Hello Technotizers, Driving any data-driven business to success is possible by making use of concepts such as Data Science and Machine Learning; many companies from the startups to the fortune 500 like Apple, Microsoft, Google and many others go on to use these concepts on a daily basis for their needs and in this article we're going to compare Data Science and Machine Learning head-on.
Introduction to Data Science:
Data Science is a multidisciplinary field basically which helps in
finding actionable insights from large sets of raw data and structured and
unstructured data at the same time, so when you talk about Data Science it
takes an unruly entity, a raw entity such as data and converts it into
something valuable and something useful. For example: information; so this
process of converting data into information and doing it in the very efficient
way is basically one of the goals of Data Science. If you have to talk about
data, data is considered to be the new oil because we all know how much worth
oil is right so data is the biggest component in today's world of technology
and this is because everyone understood the potential of data everyone
understood what data can actually drive their businesses. Driving businesses to
success is one of the biggest aspects of it, getting good insights, predicting
the future and performing analytics and much more and at the end of the day
this will of course increase profitability by somehow for all these companies.
This could be one of the main reasons why data is in the spotlight today and to
quickly write about the impact of Data Science, we've seen companies such as
Tesla that has the self-driving autonomous cars and Apple has Siri which is a
chat-bot and when you go on to learn more about these concepts or when you want
to practically use them you understand that these concepts are such beautiful
innovations of today's world. They make use of concepts such as Machine Learning,
Artificial Intelligence and all of these come in fact under the umbrella of
Data Science.
Then you have to talk about UPS. UPS basically saved 39 million gallons
of fuel by just optimizing how they deliver packages, so UPS is basically a
very famous package delivery service across the world and a courier service who
analyzed the routes probably rerouted it very efficiently to understand how
better can they produce route, how better can they actually follow along by
delivering packages and they saved 39 million gallons of fuel.
Introduction to Machine Learning:
Well what is Machine Learning is the first question you're supposed to
ask. Machine Learning as we saw in the previous article is an application of
Artificial Intelligence to begin with of course, because at the end of the day
Machine Learning whenever we talk anything about Machine Learning it is to
achieve AI on certain level it might be Weak AI it might be strong AI or
whatever it is. So Machine Learning basically provides computers the capability
to learn on their own and to improve or with the experience that they've been
using to learn. Machine Learning is a lot of usage of algorithms, mathematics
and statistics and all of these on steroids but in the future. Machine Learning
will be all about achieving Artificial Intelligence as it was just written even
right now we are striving very close to it but here when we have to talk about
cognition and achieving Artificial Intelligence as a whole we compare this to
human level intelligence and then work with it and of course this future is
very foreseeable it's very near and probably by the end of this decade we will
have multiple revolutionary concepts, multiple revolutionary tools and
techniques which will basically help mankind to get one step closer to
Artificial Intelligence. So there are multiple Machine Learning applications
around like voice recognition, social media, video surveillance, malware and
spam detection predictions and for and many other uses. As well if we talk
about voice recognition, again voice unlock is a very famous use case of
Machine Learning, then if we talk about social media or Facebook, it recognizes
your face automatically, then Instagram knows what advertisements it's supposed
to show you, Twitter analyzes the sentiments of the tweet, YouTube, Skype all
of these make use of Machine Learning on a daily basis. If you have to talk
about video surveillance, think about automated traffic; it finds a system
where you know that there doesn't have to be a cop finding people who are
violating the traffic rules, it might just be a camera which is smart enough
and which has a very good framework and it can pretty much capture people who
are going against the rules and find them automatically.
Data Science vs. Machine Learning:
1. Definition: Data
Science as you know is a field where data of any type like structured data,
unstructured data, semi structured data goes through a process of being
cleaned, filtered and analyzed and all of this is done to ensure there is
something useful which can be put out in the other end of it. Coming to Machine
Learning, it’s actually a part of Data Science which makes use of
multiple tools and techniques out there which creates beautiful algorithms
which are the basic foundation the, fundamental aspect of where and how a
machine can learn from data by making use of the experience.
2. Aim: Data
science helps you to define new problems that actually need to be solved in
today's world so instead of giving a direct solution to all the problems which
already exist this will define new problems and these new problems have a point
of answering. And this answer comes from Machine Learning. Machine Learning
knows how the problem is sorted out and it helps in giving the solution to the
problem.
3. Working: Data
Science works with multiple manual methodologies but when you have to talk
about making a machine efficient but by comparing it directly to a machine
which makes use of algorithms, Data Science lacks a little and Machine Learning
of course cannot exist without Data Science and all of these data which Machine
Learning algorithms use to work so efficiently have to come from all the other
Data Science concepts where models are basically preconditioned. Data cleansing
is done and then later these algorithms are applied to create a model, to train
these models, to test if it's working.
4. Skills: Data
Science is a prerequisite of understanding SQL, a structured query language and
this is needed because when you work with data you'd be talking to databases,
if you have to talk to databases, you have to talk to your data present in the
databases, you need to understand how you can create tables, create databases,
work with your data, alter your data, delete your data and much more. When you
talk about our Machine Learning, it requires a bit of programming in depth
because languages such as a Python or Java all of these concepts are the ones
which implement the mathematical concepts or statistical concepts and which
provide the foundation or help the Machine understand the mathematical
algorithms on which it has to use these concepts and work on.
5. Connection: Data Science is a
complete package, it is a complete process which involves a lot of things as
told, everything from data cleansing to data analysis comes in the field of
Data Science but when you have to talk about Machine Learning, it is just one
part in this huge world of Data Science.
6. Average salaries: Then coming to the next point which is basically the average salary data scientists get, an average salary of somewhere 130 thousand American dollars per annum but then you have to talk about Machine Learning engineers, Machine Learning engineers also get an attractive compensation of somewhere about 124 thousand American dollars per annum as well so these both are very lucrative carriers and they pay really well. And also they are among the top jobs in today's world.
So on this note you've reached the end of this comparison. Hope you guys enjoyed this article and got to learn a lot from the same. See you then!!
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