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  • 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

We hear a lot about how artificial intelligence

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

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    ka ying ho に公開 2021 年 04 月 30 日
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