Hey guys. Welcome to Intellipaat In today’s Video. We’re going to talk about a technology that

is a backbone of Hottest job of 21st century. That is data science. So let’s start this video with a quick fact. According to McKinsey. That is a 50 percent gap between the supply

and demand of data scientists. This clearly indicates that there’s a right

time to seize an opportunity to land yuorselves in the hottest job of 21st century. So in this spirit of helping you guys to become

a data scientist we have come up with the most comprehensive video. On data science. So this video has everything you need right

from the very basic concepts of data science that the most frequently ask technical interview

questions. So before we get started please do subscribe

to Intellipaat’s YouTube channel to get some instant notification about upcoming videos

also guys if you are interested in doing a full fledged course in data science please

do check out Intellipaat’s data science architects masters course that is co-created in association

with IBM. Link for the same is available in the description

box below. please do check out. Now let’s take a look at the agenda of this

video. We’ll start off with a quick introduction

to data science to understand what it is and why do we need it. Later we’ll be working on the complex mathematical

operations using numpy Then we’ll be learning about data manipulation using pandas and data

visualization using matplotlib. Then we’ll be moving ahead with a machine

learning concepts. We will be also learning about machine learning

algorithms such as logistic regression and linear regression and much more. We will also be working on an end to end project

in data science which will help you to polish all the necessary data science skills. Finally you will also be learning about the

most frequently asked technical interview questions. So without any further delays let’s get started. So guys we’ve all heard the term data scientists

some where in our life I presume and to put it all in one simple sentence. This is how I would pretty much go about saying

guys well data scientists are a new breed of analytical data experts who have the technical

skill to solve any complex problems. And then the curiosity to explore what problems

need to be solved as well sound sounds amazingly complex right. Well it actually isn’t. Well let me tell you this very funny thing

that I just read on the Internet data science is an rocket science. And let me prove why. And there’s endless opportunities with data

science and you all can grab Very good opportunities guys so coming the data scientist is a person

who is a combination of a mathematician a computer scientist and he’s a trend spotter

as well. Well mathematician because he is required

to play around with a lot of mathematical concepts a lot of differentiation a lot of

calculus and so much more. And then a computer scientist because he needs

to convert all the mathematics into computer science and then work with it later and then

as a traend spotter he needs to find out some analytics or to hunt for trends in the future

or to make it a part of his forum or her forum for example and then pull all the concepts

together and to work with it guys. So the question you might be asking right

now is so where do data scientists come from. Right. Oh well think about it this way a data scientist

is pretty much a person who can assume multiple roles over the course of a day right. So he can be a software engineer in the morning

or he can be all data analysts at lunchtime can be a troubleshooter. At the time of high tea you can be a data

miner a business communicator a manager and then a key stakeholder in any data driven

enterprise as well So basically at the end of the day. He puts his hand in multiple roles with the

designation of a data scientist and gets the job done. With respect to everything from the lower

levels to the highest levels and all this level of business should making in this level

of skill required to become a data scientist is what people look up to these days and that’s

why they’re very highly paid as well guys. So many data scientist you know again pretty

much began their careers either as data analysts or decisions as I have mentioned right. But then think about think about all the big

data that has pretty much went to grow and evolve or these days right. So I support the data being evolved again. The roles are being evolved as well. Data is no longer just an afterthought for

I.T. to handle. I had to put this in bold for you guys too

pretty much you know look at it and contemplate about it as well so data. Again as we pretty much talk about it as in

the Hexa bites these days and petabytes is pretty much a thing of the history by the

way. So when you have so much data to handle you

are going to need some good manpower behind that right up to coming back again. It’s a key information that requires analysis

create of curiosity and a knack for translating high tech ideas into new ways to turn a profit. Well you pretty much are getting the other

side of a data scientist right from the slide right. So you pretty much might be thinking a data

scientist just sits in you know punches in code to day in day out new pretty much gets

paid and goes on. Well not exactly that. I do have a good number of data scientists

as my close friends and all of them seem to agree about this point as well. A quick and four guys. In case you are looking for a full fledged

course in data science please do check out in Intellipaats data science architect Masters

course that is co-created an association with IBM. This causes all the required skills to master

the concepts of data science and become a data science professional. Link for the same is available in the description

box below. please do check out. Now let’s continue with the session coming

to the next question that you might have in your mind right now. What does a data scientist actually do well

a data scientist job again is to analyze all the data for any insights that can be generated

to find these hidden insights like these tiny amounts of tressure that’s so hidden inside

a business and then to pull it all out guys. So here are the following tasks that I would

like to mention that a data scientist would do right. So the first thing is to pretty much go about

identifying all the data analytics problem that offer the greatest value to the organization. Yes because at the end of the day these data

analytics problems if they’re sold really early than it is it sounds very lucrative

and at the end of the day it brings more money to the organization as well right. And the second point is pretty much to get

to know. You know you’re right. Data sets your right variables your core to

what are you working with. You know to have of hand advantage with your

core is going to pretty much again give you an upper hand compared to your peers as well. So all you’ll be working with will pretty

much you know unstructured data you’ll have a lot of images from data sets you’ll have

a lot of videos you’ll have a lot of music there’s so much more that you know you have

to look into as a data scientist than just looking at it from a far perspective and then

coming to or discovering new solutions and opportunities by analyzing the data. Well I’ve already told you that you know data

is the store well data just is is this raw information which pretty much is is useless

right. Making sense of this data converting that

data into information that at the end of the day and a company can go about making use

that’s that’s again that really takes a lot of skill right. So coming to the next point of collecting

well a large set of structured and unstructured data from disparate sources well structured

data is something which can be handled very easily. So consider an SQL table or something which

is in the form of a structure where you can look at it and make sense out of it. Even your computer can make sense out of it. That’s pretty much what is the simplest form

