Data Science
Data Science
Hay their hope everyone is good and healthy, by considering
my trailing articles I am going to start the latest technology topic in coming
days. I am Techno Bhoir, I am writing articles on Technology Topics. Today we
are going to discuss about the technology called Data Science, okay let’s
start.
We hear a lot
about how artificial intelligence and machine learning are going to change the
world and how the internet of things will make everyone's life easier. But
what's the one thing that underpins all of these revolutionary Technologies?
The answer is data. From social media to the (Internet Of Thing) iot devices for generating. Bill amount
of data consider the cab service provider Uber. I'm sure all of you have used
Uber. What are you think makes Uber a multi-billion dollar worth company. Is it
that availability of cabs or is it their service? Well, the answer is data. Data
makes them very rich, but wait, is there enough to grow a business? Of course,
it isn't you must know how to use the data to draw useful insights and solve
problems. This is where data science comes in. Words data science is the
process of using data to find Solutions or to predict outcomes for a problem
statement to better understand data science.
Let's see
how it affects our day-to-day activities. It's a Monday morning and I have to
get to office before my meeting starts. So I quickly open up Uber and look for
cabs, but there's something unusual the gab reads. A comparatively higher at
this hour of the day. Why does this happen? Well, obviously because Monday
mornings cars and everyone is rushing off to work. Work the high demand for
cams leads to increase in the cab fares. We all know this but how all of this
implemented data science is at the heart of Ubers pricing algorithm. The Surge
pricing algorithm ensures that their passengers always get a ride when they
need one. Even if it comes at the cost of inflated prices, Uber implements data
science to find out which neighborhoods will be the busiest so that it can
activate search pricing to get more drivers on the road in this manner over
maximized. The number of rides it can provide and hence benefit from this Uber
surge pricing algorithm uses data science.
Let's see
how a data science process always begins with understanding the business
requirement or the problem. You're trying to solve in this case. The business
requirement is to build a dynamic pricing model that takes effect. When a lot
of people in the same area are requesting rides at the same time. This is
followed by data collection Uber collects data such as the weather. Oracle data
holidays time traffic pick up and drop location and it keeps a track of all of
this. The next stage is data cleaning while sometimes unnecessary data is
collected such data only increases the complexity of the problem an example is Uber
might collect information like the location of restaurants and cafes nearby now
such data is not needed to analyze Uber surge pricing there for such data has
to be removed at this step data planning is followed by date.
The data
exploration stage is like the brainstorming of data
analysis. This is where you understand the patterns in your data. This is
followed by data modeling the data modeling stage basically includes building a
machine learning model that predicts the Uber surge at a given time and
location. This model is built by using all the insights and Trends collected in
the exploration stage. The model is trained by feeding at thousands of customer
records, so that it can learn to predict the outcome more precisely. Next is
the data validation stage now here the model is tested when a new customer
books arrive the data of the new booking is compared with the historic data in
order to check if there are any anomalies in the search prices or any false
predictions, if any such anomalies are detected a notification is immediately
sent to the data scientists at Uber who fix the issue. This is how Uber
predicts a surge price for a given location and time the final stage of the science
is deployment and optimization. So after testing the model and improving its
efficiency, it is deployed on all the users at this stage customer feedback is
received and if there are any issues, they are fixed here. So that was the
entire data science process.
Now, let's look at a few other applications of data science is implemented in e-commerce platforms, like Amazon and Flipkart. It is
also the logic behind Netflix's recommendation system now in all actuality Quality
data science has made remarkable changes in today's market. It's applications
range from credit card fraud detection to self-driving cars and virtual
assistant such as City and Alexa. Let's consider an example suppose you look
for shoes on Amazon, but you do not buy it then in there. Now the next day
you're watching videos on YouTube and suddenly you see an ad for the same item
you switch to Facebook there also you see the same ad so how does this happen?
Well this Happens because Google Tracks your search history and recommends ads
based on your search history. This is one of the coolest applications of data
science. In fact 35% of Amazon's revenue is generated by product
recommendation. And the logic behind product recommendation is data science.
Let me tell
you another sad story Scott killed in never imagined his Apple watch might save
his life, but that's exactly what happened a few months ago when he had a heart
attack in the middle of the night. But how could a watch detect a heart attack
any guesses? Well, it's data science again. Apple used data science to build a
watch that monitors and individuals Health this watch collects data such as the
person's heart rate sleep cycle breathing rate activity level blood pressure
Etc and keeps a record of these measures 24*7. This collected data is then
processed and analyzed to build a model that predicts the risk of a heart
attack. So these were a few hours Locations now the question is how and why you
should become a data scientist according to LinkedIn’s March 2019 survey a data
scientist is the most promising job role in the US and it stands at number one
on glass doors best jobs of 2019. Here are a couple of job trends that are
collected from LinkedIn top companies like Microsoft IBM Facebook and Google
have over thousand job vacancies, and this number is only going to grow. Hurley
these job Trends show the vacancy of jobs with respect to jog defame coming to
the salary of a data scientist the
average salary ranges between a hundred thousand dollars two hundred and eighty
two thousand dollars. Now remember that your salary varies on your
skills your level of experience your geography and the company you're working
for here are the skills that are needed to become a data scientist. You must be
skilled in statistics expertise in programming languages like our and python is
a Just you're required to have a good understanding of processes, like data
extraction processing wrangling and exploration. You must also be well-versed
with the different types of machine learning algorithms and how they work
Advanced machine learning Concepts like deep learning is also needed you must
also possess a good understanding of the different big data processing
Frameworks, like Hadoop and Spark and finally, you must know how to visualize
the data by using tools like Tableau and power bi now that you know what it
takes to become a data scientist. It's time to buckle up and kick start your
career as a data scientist. That's all from my side guys. If you wish to learn
more about such trending Technologies, you can comment any of your doubts and
queries and i will reply them at the earliest do look out for more articles
Stay at home, Stay Safe
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