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.

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until next time happy learning.

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