for a structure data well unstructured data again as you can probably discern it but then

you really need you machine you machine would need a little help to understand what the

actual data is right. So that is unstructured data. And then the next pretty much you know insight

is you’ll have to go about cleaning and validate the data to ensure that your data is extremely

accurate. You do it as complete and thourough your data

is uniform as well. The next pretty much again the point speaks

for itself right. Data has to be clean. We would we wouldn’t want to be working with

data on unless it actually goes into your business model and work with it right. So if it is working on a piece of data at

the end of the day if you know it is useless then you’re just wasting your time you just

wasting your resources on it right. We don’t want that and then you would want

device and apply more tools and machine learning algorithms and used to go about mining and

hunting or you know for it or let’s say it’s a literal treasure to hunt in the world of

big data and then you need to analyze the data to identify patterns and trends. Well to me just looking at what a data scientist

does day in and day out that’s pretty much entertaining at the same fact. And then knowing that this is a very highly

paid job and knowing that the people who are data scientists have a good amount of fun

at their jobs is pretty much an amazing thing. And on that note we need to come check out

what are the prerequisites that we can pretty much have to go about becoming data scientists

on our own guys. The first most important thing which you might

not have thought of was effective verbal and communication skills guys see because at the

end of the day let’s say you’re writing machine learning algorithms or you’re working with

your big data team you’ve pulled out very good insights. If you do not have the communication skills

where you can go on tell this to your superiors let’s say pretty much have to go to a stakeholder

and tell him this you have a meeting where you have to tell a superior or you have to

explain this to your junior you have to explain this to your co-workers peers and if you can

not do that if you cannot explain machine learning or neural network to a person who

doesn’t know machine learning but wants to understand what’s going on. Well that would be really tough if you did

not have communication skills as a data scientist so having skills is a very important thing

guys. And the second thing is if you work for a

moderately sized company your project will be too big to handle single handedly perhaps. Right. So you need to be at the top of your game

when you’re working alone all when you’re working with the team because your pretty

much whatever code you are writing may be split among five people many split among 10

people and then you guys are sitting at different parts of the world just writing the single

piece of code right so knowing that you can pretty much communicate with each other really

well get the work done and have be a very good team player at the end of the day is

a very good prerequisite if it gives you guys a very good team players then guys you can

pretty much become a data scientist. A quick and for guys. In case you’re looking for a full fledged

course in data science please do check out Intellipaat’s data science architect Masters

course that is co created an association with IBM. This causes all the required skills to master

the concepts of data science and become a data science professional. Link for the same is available in the description

box below. please do check out. Now lets continue with a session and the third

important thing is you need to have this interest to dvelve in to data to pretty much hunt for

stuff and data and collect and make sense out of the data and eventually by analyzing

this data you’ll be picking out some very very good insights from this particular data

guys. And number four is very good problem solving

skills. Again you will need to have a certain amount

of skill when it comes to problem solving puzzle solving you know because you’ll be. Because this is this not well some field where

you have a very simple goal and you just go and get it. You will have you’ll have you will know what

you’re going after right. So you will have some goals that you know

are. Let’s say there are hidden goals right now

but then you start hunting in your data you start picking up your insights and you realize

Whoa three months from now my data is going to look like this or let’s say three days

from now or after I run this machine learning algorithm my data is going to look like that. So you need to have that kind of analytical

thinking and problem solving skills. And if you guys have that then trust me you

can become a very good data scientist Well if you’re wondering if you might be a junior

and associate or a student watching this livestream right now and you might be wondering well

is it possible for me well guys sure you do not have to be an absolute professional in

this right now. But and then they stick to the end of the

video we will make sure that you know I will personally make sure that I can get you on

track to get all the prerequisites to become a scientist guys and then the most important

highlight in this particular slide is the educational background right you guys. I wouldn’t stress on this very much because

I know people who do not have a computer science background becoming data scientist but then

many of the companies especially in India and Asia they require a computer science background

for you to become a data scientist or you need an educational background from information

technology you will need mathematics and statistics is extremely important because you’ll be working

with a lot of numbers you’ll be crunching in a lot of graphs and so much more and then

work experience in a related field. Well if you have it good enough. If you don’t then a certification will come

into play in this particular point guys so on that note we need to quickly check out

what the roles and responsibilities of a data scientist are. Again I have pretty much put it into a very

simple terms. I’ve broken it down to four concepts right. So as a data scientist again guys you will

need to know that you are need pretty much need to design your own systems your own algorithms

you need to develop something from data you need to deploy the most relevant solutions

for your business and share your results with all these stakeholders and business leaders

and so much more right. So what does this call for. Well this pretty much has you to know requires

you to pretty much go about preparing goal for all the big data all that have data that

the world has to offer. Implement all the relevant data models create

databases to support a business solution present all of these to stakeholders and so much more

right. So as i told to keep it simple I’ve broken

it down into four terms out here analytics collaboration business understanding and strategy

design guys so coming to the first one it does analytics. Again I have to put it out in simple terms

it’s exploring ways to infer relevancy in your particular data right. We already know that you know the amount of

data that’s being generated every day is 2.5 quintillion bytes 2.5 quintillion bytes I

wanted to put that in bold and probably this let you guys share it quintillion bytes site

and having up and most of it is relevant data at the end of the day right. This is huge. You need to you need to take charge and go

about leveraging your fast growing data sources and you need to capture the market analyze

the market and knowing that data is the biggest business challenge these days and that your

competitors are facing it. Solving that problem right now is really important

as a data scientist guys and then regardless of whether the data is you know from a computer

it’s from other sources it’s from Twitter. We really don’t care. The data comes in from all the different sources

in this world. But then at the end of it you will be doing

some relevant analytics on it and you will be making sure to discover some important

