post created by :
1. Amit Kumar Auddy(BSC. IT-Big Data Analytics)
2. Aryadev
Banerjee(BSC. IT-Data Science)
Data Science Job Trends
Data Scientist – With the surge
in data and its correlated fields, the job of a Data Scientist has
become the most sought after job. Many IT professionals and
academicians who have worked in quantitative fields want to become
data scientists.
There will be a sharp increase in
demand for data scientists by 2020. According to IBM, an increment by
364,000 to 2,720,000 openings will be generated in the year 2020.
This demand will only grow further to an astonishing 700,000
openings.
The requirement for the number of
data scientists is growing at an exponential rate. This is resulted
by the emergence of newer job roles and industries. This is
supplemented by the increase in data and its various types. The
number of roles and data scientists will only increase in the future.
Some of the positions in data science such as data engineer, data
science manager & big data architect. Moreover, the financial and
insurance industries are becoming major players for recruiting data
scientists.
Data
Science over the next few Decades
Data Science is predicted to grow
over the next decade. It is a staggering fact that over 90% of the
data in the world was generated in just 2 years. It is unimaginable
to realize the amount of data that will be generated in the next
decade. The demand for data scientists will rise by 28% by 2020
alone. More and more industries are becoming data hungry and they
need data to hold specialized data scientists who can craft products
for the customers. About 11.5 Million jobs will be created by 2026
according to U.S. Bureau of Labor Statistics.
Data Science is rather an
unrefined and crude term. It is a general term that has several
definitions. However, with the passage of time, the data science
roles will become more concretized. There will be a concise
definition that will be imparted to data science that will enable the
data scientists to handle corresponding operations. Deeper career
paths will be developed in data science. Furthermore, a cleaner set
of rules and regulations will differentiate pure data scientists from
others.
There will also be a
diversification of roles in data science over the next decade.
Currently data science is an uncharted territory that is often
misrepresented by various industries. The job role of a data
scientist, therefore, suffers from this lack of representation. This
is because currently both SQL operators and Data Scientists come
under the same general definition of data science. This problem will
fade away as industries will create job roles that are specified only
to the specific data roles.
Rising
Demand for Data Scientists-
The rise in demand for data
scientists will prompt educational institutes to include it in their
curriculum. The data literacy will increase in future and a data
scientist will have a specialized holding, pretty much like a doctor
or a lawyer. That is, he/she will be part of an entirely new
discipline in itself. Recently, many universities have released their
data science degrees that will bridge the skill-gap in the
industries.
Since the field of data science in
itself is young, data scientists do not hold years of experience
behind them, as compared to other IT related field. In the next
decade, data scientists will see a much greater distinction between
Senior Data Scientists and other positions.
Top
Data Science Jobs
1.
Data Scientist
Data Scientists are analytical
experts who are responsible for finding insights and patterns in the
data. A Data Scientist is responsible for handling raw data,
analyzing the data, implementing various statistical procedures,
visualizing the data and generating insights from it. A Data
Scientist is also responsible for handling both structured and
unstructured information.A Data Scientist must have knowledge of
various tools like Hadoop, R, Python, SAS, etc. Knowledge of data
preprocessing, visualization and prediction are some of the important
requirements of a Data Scientist.
2.
Data Architect
A Data Architect is responsible
for implementing the blueprints of a company’s data platform. This
blueprint or architecture delineates various models, policies, rules
that govern the storage of data as well as its use in the
organizations. A Data Architect is responsible for organizing and
managing data both at the macro level as well as the micro level.Some
of the important tools used by a Data Architect are XML, Hive, SQL,
Spark and Pig.
3.
Data Engineer
A Data Engineer is responsible for
building big data pipelines and models for the data scientists to
work on. A Data Engineer must be well versed with both structured as
well as unstructured data. A Data engineer is not only responsible
for building data models but also maintaining, managing and testing
it.Knowledge of database models and ETL are two of the most essential
requirements for a Data Engineer. A Data Engineer is responsible for
modeling large scale processing systems using tools like SQL, Hive,
Pig, Python, Java, SPSS, SAS etc.
