roles of data scientiest Defining Data Science • What Does a Data Science Professional Do? • Data Science in Business • Use Cases for Data Science

1. Data Scientists And Their Role
Who Are Data Scientists And What Do They Do?
As fancy as it may sound, there is a common notion among the masses that a data scientist is a
rockstar! A genius who has a solution to all the problems and can solve any given query within
seconds. Well, at times it is easy to extract useful patterns and at times, it can be a
pain for the cerebrum!
A data scientist is an individual who has the power and freedom to experiment with tons of
different kinds of data- both structured and unstructured. If you give him a problem and ask his
opinion, he will present you with a whole new perspective of that problem and give you honest
answers based on his knowledge in mathematics, problem-solving, critical thinking and careful
analysis.
For anyone who is willing to carry this “tag” along should be well-versed with a lot of concepts.
Some of them are
• Mathematics
• Statistics
• Problem-solving
• Data wrangling or data munging
• Coding prowess in both R and Python
• SQL
• Hadoop
• Machine learning and AI
• Data visualization
• Communication skills
These skills should hold mandatory for anyone who wants to make their resume shine for the
field of data science. Yes, there is a difference between a data scientist and a data analyst.
Responsibilities of a data analyst vary from that of a data scientist. A data analyst has a lot to do
with converting the data into a structured format in order to process it further. Its profile focusses
more on data mining and data auditing. Data mining involves retrieving information from large
databases with the help of SQL to extract new data/information. Data auditing involves checking
the essence of data and trying to figure out if the data is capable enough for gaining useful
insights or not.
On the other hand, taking the clean data and trying to gain some meaningful insights is what
data scientists begin with. This data is later crucial for the machine learning part where firstly,
the results are analyzed for the dataset. Then, an algorithm either from classification or
regression is implemented in order to create a model and make it sustainable enough to gain
some business insights with the help of visualization tools. Data visualization is carried in plots
and charts and enables a non-data science person to understand the key findings in the data.

2. Are There Enough Skilled Data Scientists In The Industry?
The current scenario of the industry is that it is flooded with data scientists, both freshers and
experienced. Freshers are the ones who already consider themselves as experts in data
science- something which usually takes years to master. They are probably the ones who got
excited by the hype and acquired these skills by some MOOC courses and reading a few books
related to data science. The experienced candidate probably has some working knowledge in
cleaning data and implementing models in real-world. Experienced candidates are preferred to
the freshers in data science. Data science is an emerging field where it requires enthusiasts to
remain abreast and informed with the latest developments and skills that are necessary for their
enlightenment.
According to a survey conducted by IBM, the demand for data scientists will soar by 28% by
2020. That includes all jobs which require machine learning, big data, visualization like Tableau
and PowerBI expertise and knowledge of data analysis. This is divided among the industries
looking for such professionals in finance, insurance, professional services, and IT sectors.
Meanwhile, there are a lot of data scientists actively looking for jobs. One might wonder why
they are not being hired. There is a huge gap between the skills and tools required by the
industry and ones that the newcomers possess. The online MOOC courses available on the go
often fail to imbibe those skills at certain levels. Just reading online some reviews and
suggestions and learning the basic skills that are needed to get into this industry might not be
enough. A proper guidance topped with the right resources is important to start this exhaustive
journey. A candidate who is always thirsty for new challenges and loves problem-solving of any
kind is capable to become a skilled data scientist. He likes observing and defining a problem
from different angles and perspectives. Coding is his daily hustle and loves doing it, not
because the problem demands him to do, but he knows how interesting it becomes to come up
with new findings and insights and then make a cute little story out of it!