Now a day we often hear the buzzwords like
Data science (DS), Artificial Intelligence (AI), Machine Learning (ML) and Deep
Learning (DL). In this blog, we will try to understand each term to avoid any
confusion.
What is Data Science??
Data science is “a field of study of
data “. Here data is being
studied, analyzed and processed so as to gain more information from data.
As per Wikipedia:
“The term
‘data science’ has appeared in various contexts over the past thirty years but
did not become an established term until recently”
Why data science became so important
lately??
As
you hear very often, data is new oil.
With the Industrial revolution, oil (main source of energy) becomes so
important that each and every country is reliant on the oil to run their
economy. Even at present, oil is the main source of energy and a small
disruption in its flow bring chaos to world.
In the similar way, with the advent of
technological revolution data became one of the most important tools to have
edge in highly competitive world of market economy. Businesses, having more
insight into data, thrive in market economy with competitive advantages.
Insight into business scenario comes through analyzing the data, understanding
it, having meaningful insight into it. Based on the insight, timely action
helps the businesses to beat their competition. Now a day, many companies have
a data science department to grow their business.
Currently data is being used to do
following type of analytics:
1 Descriptive Analytics: This is the area where data is being studied in the business to get
more insight into it like understanding trends, biases, variations etc. This is
mainly about data mining, data aggregation and disaggregation to understand
‘What has happened?’
For example: Slicing and
dicing sales data to understand which area / product is contributing more to
the overall sales
2 Predictive Analytics: This is the
area where data is being used to forecast with help of statistical and
forecasting tools. This is mainly about ‘What could happen?’
For example: Using the
past sales data to forecast future sale.
3 Prescriptive Analytics: This is the area
where optimization and simulation algorithm is used to advice on possible steps
that needs to be taken in given scenario.
For example: Recommending
promotion that needs to be used to increase the sales.
What is Artificial Intelligence (AI)?
Artificial intelligence is about equipping
machine with a person like intelligence.
A person’s intelligence comes from the data
that is gathered through various sources like by seeing, touch, smell etc and
then brain helps to process the data and to make decision or act.
Similarly, Artificial intelligence comes
from gathering, understanding, learning data and making decision based on it.
With the technological advancement in computing/processing power, machine is
able to gather/store and process lot of data to get the insight to describe,
predict and prescribe. Artificial Intelligence has become a buzzword since it
is now being widely used in almost all sectors like health, finance,
manufacturing, retail etc. It has intruded our day to day life too in form of
google assistant, apple siri, amazon’s alexa etc.
Machine Leaning (ML)
Machine learning is a subset of AI or can
be termed as an implementation of AI.
Here machine learns the data patterns based
on the huge data provided to machine and then use that information to
understand new data that is coming in. Mathematical/statistical models and
programming language are used to implement it. There are different forms of
machine learning:
1 Supervised learning :
Here historical data
(training data) is used to understand the relationship between independent
features (input) and labelled data (outcome). Statistical model are made based
on the training data. Any new data’s (test data) outcome is predicted based on
this statistical model.
2 Un-Supervised learning:
In Un-supervised learning,
data does not have any outcome column. Here model uses intrinsic pattern of the
data to learn and give insight. Clustering is one such technique where data are
clustered together based on their similarity.
3 Semi-supervised learning:
Here model uses concept of
both supervised and unsupervised learning to get data insight.
4 Reinforcement learning:
This kind of learning
doesn’t use any answer key to guide the execution of any function. The lack of
training data results in learning from experience. The process of trial and
error finally leads to long-term rewards.
Deep Learning (DL):
Deep Learning is a subset of machine
learning where it tries to mimic human brain. As the brain receive information
and tries to compare it with known item before making sense, in similar way
deep learning tries to compare predicted value with outcome and try to self
learn. It uses concept of neural networks to do so.. It tries to learn itself
from given data without any human intervention. It is evolution on ML in the
sense that apart from what ML can do ,it can also work on large data set as
well as the complex scenario where machine learning fails like Speech
recognition , image recognition etc.
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Diagrammatic Depiction of DS /AI/ ML/DL
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