information that which if you would not then the information would just stay hidden in

this data which is useless right. A quick info guys. In case you are looking for a full fledged

course in data science please do check out Intellipaat Data science architect Masters

course that is co created in association with IBM. This course has all the required skills to

master the concepts of data science and become a data science professional. Link for the same is available in the description

box below. please do check out. Now let’s continue with a session. So this brings us to the second important

thing. So does this collaboration collaboration again

as it all collaborating with your teammates collaborating with your peers. But then the highlight of a data scientist

is collaborating mostly with the stakeholders. So you must need to learn. Do you know you have to learn to design develop

and implement the most appropriate solution for your business and then pretty much does

the exact same point that we already discussed. You need to prepare for big data you need

to handle and create databases. Implement a data models guys so as data scientist

you know you will be working closely with your marketing team. You will be working closely with decision

makers stakeholders in the products and your different products to basically you know communicate

all your possible outcomes of what can happen to your product guys and this type of collaboration

as we already know what has been proven to be a huge achievement and is often considered

to be one of the most important data scientist roles and responsibilities well. Think about it. I already told you right. Communication is an extremely vital part of

data scientist life and as you might have thought initially that coding is everything. Well actually it is’nt guys if you’re very

good at communication trust me you can become a very good data scientist in your life as

well. So this brings us to our third important role

and responsibility which is understanding your business. You need to understand what your business

problems are and then you need to find the best possible solution to your business problem

right. So again data scientists combine lot of data. They go on computing and all the technology

they use is at the end of the day helping them to gain very much valuable insights you

know by digging by leveraging all the business levers available to them right they will be

pretty much making use of everything that thrown at you to make sense and then do analytics

on it. This requires a lot of skill and and this

just look at it. So pretty much you have to combine your data

to compute the data you have to go on using the technology right. It sounds so complex. I do not want you guys to be overwhelmed by

this. When you put it down on paper this looks complex

but then at the end of the day it is a very simple concept and stick to the end of the

video and I show you how you can become a data scientist. So coming back you know advising companies

on data potential and gaining new insights and then transforming these insights into

business goals is very important guys. Developing solutions that improve business

performance through advanced statistical analysis global data mining we just saw that person’s

resume. Data mining was a big part of that resumé

right. And then data visualization techniques as

well. last one I promise. So the last roles and responsibility that

I want to discuss with you is strategy and designing that you need to know how you can

interact with your teams and customers right. Ask a lot of questions as a data scientist

build relationships because when you ask a question you will get to know the details

of how you can work faster. And then when you all pretty much build relationships

you can understand the context of the roles in your organizations right. Take a second to give that another read again

and this would make much more sense to you. Now that you have been enforcing this part

of interaction a lot so I hope you guys did that anyway. Up to you know a good way to learn is to interact

with people we already know that. And then even let’s say if you want to know

how you can retrieve data you will be know which table you need to go to which person

you need to talk to in real life. Here the table we’re talking about is a database

table. But then yeah you will actually know which

physical day will you need to go to to get your data from your big data team and so much

more guys so you can answer as many questions as possible just by finding out where you

can look guys coming to the most important part of a data scientist job is a data scientist

salary guys do not be dumb founded by these numbers these numbers are just the average

and the number can be lower or number can be very high from the numbers that I’ve quoted

here in the United States of America. The average salary for a data scientist is

one hundred and twenty American in hundred and twenty thousand American dollars. And in the in the country of India and pretty

much is 16 lac Indian rupees per year. So this is per annum rate. So these numbers I always get an audience

where you people are asking me to give me numbers in Indian rupees and in American dollars

well guys to see your curiosity zero. The average numbers. I personally know people walking in Fortune

500 companies who make a lot more than both. The numbers that you see on the screen. But then I just do not want to give you false

numbers tonight. So this is the average I do like it stands

right now a hundred and twenty thousand dollars and 16 lac indian rupees. And then this is for many of us is a big number

and we can be happy with this. Well we can be happy with this knowing that

day in and day out we are going into office. Are not on the way to become a good data scientist

or we are the scientist already. And then we are loving our job whenever I

need a data scientist or data analyst from very good company. Those guys love their jobs and you know they

do not complain one bit about their job. They like anirudh it has been amazing we love

our job. you know we love coding and we love that we

are being paid handsomely for doing what we love right. Guys again this number again speaks speaks

huge volumes for me and I hope it does for you as well. So this will bring you to a very curious thought

of who might be hiring in this world right. Let me first begin with India guys with Unisys

we have fractal analytics we have IBM we have enstant young they have edgeverveof edgeverve

was an infosis company we neosigma in a very good data analytics company here in Bengaluru

again we have OlA you have Oracle Siemens Truecaller all these guys are on the constant

hunt for data scientists around the world guys that are ready to pay you the amount

you have to show them that you are skilled enough to get the job and they will hire you

irrespective of your location irrespective of your pay and then coming to the rest of

the world or mostly the United States of America. Well Instagram’s hiring LinkedIn’s hiring

Pinterest Viber WhatsApp Evernote Android ah Twitter Tumblr wechat Facebook Vimeo YouTube

Amazon Oh I’m just trying to name all these logos I see on the screen and do not be in

be fedeled by just the logos on your screen each and every one of these companies is paying

over one hundred thousand dollars over 15 lakh Indian rupees to hire the best data scientists

out there guys again I’m pushing on it you just need the right skills and you will land

an amazing job as a data scientist with all these guys Skype is hiring and adobe is hiring

