What is Data Science & Advantages and disadvantages of Data Science
With the advent of tech, society has become compact and connected. Information binds us all in one monstrous bubble, making it a truly global village. The central premise to this phenomenon is data. It drives the entire ecosystem and lack of it can have disastrous consequences. In order to understand this spectacle, academia created a separate vertical called Data Science. A subject that has certainly made a mark for itself all over the world. In this article we would look at Data Science a tad more closely and try to decipher it.
What is Data Science?
Before we delve deeper, it is imperative that we begin by understanding what the definition of data science is. Data Science, as the name suggests, is the study of data. It analyses the characteristics; the idiosyncrasies and the way data behave. However, it is not limited to that. It is also about extraction, visualization, managing and storing data. The primary aim of data science is to enable researchers gain insights into massive processes that take place all around us. Instances like consumer behaviour, nuclear fusion, stellar activity, ocean currents spew monstrous amount of information which are abstract to the naked eye if not collated properly and recorded systematically. Data Science uses both structured and unstructured data.
Data Science is a multidisciplinary engagement where an ensemble of specialisations like Maths, Statistical Science and Computer Science play vital roles. The scope of Data Science has grown exponentially in the recent years with the spurt of smartphone usage and ecommerce transforming our lives forever. Big Tech companies are ever reliant of figures to understand gaps and are racing to cover them. Newer technologies like AI, ML and Big Data are also playing a big part in pushing up the demand for data scientists.
The usual step that a data scientist undertakes to generate relevant information are:
The Data Extraction Step
Analysis of the data
Visualisation of the data
Storing of data
As the statistics reveal, the demand of data science is on an upward swing and there are plenty of technologies vying to capture a large part of the market. As the technology and study of data refines, there would be greater emphasis on understanding it more intently. A large part of data analysis is also automated, thanks to state-of-the-art software, which performs complex calculations. Because of this, the demand for quality software developers with innate knowledge of data structures has risen sharply too.
Advantages and Disadvantages of Data Science
While Data Science is a new age science that is witnessing an upward swing, there are disadvantages too. Let us quickly have a look at the advantages and disadvantages presented by Data Science.
There is a popular study which shows that by 2026, data science will attract 11.5 million jobs. This is a humongous number and there is every possibility that this figure will not just be reached but breached too. As the world becomes more entangled with technology, the amount of data generated will increase manifold. Already there has been a multiplying effect due to the arrival of NFC (Near Field Communication) and the IoT (Internet of Things). These will multiply further with the advent of time.
With demand will appear the abundance of positions. Organizations are racing to latch on to the best available talent before the supply line dries up. However, the biggest reason for the abundance is the requisite skill set that a data scientist must master. Not many will have the tenacity or the intelligence to garner expert level abilities in math’s, statistics and computer science together and that is what makes these positions abundant. It is simply difficult to be filled up. It is in stark contrast to those of IT companies who look for software developers skilled in basic or advanced level programming knowledge in a few languages. The supply of data scientists is always on the lower given the constraints whilst the demand is evidently soaring.
This is in continuation of the above point. Because of the inherent obstacles in becoming a proficient data scientist, there is an abundance of positions and the given the trickling supply, the one who eventually become data scientists and fill up those positions are paid a ransom. Glassdoor suggested that Data Scientists are, on an average paid almost $116,000 annually. This makes it an immensely lucrative career option.
Data Science is an amalgamation of three highly dynamic subjects, mathematics, statistics and computer science. All three fields are enormous by itself and requires rigorous training and in depth understanding. Because of this, data science can be used in plenty of fields and disciplines. For a matter of fact, anything and everything that generates data will become a subject of study and data scientists would be interested in it. The bright side to this is that data scientists will be able to become a part of various domains without being domain experts and only on the strength of their number crunching ability.
Makes Data Better
The primary task of a data scientist is to help data look better. Data scientists work with both structured and unstructured data. Organizations expect their data scientists to process and analyze data and filter them to a level where it is easy to understand. Another important task is to improve the data quality. A group of data which helps no ones is a waste of information whereas a group of data which can transform the way of doing things is of immense help. Enriching data and making it better also makes data science a subject to look out for.
A data scientist position is a prestigious position and given the lack of consistent supply, a job to die for. It is also the reason why organizations pay attractive salaries for the same. The knowledge one carries also has its weight in gold and thereby makes the position enviable.
