data science has been a trending field
of study in recent times this is because
of the amount of data that we create
constantly and the computing power that
is available with advancements in
technology but what is data science
think about what happens when you book a
ride on uber you open the uber app on
your phone and tell the app where you
want to go uber tries to find the
nearest cab since then the directions to
come pick you up and take you to your
destination that was simple but in the
background the seemingly simple task is
carried out by collecting mountains of
data from various sources like the
phones the map and historic trends of
traffic and demand for rides with this
data modern-day computers are programmed
to calculate the nearest driver to you
the best route to your location and
destination the time it will take and
what you should pay in other words this
is made possible with data science data
science has countless other applications
as well and is at the intersection of
statistics data analysis and machine
learning it is a combination of
scientific methods models and algorithms
working together to extract actionable
business insights from data the u.s.
faces a shortage of 140,000 to 190,000
people with deep analytical skills and
1.5 million managers who can analyze big
data to make effective decisions the
average salary of a data scientist is
around 118 thousand dollars so still
interested in data science as a
profession continue on to learn more
about who can become a data scientist
why data scientists matter what is the
data science lifecycle how big data is
driving the data science revolution the
career prospects for data science data
is the oil of our generation beta
science is becoming indispensable in
today's digitally driven world helping
businesses understand consumer behavior
fine tune its messaging and capture new
market share to become a data scientist
you don't need to have a technical
background to be a data scientist what
you do need is in-depth knowledge in
mathematics analytical reasoning the
ability to work with large amounts of
data it would also help to have a strong
intellectual quest knowledge of data
engineering visualization ability and
excellent business acumen if you do come
from a non-technical background you will
likely use are if you are from a
technical background then you could use
Python and R it is all about
understanding the possibilities and
asking the right questions all in the
search for the best answers every
company is flooded with data and they
have more data than they know what to do
with so regardless of the industry
vertical data science is likely to play
a key role in your organization's future
success data scientists help find new
ways of reducing costs entering new
markets and customer demographics and
launching new products or services data
science also has found social and
medical applications such as child
welfare and predictive diagnosis as well
so what does the typical data science
lifecycle look like the data discovery
step includes the search for different
sources of relevant data structured or
unstructured data then you make a
decision to include specific datasets
into your analysis the data preparation
includes converting data from different
sources into a common format you will
standardize the data look for anomalies
and make it more appropriate to work
with the data science models are built
using statistics logistic and linear
regression differential and integral
calculus among other mathematical
techniques you could use tools like R
Python SAS SQL tableau and so on getting
things in action phase includes checking
the data models for its effectiveness
and ability to deliver the results you
will have to verify the model works if
not you have to rework on your model a
data scientist needs to liaison with the
various teams and be
able to seamlessly communicate his
findings to key stakeholders and
decision makers in the organization
another critical element of data science
are algorithms which are a process of
set of rules to solve a certain problem
some of the important data science
algorithms include regression
classification and clustering techniques
decision trees and random forests
machine learning techniques like
supervised unsupervised and
reinforcement learning in addition to
these there are many algorithms that
organizations develop to serve their
unique needs big data is driven by the
data science revolution big data is the
engine propelling the rise of data
science hadoop is a popular big data
framework used by most organizations
Hadoop works in a distributed manner
where in both the processing and storing
of data is distributed on commodity
hardware Hadoop is easily scalable
highly economical fault tolerant and
secure Hadoop consists of Hadoop
distributed file system or HDFS for
storing data and uses MapReduce for
processing data another emerging
framework is Apache spark which is
touted to be up to 100 times faster than
MapReduce spark stores the data in the
RAM so iterative processing is fast and
efficient it also deploys direct acyclic
graph or daj for processing of data
there is a huge demand and supply
mismatch when it comes to data
scientists due to this salaries of data
scientists are among the best in the
industry top companies like Amazon
Google Facebook Microsoft in the tech
space to others like ExxonMobil Visa
Boeing General Electric and Bank of
America are actively hiring data
scientists now that you have learned
about data science why data science is
indispensable the data science lifecycle
how it relates to big data it is time
you start your journey in this promising
domain and see your career soar
 