Best Buy is hiring Wal-Mart Apple BBC Google well throughout the world again let me push

this fact again you might be wondering well these guys are hired in a America not in India. Well no if you guys have the right skills

if you are trained first certified and the right thing you can push for all of these

jobs and you will get there guys this is the guy you know this is pretty much it you’re

seeing your screen bombarded with employers around the world who just want to hire are

data scientists right now we had data coming in every second from every corner of the world

but the problem is we did not know what to do with it. So we had a lot of data with us but we were

not trying to find out any insights from the data or in other words we were not making

any decisions based on this data and this need to understand and analyze data to make

better decisions is what gave birth to data science. so now we will go ahead and look at the scenario

where data scientist needed. So this is MAT CEO of a telecom company called

black space in the last six months the company has been losing its customers to its rival

company named white space and the company goes into a loss. Now Matt wants to understand what is the reason

behind this So Matt calls up David who is a data scientist working at this company. He asks David to analyze the entire customer

data which they have and come up with solutions to increase customer retention. So David starts out by understanding the data

first. Then he goes ahead and build some algorithms

on top of this. And finally shows the results in the form

of beautiful visualizations. So guys the need of data science comes in

and we have to understand the data to find interesting insights and make informed decisions

on the basis of this data. So the simplest explanation of data science

would be applying some sort of scientific skills on top of the data so that we can make

this data talk to us. Now what do we exactly mean by applying scientific

skills on the data. Well precisely. Data science is an umbrella term rates and

combines multiple scientific skills and techniques. So some of the techniques which data science

comprises are. Data visualization data manipulation statistical

analysis and machine learning. So when you combine all of the scientific

skills into one what you get is nothing but data science a quick info guys In case you are looking for a full fledged

course in data science please do check out Intellipaat Data science architect Masters

course that is co created in association with IBM. This course has all the required skills to

master the concepts of data science and become a data science professional. Link for the same is available in the description

box below. please do checkout. Now let’s continue with the session. Now let’s go ahead and have a look at these

different scientific techniques in brief. So we’ll start with data visualization so

data visualization is an essential component of the data scientist skill set. So when simple terms data visualization can

be said to be an amalgamation of signs and design in a meaningful way to interpret the

raw data through graphs and plots. The next technique in data science is data

manipulation. So normally the raw data which we get from

different sources is extremely untidy that is drawing inferences from this data is extremely

difficult. So this is where data manipulation comes and

these data manipulation techniques help both to change the data and make it more organized. So that finding insights from the data becomes

easy. Then we have statistical analysis. So simply put statistical analysis helps us

to understand the data through mathematics that as these mathematical equations help

in understanding the nature of the data set. And also to explored the relationship of the

underlying entities. And finally we have machine learning which

makes up the major part of data science fully understand the concept of machine learning

where this example here. So what you see in this light. What is this exactly. It’s a car isn’t it. And how about this. Well this is a car two. And thus again a car. Now how do you know all of these are cars. Well as a kid you would have come across a

picture of a car. And you would have been told by your kindergarten

teacher or your parents that this is a car and your brain learned that anything which

looks like this is a car. And that is how our brain functions. But what about a machine. well like the same way as fed to a machine. How will the machine identify it to be a car. So guys this is where machine learning comes

in. So we keep on feeding images of a car to a

computer where the label car under the machine learns all of the features associated with

the car. And once the machine learns all of the features

associated with the car. We will feed it new data to determine how

much has it learned. In other words raw data or the training data

is given to the machine so that the machine learns all of the features associated with

the training data. And once the learning is done the machine

is given new data or the test data to determine how well it has learned and that is the underlying

concept of machine learning. So guys just to sum it up. Data science is an umbrella term that’s comprised

of different scientific skills such as data visualization data manipulation statistical

analysis and machine learning. So guys these are the stages involved in the

life cycle of our science data acquisition data processing model building pattern evaluation

and knowledge representation. So let us go ahead and understand each of

these individually. Let’s start with the first step which is data

acquisition. So we already know with our data comes from

multiple sources and it is present in multiple formats. So our first step would be to integrate all

of this data and store it in one single location which is a data warehouse. Now from this integrated data we have to select

a particular section to implement a data science tasks. So this would be all about target data. Once the data acquisition is done it’s time

for pre processing. So the raw data which you have acquired cannot

be used directly for the data science tasks. This data needs to be processed by applying

some operation such as normalization and aggregation and after pre processing is done and it’s

time for most important step in this data science lifecycle which is model building. So here we apply different scientific algorithm

such as linear regression K means and random forest to finding interesting insights. So after we build a model on top of a data

and extract some patterns it’s time to check for the viability of these pattern that is

in the step. We check that all the information is correct

useful and new and only if the information satisfies these three conditions we consider

the information to be valid elsewhere discarded once the information is validated. It’s finally time to represent that information

with simple esthetic graphs so you guys are summing up the data lifecycle comprises of

these steps. Data Acquisition data reprocessing model building

pattern evaluation and knowledge representation. So one such application of data science application

is chatbot. So would these chat boards are automated boards

which respond to all our queries. Now I believe all if you must have heard of

Siri and Cortana So these chat bots are perfect applications of data science and these chat

bots are also used across different sectors such as hospitality sector banking retailing

publishing and another very interesting application of data science is the self-driving car. So will the self-driving car is the future

of automotive industry a car that drives by itself without any human intervention is just

mind boggling isn’t it. And all of this is possible with the help

of data science. Next a sentiment analysis for sentiment analysis

is an application of data science which helps in understanding that emotion or sentiment

behind something. So let’s say elections are going on and you’ll

want to know how would a particular party fails the election. So just by analyzing the sentiment of the

people you can understand when the performance of the party in the elections and other application

of data scientists images tagging. So I believe all of you have Facebook accounts

now whenever you have it on a person’s picture Facebook automatically tags a name to the

person. And this again is done with the help of data

science. Let’s get start with NumPy. So I believe you guys already know like what