Full of Surprises
There is a misconception that data science is a boring and mundane job. To be honest, any job, done regularly routine will have an amount of monotonicity. However, data science is not just any regular job. It is the combination of number crunching and collation and then formulating processes to generate pure numbers that are readable and easy to comprehend. It is a subject that has full of surprises and an individual eager to learn will find it immensely interesting. One of the many facets of data science is automation of tasks. This requires a knowledge of computer science and programming skills.
Makes World a Smart place
One of the reasons why apps have become smarter and machines more intelligent is because of the ubiquitous data scientist crunching away numbers to improve efficiency. From automobiles to space shuttles, vaccines to medicines attacking cancer cells, the data scientist is making sure that processes are optimised to ensure flawless performances. Industries rely on data science to tweak their business model. It helps them to penetrate newer consumer bases as well as improve consumer interaction. Marketplaces like Amazon, eBay and Alibaba use intuitive systems that makes recommendations to users based on their search results. While this is indeed a form of Artificial Intelligence, the inherent science behind it is Data Science. An interesting ability that data science has been able to quantify is human behaviour. Most product development companies are dependent on consumer behaviour to assess the success of their products. Marketing and sales are modified to suit customer tastes, which in turn keep the billing busy.
It saves lives
Data Science has greatly impacted the health and pharmaceutical sector. Prediction software based on data driven processes allow researchers to predict the next viral outbreak or the seronegative percentage of a populace. It also provides information to vaccine manufacturers and assists them to build vaccines that become the mainstay of humanity.
While there are many advantages of data science, it is, like every other thing, imperfect and does have some sneaking flaws.
While the general definition of data science is the study if data, there is still a large grey area which allows for misapprehensions to creep in. A data scientist is required to crunch data and provide relevant insights but that role changes as per the specialisation of the organisation. There is a school of thought which even terms Data Science as Statistics in a new bottle.
Impossible to Master
Data Science, as mentioned, numerous times, is an amalgamation of maths, statistics and computer science. All the three fields are vast knowledge repositories and impossible to fully master. Hence, Data science is near impossible to understand and know. There are plenty of university courses and fill-gap methods, which claim to provide complete knowledge but given the vastness of the premise, it is impossible.
While data science is all about generating refined data, it is not always the case. There are times when the output is not as expected, and this is a common happening. While data science is an intriguing field, there is enough room for frustrating outputs and failed processes.
The thin line of data privacy
While data is the new fuel, there is a fine line between data evaluation and breach of privacy. Data scientists, at the end of the day, will be dealing with information that consumers provide and generate. This data is then analyzed, and insights gained. This is a potential hazard for every organisation because the possibility of a conscious or an unconscious data leak is high. These are ethical issues which the companies must grapple with. Protection of data is a necessity and even though data science is important for the growth of the organization, safeguarding the customer is a legal responsibility failing which the organization would be severely reprimanded. There is also the stigma of financial and reputation loss attached to breach of privacy.
The cost for studying data science is high and not many would be able to afford it. On the business side, the cost of tools that organizations use is also exorbitantly expensive. That is because of the limited number of competitors currently. This software is proprietary, and every enhancement is paid. This makes the entire endeavor of data science a costly affair.
Inherent Domain Knowledge
As mentioned previously, data science is a combination of three sciences: math’s, Statistics and computer science and without knowledge of either of them, it is not possible to become a successful data scientist. However, it doesn’t stop there. Data science is required across a wide range of industries. Hence, specific domain knowledge is also an added requirement. For example, the automobile industry requires data scientists with a basic knowledge of automobile engineering while the pharma industry would require individuals with some knowledge of drug discovery methodologies. It is this quandary that acts as a spoiler for those who are interested in the field.
Current Data Science tools
There are few data science tools which are assisting data scientists make the world a better place. The following are those which have made a mark for themselves:
MLBASE: This tool has some of the widest range of functionalities and is user friendly too.
Apache Graph: A highly scalable tool, Apache Graph is an iterative graph processing system that provides data scientists with actionable knowledge and detailed insights.
TABLEAU: One of the most popular data science tools that is used globally, TABLEAU is a powerful data visualization tool that has a powerful graphic engine and is extremely interactive.
TENSORFLOW: TENSORFLOW is a Machine Learning tool that is used globally for Machine Learning algorithms, namely Deep Learning. One of the reasons why it is also popular is because it is open-source and is still evolving with constant improvements and additions.
Data Science is a sough after domain and there is not enough supply of quality data scientists. If one has the knack to talk to numbers are comfortable handling unimaginable amount of them, then this is the field to be. There are challenges on the way but given the need of organizations, this opportunity should not be missed.