4.
Data Science Manager
A Data Science Manager is
responsible for handling and managing data science projects. A Data
Science manager handles the team and manages the performance to meet
project deadlines. Usually, data science managers have an average of
five-year experience in any of the data science domain like date
engineering, data science or analysis.
Data Science managers are
responsible for planning and curating a roadmap for the data science
team to follow. Furthermore, they are responsible for executing the
plan of action and delivering the results before the deadline. He/She
should also have strong communication and leadership skills in order
to guide the team efficiently.
5.
Statistician
A statistician is the oldest job
title among all the roles. Before data science, statisticians were
employed by the companies to use statistical modeling for
understanding various trends in the market. A statistician is
responsible for implementing A/B testing, harvesting data, describing
data, developing inferential statistical tools and performing
hypothesis testing.Some of the tools used by statisticians are R,
SAS, SPSS, Matlab, Python, Stata, SQL etc.
6.
Machine Learning Engineer
A Machine Learning Engineer is
responsible for tailoring machine learning models for performing
classification and regression tasks. A Machine Learning Engineer has
the knowledge of various techniques like clustering, random forest
and several other deep learning algorithms. It is an advanced field
and people are required to possess analytical aptitude skills to
develop machine learning algorithms.Some of the popular tools used by
the machine learning engineers are TensorFlow, Keras, PyTorch,
scikit-learn, Caffe etc.
7.
Decision Scientists
The field of decision science is a
relatively new field. Decision Scientists help the company to make
business decisions with the help of tools like Artificial
Intelligence and Machine Learning. It is a part of data science that
extends to design thinking and behavioral sciences to better
understand the clients.
What
does Data Scientist do?
A data scientist mines complex
data and deliver systems-related advice to his/her organization. In a
team, they manage statistical data and look at what their company
needs to create different models. A data scientist knows how to
interpret data and extract meaning from it. Since data is rarely ever
clean, s/he spends time collecting, cleaning, and munging it. S/he
needs skills like statistics, machine learning, software engineering
skills, persistence, and being human. That person also spends time in
exploratory data analysis- in visualization and data sense. S/he will
find patterns, build algorithms and models, design experiments,
communicate with team members, and perform data-driven decision
making.
Data
Science Job Trends in India
Following are the trends in Data
Science job postings per 1 million –
From the above chart, we can infer
that Data Science jobs have been growing in trending charts. In 2019,
it is expected that this trend will only grow higher.
Data
Scientist Salary Based on Location
Major metropolitan cities like
Bengaluru, Mumbai and Delhi have a high concentration of data science
companies as well as thriving tech companies. Furthermore, various
burgeoning startups in these cities are working on data science
technologies. Therefore, there are ample opportunities to find work
in these areas. Let us now have a look at how these cities are ranked
based on the salaries offered.
Data
Science Salary Based on Skills
Based on the skills of a data
scientist, the salary also varies. Machine Learning is the most
in-demand tool that the data science industry requires. As a matter
of fact, it is the most sought out and highly paid skill in data
science. Following are some of the key skills based on which salaries
are offered to the data scientist.
Data
Scientist Salary Based on the Industry
The salary of a data scientist is
dependent on the industry of employment. Based on the industry, the
data also varies. Therefore, the specialization of data scientist
also differs across the industries. According to Linkedin, Data
Scientists earn the highest salary in the area of Consumer Goods
followed by Finance, Energy & Mining, Media & Communication
and Corporate Services.
Data
Scientist Salary based on Degree
According to a survey carried out
by Linkedin, a Ph.D. degree can earn you the highest package in data
science. This is followed by a Master’s Degree. Therefore, it is
recommended that you have an advanced degree. Furthermore, the field
of your study also plays some role in determining salary.
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