Python is capable of how powerful it is so we can use python for all sorts of things

right from digital development web development and data science. We can do anything and everything with python

in the programming world. Next thing is Python builds up on its functional

modules. So it has all the all code constructs of programming. There are three types of programming that

we have structural. Object oriented and functional so it can if

it is capable of doing everything. The next is when we work with anything in

Python. We have a fancy thing called package in python. So those packages are available for free in

the market. It has a lot of built in functions and built

in things that will help you guys to write many many codes easily without waiting for

the detailed algorithm and all So in that way these packages are really helpful. So what will be will we do. We will be starting with this Numpy for today. Okay. So after that we will see where we know that

we will go for SyPy so these two are basic packages for data scientists. So numpy. What it does it is used for mathematical and

logical operations on that is and it provides feature for multidimensional. arrays also so again when we tell arrays Python

doesn’t have any array. It is built on a list on it. So basically when we go to numPy when we go

for indexing slicing and all those kind of stuff it will only be the list. Everything will be based on the lists. So that’s how it goes. Okay so that’s what we will start with. So NumPy will have a lot of functions which

will help you to I mean play around with the arrays of it. Okay. So that’s how it works. So now what we will do we will just cover

the basics of numpy and then we will go for the coding examples and All and thats how

we will be planning for the day for that let me open up a new console for you guys So. That it gets easier for typing of the codes. Okay. Okay so go ahead to the ppt now. So first of all we will see how to create

an Numpy Array. Okay Numpy Array once again I will repeat

it is just Python list it is nothing more than that but it has some added features along

with Python lists. It has some added features so that’s why numPy

is used again due to some extra features that we don’t get in a normal lists in Python So numpy can support 1d 2d both of the banch

of array okay. And I think you might be using this Anaconda

version of Python so but that will be coming along with Pandas having NumPy version of

restart. Numpy will be pre installed in the Python

versions. Okay. I mean Anaconda python. Now when you use a package right what we do

we import the package we import the package with something like we with the Syntax. It can be any package okay. Alias can be anything. So what is the syntax Syntax is import then

in that package name then that will ask you what it does to assign an alias. Otherwise you would need to type the entire

thing throughout the program and then we will have alias name of NP okay. So that’s what it was. Okay so now when I say NumpyArray it can convert

anything any any python data structure to arrays. So that so it works. Okay. So if a show you like this. why it is taking time now Yeah so C it has converted both these things. So the first one is Python list. Second one is a python double right. So when we pass that argument that that tuple

or list to apply a numpy array function it converts them all to a numpy array. So that is what this returns and what ever

to be provided as you see the outputs are starting and ending with square brackets. So that means it is already converted to a

list. Now we can perform various functions and we

can perform various functions in numpy. Yes so once we have numpy array created which

can be editable. Because it’s a list now it’s not. Anymore a tuple So that’s the way it is editable. And numpy doesn’t come by default with Python

if we want to use it. We need to install it. So let’s say you are not using Anaconda and

you are using this kind of Python user interface then what you need to do you need to install

it using Command prompt install. Sorry. Yeah. And again and again we’ve been strong number. So it will find it and it will install numpy. I don’t have this preversion set so thats

why it is not working but it should install it. OK. So that’s how it works. That’s all we can use numpy in the end this

kind of functions also. Now we can have 2d arrays in Python also like

2d arrays in numpy also. So how we write it we write it like this. So we just need to pass 2d arrays 2d arrays

means to let’s say we can pass two lists. So it will be. Condemning it to. 2d array. OK so it will be changing ainto 2d array. So that is how we write it in Numpy array

that is how we create arrays in Python. So that’s how we do it. So now next will be how where it is and where

it is advantageous other than using basic python List or python like that. I can list all Python right. So by number you were changing that. Yes so we are changing data type from mutable

to non mutable this is the other one. Right. Next is ND array object. OK. So in numpy as I have shown you to 2d arrays

right it can go up to n and then can have any number. So in the real world if you ask me we don’t

use more than two Max to max 3d arrays but yes it can go up to that. So like that it works. So next is and next is like why why we use

a numPy arrays so and numpy this ND arrays will have all the items of same type. Okay. So it can’t be one of them is int and one

of them is string list. It has to it all all of them has to be entries

like that. So items can be accessed using zero index

as you know Python arrays are Python lists also starts from zero indexing rate. So here also it remains the same property

it has zero index so if we go for a numPy area then if we want to see the first row

it will be like this. So it will give you the first row and this

will give you the second row right. Similarly for columns. If you want to see first. Sorry. If you want to see the first column. You need to do this right Sorry. So you need to do this so you will see the

entire values. If you need to see that only the first column

then you need to do it do it at this. Like that. It works great. So that’s how it will be working now for 2d

arrays. I will be coming. Okay. I am working on 2d arrays. So that’s how it works. OK. So we will be coming to that in a few minutes. So 2d aarrays I will show you. So for others what was the names A and B 2d

arrays it is I will be showing you. No not an issue. So for arrays if you see 1d array it will

give you a zero. That will be 1 a 1 will be 2 like that. And if you do want to go then it will be printing

on every single element because the right hand side index is always left right and that

index it is not up to it goes up to that. It is not including that right right hand

set index so it has has two elements. Now if we do this then it will print all. If we do this that also will mean the same

and it will be in the same way it is pretty basic pretty much like the list that we have

in python. Now numpy arrays are beneficial because it

picks up the same size of blocking the memory for all objects at stores. This part we will see a comparison in the

later little half of the section and then we can understand like whether it is beneficial

rather than storing it in a classical list then numpy array Now next is if you go in to check the individual. Yes right. So it will begin colon n-1 that is how the

right hand side the we will always be excluded like that. OK. Next is NumPy Array initialization right. So how do we initialize NumPy Array as we

have already seen. Right. How do we do that but still. This is how you do it. OK. So initialize means what we want to do is. We don’t want to place any list in List in

