blockchain will apply to healthcare, banking, insurance and other industries.
- Automated ML systems and Augmented Analytics together will transform Predictive Analytics and take it to the next level. Predictive Analytics will further help change the face of healthcare.
- The job title of a ‘Data Scientist’ will undergo a massive transformation to include an array of diverse roles. As technology, Data Science, and AI continue to advance, Data Scientists will have to evolve to keep pace with the dynamic learning curve of Data Science.
These are only a handful of possibilities that Data Science will bring into our world in the next few years.
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Why learn Data Science?
If the reasons mentioned above weren’t enough to convince you about the importance of learning Data Science, maybe these four reasons will:
Data is the fuel of the 21st century
According to Simon Quinton, “If Analytics is the Engine, then Data is the Fuel of the 21st century.” Without data, businesses would not be able to uncover useful insights that could help streamline their business. After all, where would all the essential customer information come from, if not for data? Without customer data, it will be impossible to improve customer satisfaction or create personalized recommendation lists.
As we mentioned earlier, the demand for skilled Data Science professionals, including Data Scientists, ML and AI Engineers, is on the rise. However, the supply of skilled professionals in the field is creeping up at a much slower pace. IBM maintains that by 2020, Data Science will take up 28% share of all digital jobs, but unfortunately, the job vacancies remain vacant for as high as 45 days due to the lack of talented applicants. Furthermore, IBM’s The Quant Crunch report states:
“Machine learning, big data, and data science skills are the most challenging to recruit for, and can potentially create the greatest disruption if not filled.”
With so many vacancies in Data Science, now is the time to upskill and take advantage of the golden opportunity!
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A lucrative and high-paying career
Data Science is a highly advanced and exclusive field of study, and it is no doubt that professionals in this field make big money. For instance, according to PayScale, the average salary of a Data Scientist in India is Rs 6,99,928, and the average salary of a Data Analyst is Rs. 4,04, 924. All the job roles in Data Science have pretty much similar salary scale. The best part – since Data Science is still evolving, you will never have a stagnant career. There will be plenty of opportunities to learn, upskill, and earn more money.
Highly flexible with an abundance of positions
Data Science is a versatile field that has found applications in every industry, including healthcare, banking, e-commerce, business, and consultancy services. However, only a handful of individuals possess the requisite skill-set to make it big in Data Science. Also, Data Science job roles often have overlapping skills, which imparts a certain degree of flexibility and agility to Data Science professionals. There are plenty of vacant positions to fill, but not many applicants to fill those positions.
Data Science is not only helping organizations understand their target audience, markets, and risks associated with business, but it is also helping them get close to the customer – all with the help of data. The promising field also puts forth great career opportunities for aspirants. Data Science is a less-saturated, high-paying, and emerging field guarantee constant growth and development to professionals who commit to it.
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Are data science interviews hard?
Most data scientist jobs would necessitate a fundamental understanding of at least one programming language, the most common of which are Python and R. Some interviewers, unlike most SQL interviews, will ask you to execute your Python/R code. Data scientists are in charge of releasing production code, such as data pipelines and machine learning models, at many firms. For initiatives like these, strong programming abilities are required. To ace a data science interview, you'll need to know a lot about arithmetic, statistics, programming languages, business intelligence fundamentals, and, of course, machine learning techniques. The interview is moderately challenging. Nevertheless, the amount of difficulty is dependent on your preparation.
Is data science a growing industry?
Organizations are attempting to develop a competent personnel pool capable of providing technical competence and allowing them to move quicker in a competitive climate. Organizations of all kinds and sectors, large and small, are relying on technology to improve their productivity. Data scientists are the backbone of today's businesses, helping them to utilize data and achieve their strategic objectives. With the global expansion of data science, there are numerous employment possibilities accessible across sectors, resulting in a high need for competent individuals in this field.
Is it important to be good at mathematics for learning data science?
While calculus is required for many aspects of data science, you may not need to study as much as you think. For most data scientists, understanding calculus principles and how those principles may impact your models is all that matters. If you're performing data science, your computer will use linear algebra to efficiently execute many of the needed computations. You won't have much fun as a data scientist or data analyst if you're scared of arithmetic or refuse to look at an equation. Math, on the other hand, should not prevent you from becoming a professional data scientist if you have studied high school math and are ready to devote some time to improving your acquaintance with probability and statistics as well as learning the ideas behind calculus and linear algebra.