Data science as a field of career is considered to be one of the best. If you want to earn the big bucks, if you want a dynamic career with plenty of growth opportunities and if you have the knack of solving puzzles- you should definitely aim to be a data scientist.
Data science has become a buzzword over the past couple of years. Everywhere you look you can easily find an article or something related to data science and the benefits it carries. Owing to an increasing value of data as a resource, data scientists have developed a reputation as vital for organizations aiming to utilize data internally or seeking to monetize data.
However, there exists too many confusions associated with the field of data science. Organizations and individuals who are willing to either utilize data science for business or acquire data science skills from a best Data science training course should be able to clearly understand what the field is.
Let us discuss what actually is data science first:
Data science is an umbrella term under which lie many domains and the term itself is very difficult to define. In very simple words it can be said that Data science enables us to transform data into value. And here lies the problem! What is considered valuable is very subjective from organization to organization and from individual to individual. A research institute might find any pattern arising out of a data pool valuable and for a business only patterns that are relevant to business growth more valuable. This is why exactly what a data science does varies greatly from place to place.
However, the main goals of the field of data science are-
- To extract actionable insights out of data pools
- To develop techniques for faster analytics
- Solving complicated problems with data
- Seeking new ways of acquiring data
What exactly a data scientist does to achieve such goals?
A data scientist may use simple statistical analysis for making meaning out of a structured or unstructured data. A data scientist may also be required to develop and train sophisticated ML models in order to automate the process of data analysis or for predictive analytics or prescriptive analytics. Many such ML models are based on data which was collected during from multiple sources from time to time and may contain a lot of inaccuracies and a data scientist must also be equipped with the skill of getting rid of such inaccuracies aka data cleansing.
However, the title ‘data scientist’ is ambiguous!
Data scientist as a job title is ambiguous and even sometimes may appear as vague. This is because companies hiring professionals do not have similar job descriptions for the same title ‘data scientist’. For instance, a software firm advertising a data science position vacant may use the job title for a role of data consultant while another firm may use the same title for a role of data analyst. While both the job roles do come under the skillset required to be a data scientist yet an individual with only data analytics skills can not be technically called a data scientist. Interestingly, in various industries there are many individuals who are actually working as data scientists but their job title may not mention it!
So, instead of trying to define the title, let us try discuss the various job descriptions associated with a data scientist:
- Acquiring data sets and preparing them for analytics
- Find out trends from a big data set and then developing ways to utilize such trends
- Developing systems using AI or ML or DL for predictive analytics
- Communicating insights to concerned parties with efficiency
- Helping companies to monetize data
And this list goes on, as a data scientist is supposed to be equipped with a broad set of tools and is capable of doing innumerable things with data!
Do not confuse data science with data analytics!
It is easy to confuse data science with data analytics as both contain a lot of similarities. But it is important to understand where data analytics ends and data science begins. A data scientist is a problem solver who not only helps to solve problems but also formulate problems which when solved may give a lot of benefits.
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While a data analyst is primarily concerned with solving a problem and cannot dig deeper. It is also to be noted that while many required skills are similar for both the jobs yet a data scientist is considered to be more skilled. This is because a data scientist is required to build advanced statistical models, proficient in ML or even DL and statistical programming languages like R!