there Okay. But what we want to do we want to have some

by default values. OK default arrays we want to pick it up. But let’s see how we do it. First one is we will I mean if you guys are

familiar with the terms like masking and all that is for image processing and for data

processing we will do that a lot of things we will do that. OK. So a lot of time we will be. Lot of time we will be using the zero based

or one based indexes. OK. So for creating a zero based arrays rate what

we need to. Come on what we need to do we need to have

some some sort of like some shape of a zero arrays. OK so that’s how you do it again. That’s how you do it in NumPy So what do you

do. You write. NP.0 Then you specify the when you specify

the dimension of that. Three by four means it will have like three

rows and four columns so first one is off so you can think it like this it will be NP.0

then row comma column. That’s all what you need to pass it that that

this kind of tuple you need to pass it to the python arrays OK. So that’s how it works in Python. I mean NumPy. So that’s how you print 0 arrays in NumPy. OK. Next is if you want something with the interval. Right. So how you do. Right. Like this. NP.array within Bracket we pass. So it will print you thjat another a new arrays

or what we are doing. We are sorry.n here. What we are doing. By. Array. So in this what we are doing we are we are

right. It’s arranged So it’s a range actually. OK. So what we are doing we are passing three

variables. First one is the starting number. Second one is the ending number. And last one is that the line is the interval

that we want to print. So from 10 to 25 we are having five. Are we one one lists to we have five separated. So first one and second differentially five

like that. So that’s all we have. So we have three numbers 10 15 20 and always

remember right and will be excluded. So 25 won’t be taken up till 20 we will go

till 20 we will print it out. OK. So that so this kind of arrays are getting

printed out and in the NumPy Now this is the same thing again. Next thing is this A range is done right now

let’s say we want as we want to spread some points over a straight. OK. So how do we do it. We want to what I mean what I mean is if you

think of it 2d plane right like x and y plane if you think of and we want to spread some

points along that line. So what we will do then like we want some

points between range A and range B. OK so here we are giving intervals right. So we need to calculate the interval we need

to calculate the point difference and all then we need to provide it here. So that’s how it works. But we don’t want it. We want it other ways. We want it to spread along on Nireline like

this. So for between five and 10 we were we want

six numbers. It is almost same as NumPy. It has a big difference. So how it is different I will show you. OK. So one doubt in the arange the last of the

N number. Can we put as 19 instead of 20 to the last

exam. Yeah. Then it will print single number no no no

in the second argument. yeah yeah we can. It will go to like in only. OK thanks. Now you can put anything so the logic is when

it reaches the end number right. With interval if it goes past or if it goes

till the last number then that number will be called. I mean that number would be excluded right. So let’s say 18 plus two will be twenty eight. So 20 will be excluded. Okay. So that’s how it works. OK. So for 19 18 plus two is 20 these when you

is 19 will be excluded right. No no no no. Nothing is there to print it out. So that is why we are not in the loop so I

wouldn’t go in that. Okay. Next is this leanpace. leanspace and at any rate. So arrange what it does it takes in. Two num three numbers for start number and

number. And. So we what we do in arrange we have to use

three numbers as a parameter. We what we take we take n numbers the start

number end number and the interval and we print out the values based on the interval. So from start numbered till the number we

print out the values based on the interval. Okay. But in Lynn space what we take we take three

parameters again and what we have in that we have like we have a start number end number. And then we take in number of points in the

number of points in there. So if we give you a start number end number

then if we split it into 10 points then we will get 10 points between that start number

and end number. That is the difference between lean spaceand

arrange and all these functions. We will be using heavily when we use to go

forthe data science okay because these things we will need and they will see how do we do

that. The things we will be discussing when we discuss

about the data science concept. Okay. Now next is how do we do array with the same

number. Okay. Next is how to create an array with the same

number. So this is same as same for same uses like

that. Like for zeros iight formats for. Carving up some masks like that. Okay. So if you use this keyword. Then if you pass some values. Along. If you pass some values then it will give

you that number of the damage dimention it will take care and it will get and it will

give you that kind of an array so row and then column and then it will be picking up. So NP.pull it will take up. A.first the tuple tuple for row numbers. Column numbers and it will pick up the number

to fill that array with the. Number to fill like that. This thing works this full function works. It can be anything. Okay. It will give you array full of the kind of

numbers of any any dimension any dimension can be given to any real world number that

can be given. I’m sorry any integer number can be provided

to them by dimension so thathow this full function will work. Okay. Next is to randomize an array again for I

mean that masking only. So what we do Let’s say we want that three

by four for Matrix to be randomized. So it will give you some some here and there

numbers of random numbers. So that will be and that those will be structured

in a matrix form. Yeah but I mean you pass and that rate will

be organized. So random as this thing. Okay now we go to how do we access this NumPy

Array. Now let’s say we are creating two arrays. How do we access it. Okay. How do we see what’s wrong with the array

and what not. Let’s say I am creating upthe 2d array. Now If i do. So that’s how they shift function. So again that’s how we can access a shape

of an array. So shift function will return below. row number and column number. So a dot shaPE. it turns tuple.row and coloumn Like that. Okay so it will be returning like this now. The major next thing is what is the use of

this shape function. Okay so this is a tuple but we can change

the values of the tuples. This will change it. We can access the shape function of it. And we can change the values. Even. Now we can access like our come. Come on. We can access this. Yeah so we can access it like this okay. Come on. I have not given print. Okay so we can access individual elements

of the shape tuple that is being returned like this. Okay. Like we use it like we access normal tuples. So we can what we can do with this. A dot shape. We can go. We can get the tuple. I mean tuple for row number and column number

as an output. We can change the axis. Change the shape of the shap of the matrix

with that with the shape function and we can access individual arrays let’s say you want

to see how many rows are there. It as a trade. You can use it. You would want to loop through all the data

