Data Science in 8 Minutes | Data Science for Beginners | What is Data Science? | Edureka

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 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 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 are P 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 is 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
boober 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. Exploration and Analysis. 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 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 Qu
ality 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 bars seven. 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, make sure you subscribe
to our Channel until next time happy learning. I hope you have enjoyed
listening to this video. Please be kind enough to like it and you can comment any
of your doubts and queries and we will reply them at the earliest do look out
for more videos in our playlist and To Edureka channel
to learn more. Happy learning


  1. Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Data Science Masters Certification Curriculum, Visit our Website:

  2. This is language is very lucid and give a clear understanding of what is data science.
    Does the Edureka Team provide mentor for the students ?

  3. I'm BCA graduate of 2014. I was preparing for IAS exam but didn't clear. Want to come back in IT and also preparing for DS. Can u notify the challenges I will face and how can I overcome from this?

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