Data Science is defined as ‘a multi-functional and interdisciplinary field which deals with studying or retrieving enormous amounts of structured or unstructured data’. The term “Data Science” was used for the first time in1974, although, merely in a theoretical sense.
In the present digital era, our day-to-day activities and preferences are inherently converted into structured figures of ones and zeros to calculate or predict our choices and behavior. Imagine the amount of these figures of the entire global population floating in the oceans of fiber optic cables and buried in the deserts of micro-processors. These zeros and ones are then converted into useful data for end number of purposes ranging from analytics, research and development, statistical integration etc., the list keeps on adding. To cater to these ever-increasing fields of its application and to produce a precise calculation of this data for the concerned purpose is why a ‘Data Scientist’ has to come into the picture.
Why does Data Science play vital role in the current market?
Have you ever wondered why a doctor asks you about your medical history, as in, information whether you’ve been exposed to any disease, underwent any surgery or the type of medication you were on? The doctor collects all this data to analyze your healthcare needs and to provide you with an efficient and adequate solution. Similarly, data science deals with collecting the data available, and which is relevant to the purpose and provides a user with precise solutions.
Now the obvious question is that, what could possibly be the difference between the procedures in data science and those included in statistical analysis? The answer to this question is evidently present in the question itself. Statistical analysis is used when the data which is to be studied is less in volume while, data science enters into the picture wherein the data to be studied is unfathomably enormous. This factor enables data analytics organizations to perform efficiently and provide precise results.
The next intriguing query that arises is where can one apply data science and its usage? An appropriate answer to this question can only be determined if and when the term ‘market’ is no longer known to people. The reason being, data science can be applied to almost every other sector of the market, ranging from small scale retail to global healthcare applications. Narrowing it down to an individual level, every other social media app or website uses data science for creating a more productive performance based on extensive analysis of user preferences and digital social behavior. Considering the application of data science in business models, an intensified learning and proficient usage of statistics provides enhanced decision making and improvised hiring quality. Data science collects and studies previous data and calculatedly predicts risk factors and instances that can prove both fundamental and detrimental.
These essential nuances ascertain the pivotal nature of data science in the apocalypse-proof, dynamic and metamorphosing global market.
What does it take to be an industry grade Data Scientist?
Although there is a shooting demand for data scientists, this position demands an extensive acquisition of degrees and relevant experience and expertise. Following are the detailed prerequisites to land a data scientist career:
- A data scientist should hold a PhD or a Master’s degree either in Mathematics and Statistics, Computer Science or Engineering.
- Must have comprehensive knowledge of various analytical tools such as SAS and/or R.
- Along with Java, C/C++ or Perl, a data scientist should also have expertise in Python Coding.
- A data science candidate must be able to execute composite SQL queries since it is essential to handle large components of data science.
- Be it in any form, a data scientist must be efficient enough to work with unstructured data.
- One should possess logical curiosity to decipher bulk data in every creative method.
- A data scientist needs an accurate understanding of the industry they’re placed in and comprehend the business errors that the organization is attempting to find a solution for.
- Certification courses such as CAP, CCPA and CSPA shall be an additional advantage to a data scientist’s profile.
The blown-up roofs of surging demands in the data scientist career profile will soon be an unmatched one due to its perks that include enhanced career growth, ensured salary raise and promising profile appraisal. This surging demand, when met with adequate provision of quality education and training, will most certainly kick start an advancement in every corner of the tech market.