sets all the rows of your data sets using a for loop. You don’t want to go for a map and all thats

why when you use a dot shape and for printing a. That’s how you use okay for print a. What you do you. You will just. I’m sorry For shape zero. That means you are accessing individual elements

of Tuple right so zero will be giving you the number of rows and one will be giving

you the number of columns like that. Okay. Okay. One thing you guys are learned with. I mean you guys know about lambda functions

right. It was covered. Yes. Correct. But just to want to go back to the zero index

zero index it to return the number of row correct. Right. If you keep comma on another zero then you

click the number of column. Is it working. It’s not. No one shape is always give the column and

rows yaar that zero will give the rows and one will give the colomns that’s it So that

is tuple basically right. Can you please do that. So tuple how it works. Number of elements in Tuple we will we will

have a one D array right for one D arrays Can’t be having to two indexes. OK so it will be zero to like that. But it one day one d tuple or one day array

what do whatever you it. OK. So that’s how it works. OK. Now we can have several more examples of data. I mean this shape function but I am not going

into the detail but you need to remember one thing you need to max the number of elements

in the Tuple right we have six elements right 1 to split it 2 to 3 it won’t work because

the size of that is that total of six right. So we need to have a multiple of six only

that so we can break that so we can reshape that’s what is about this shape function OK. Next is how to get the total number of columns. OK. So again I am having 10 functions and I want

to see the size of it. So it will give you 24. As you can see there are 24 elements in the

array. OK so that’s how that so this size function

works and this is another syntax of in NP dot arrange. So here is what it takes it takes some number

of. Number of values. Number of points needed. These. Numbers. Between 0. till That numbers. OK. So that’s how the another syntax of arrange

works. OK. So for size function it will give you the

total number of elements in an array. So that is what size function works for. OK. Next is dimension of array it is more or less

same like this it is more or less the same thing like that like shape functions. So I will put it right in there and only difference

it can be modified. So where were we we were with this endim right. So how it will work endim will return you. It is not. I mean it is a bit different from shpe shape

returns you the exact shape of the array like the number of rows and number of columns. Right. But endim will return you the number of dimensions

of the array. Okay. So if you see the endim then what we have

we have two has return right. That means it has total of two dimensions. 3 and 2 2 dimensions right. So that’s what ended returns. And endim will return you. The number of dimensions of array. So that we need to. Remember. OK so that is a word to remember so endim

returns to the number of. Dimensions. Again it is not shape but it is a dimension. OK. Now D type is the array data type that we

have already. OK. Next is D type OK. D Type is to give you the type of each element

and the array. And then NumpyArray will give your type of

all the elements an NumPy Array cannot be heterogeneous. It has to be homogeneous always. So NumPy arrays will return you. I mean the the order the homogeneous arrays

so when you do a D type function on an array it will return you the number of dimensions

of that. I mean sorry the database of each of the elements

in array. So here we have 24 24 int numbers right. So that’s why we have int 32 as a return type. Yes. So that’s how it works. OK. So that is what is about this d type function

next is how do we use it for maths calculations. OK. So any any doubts till this part till this

d type. Now if I show you something more here. OK. Next as I have created. Now aray with like float numbers so you see

that arrays are D types sre printed as to 64. OK. So you have you’ve got an array of I mean

the D-type as float sixty four. So that so this d type function works. OK so. NumPy as the name suggests it used it is used

for linear algebra. So numpy is used for no. mathematics or linear algebra. So you won’t be really having string arrays

in Numpy. OK so you won’t be having it. That is not provision to a Numpy. It’s only useful linear algebraic operations

and for that we need in. Integers and a floats Right. nothing more than that. OK. Next is a numPy maths. We can do summation division multiplication

and all those kind of here and there stuff with this numPy. Again how do we do it. OK. Let’s get started with a single array. So this is how numPy submission function works

and it can add up to different things. Okay then some will return you some of the

elements or it can be you sum up two arrays. OK some can do anything. Anyone. So sorry. So some can detain you either some off all

elements in a Python array or it can return you the some of elements of two mattresses. So matrix multiplication matrix addition all

this logic will be implemented in there. It won’t say why it is not working. Sorry I haven’t put it in a list OK so now

what it will do it will take up to list it will add up all the elements and it will return

you the out. OK so 5 10 2 3 that means 10 15 and 2 3 5

so twenty eight and subtract it will take to arrays and it will subtract the results

and it will throw it up for you. So that’s so this abstract subtract and numPy

dot sum function is used for summing up and the finding difference between two mattresses

and NP.sum has a few flares in it can do a few things so we will discuss that. So let’s say we have seen how it takes in

two lists and eight. It sums you up that result and show it to

you. Now if you specify an excess parameter then

it will do the sum based on the rows or based on the columns. OK. We’ve been told A and B for you. Yeah. So see a and b are 5 10 2 3 8. If we do some of some with exit 0 that means

it will do your column wise some. So that means 5 2 7 10 3 13. Okay. And if you will for row wise sum then it will

be X as equals 1 so it will return you fifteen and five right so that so this will. work this will work row wise some that. That’s how it will work. So that’s what is the. So that so this functions work. that’s how this numPy arrays. Arrays some function well. So that’s how you can get the.

Data science life

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Please make video on "Data mining "

Best educational channel in YouTube

wowwwww thanks a lot.:)

Does this video has all the data science concepts?

Very good tutorial

I am a big follower of this channel and have learnt a lot from Intellipaat. Keep doing the good work.

sTILL THERE ARE PEOPLE FOR WHOM EDUCATION IS NOT A BUSSINESS . HARD TO FIND SUCH PEOPLE.

THANKS.

Hi, Awesome as always. Please upload django tutorial on how to develop apis without using django-restframework.

Hi I am a Collage student and I know c++ and little bit about python means I can build small projects on python.

