The advances of the information technology world have evolved so
much these days. The role of a data scientist is almost similar to that of a
statistician and it involves machine learning, predictive learning and advanced
analysis. The companies are in a keen search for data scientists to help them
solve unstructured, semi-structured and structured data which collectively is
known as big data.
Unstructured data contains unorganized information that does not
come under a pre-defined representation. This contains potential data from
social media that can help institutions in gathering information about customer
needs. Structured data contains
information that has already been managed by organizations in the form of
spreadsheets and relational databases. Hence, multiple data forms should be managed
actively in order to attain business decisions.
The demand for skilled data scientist comes from their analytical
skills, mixed personal traits and experience. Their day-to-day job is to gather
and analyze data. They use different types of reporting tools and analytics to
detect trends, relationships and patterns in data. They are much required to
break down complex data into simpler forms. A data scientist must be able to
steer business decisions to creating and improving a product or services using
analytical data.
Skills required
by a data scientist:
Apart from being able to crush large numbers and solve complex
problems, a data scientist must also have good communication skills. With
better inputs, a data scientist can arrive at a solution with a better
statement. Creativity along with intellectual curiosity and intuition are a few
of the soft skills required by a data scientist. Interpersonal skills can also
be treated as an important trait for a data scientist as they are required to
present their data insights in such a way that every person at any level of the
organization can understand it.
Leadership qualities and decision
making skills are two of the most important strengths of a data scientist.
Experience with modeling, clustering, statistical research skills and
segmentation are few among the hard skills required.
A data scientist must have
a strong foundation about mathematics that includes statistics and a background
in computer programming. In programming, he must be well aware about Python, R,
Perl, Scala, etc. Data science also requires a large number of tools and
platforms such as Hive, Pig, Hadoop and MapReduce. A data scientist has a basic
idea about data warehousing, data analyzing, machine analysis, predictive
analysis and modeling.
Various tools have been introduced to simplify the techniques of
data science applications. With the help of data science, companies have
started to employ big data that can bring value. Due to heavy competition,
customer needs and regulatory restraints, financial institutions seek new ways
to gain efficiency by leveraging technology. Companies have a labor shortage in
search of employing best data scientist.
Beyond having math skills, a data scientist must also possess
creative abilities to create a context and meaning of the data they analyze. Standardization
of data science between data scientists and business can provide data specific
solutions.
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