But I want to learn Data science so please tell me should I start this course or I focus on python first.

I am a sales guy can I become data scientist?

Good going intellipat

Sir aap log to bahuttttttt badhiya aadmi nikale…thank you so much sir….ππ

π₯π₯π₯Following topics are covered in this video:

01:45 – Who is a Data Scientist?

03:03 – Where do Data Scientists come from?

05:35 – What does a Data Scientist do?

08:55 – Prerequisites to become a Data Scientist

13:12 – Data Scientist Roles and Responsibilities

19:20 – Data Scientist salary

21:05 – Who's Hiring Data Scientists?

22:52 – Need of Data Science

23:19 – Where is Data Science Needed?

24:14 – What is Data Science?

25:15 – Understanding Different Techniques

28:09 – Life Cycle of Data Science

31:24 – Numpy

33:21 – How to create Numpy Array?

36:50 – Ndarray Object

40:00 – Numpy Array Initialization

46:31 – Randomize an Array

47:00 – Numpy Array Inspection

53:33 – Numpy Array Mathematics

01:09:24 – Numpy Breadcasting

01:09:40 – Indexing and Slicing in Python

01:15:25 – Array Manipulation in Python

01:30:15 – Splitting of Arrays

01:36:31 – Advantages of Numpy over List

01:45:46 – Python Tutorial for beginners

01:46:44 – Introduction to Pandas

01:49:56 – introduction to Pandas

01:56:20 – Pandas vs Numpy

01:58:27 – How to Import pandas in Python?

01:45:46 – Python Tutorial for beginners

01:46:44 – Introduction to Pandas

01:49:56 – introduction to Pandas

01:56:20 – Pandas vs Numpy

01:58:27 – How to Import pandas in Python?

02:00:00 – Data Structures in Pandas

02:00:29 – What is Series object?

02:03:00 – How to change the index name?

02:05:16 – What is a Data Frame?

02:07:28 – How to create a Data Frame?

02:125:53 – Merge, join and concatenate data frames in Pandas

02:32:34 – Analysis functions

02:37:10 – Cleaning function

02:45:32 – Manipulation

02:50:10 – Sorting the Data

02:51:00 – Filtering

02:51:55 – Data Visualization

02:56:00 – Quiz

02:58:35 – What is Data Visualization

03:04:01 – Anscome's Quartet

03:13:34 – Data Visualization libraries

03:15:36 – What is Matplotlib?

03:19:24 – What are the types of Plots?

04:39:56 – Introduction to Machine learning

05:03:40 – Linear Regression

06:23:43 – Logistic Regression

06:44:30 – Spam Email Classifier

07:28:06 – Logistic Regression Example of a person having the heart disease or not

07:56:00 – What is Classification?

07:56:47 – Decision tree

08:34:00 – Understanding Confusion Matrix

08:45:10 – Naive Bayes classifier

9:03:17 – What is Clustering?

09:34:27: Data Science project 1

10:33:49 – Interview Questions

11:30:27 – Data science project 2

12:11:31 – Quiz

12:11:51 – Data Science Interview Questions

Thank you intellipert

thank uβΊοΈ

Can you guys make a separate video for numpy stacking as at 1:28:58 the whole concept is getting mixed up. I think the teacher was not very familar with that topic also plz use some other IDE(Which has a black background) or big font as what you guys are typing is also not getting visible clearly at 720p also.

Sir you are very good teacher I want to neads s this coarce

Ethical Hacking Full course

This i want to learn from Intellipaat

nice all in one great

need maths and probability video please

Good teaching

I have a question about data science how I can contact to you

I need assurance about job

I am ready to hard working

Through a intellipaah i fund right direction for data science

Thank you

tq so much intellipaat…we learned lot from u…we expect more from u..tq so much once again

This Video must be in parts … Please upload in parts for daily time table.

Thank you for the video Sir

Is it very hard to become a data scientist from Mechanical engineering background?

Pls upload a detailed video on React Native….

Noobs comments on YouTube be like sir can u do the same course using Linux computer

What are the prerequisites for this course sir?

sir make comprehensive videos of math and statistics for machine learning

I am from commerce background do I would be able to do data science and if yes how much time it would take to learn data science

Where do I get that dataset for that customers record????

Want to know aap orvedureka rk hi ho? Dono best ho

I am Doing Co Engg ;

I don't know c lang' In deep.

Should I learn It in 2019 Or go for

any other languages

Respected sir, I am pursuing MBA finance. I want to learn Python for finance and marketing domain. Can you help me out in that?

Please make English subtitle

Most underrated channel ever please like for their hardwork

Sir plz Hindi me bhi video banaoπππ

Can you please share the code snippet and dataset used in the training. It will be very useful.

I don't know phython.. Nd i m manual tester.. Want to switch data science… How to start… Nd how much time it will take….what skill needs for data science

Thank you sir!

What is the difference between data science and information technology ?

Sir I just want to know whether I should go for data science or not. ..is there a future scope???

1like for ur..Hard work Bro..β

I have 10 years of experience in Testing (Selenium+Python and Java), How difficult will it be to switch career in Data Science (with no experience) and getting the same pay I am getting; presently?

commerce background vale ni kr skte kya ye course

GitHub link please

Can I do well in Data Science if I have not Computer Science and IT background ?

Can commerce student become data scientist…if yes how long it may take for commerce student to learn

Nice Manoj sirπβ€οΈ

Chutya

Iam a student of datasciencee tq sirr

Hi sir can i please know how real time object detection and recognition can be done and how to train the existing pretrained models pls…

Can we got job to learn from your channel

can i know the order of becoming of data scientist

1. data science

2. machine learning

3. deep learning

4. nlp

5. statistics

am i correct? if not? correct me..!

thank u

Is all courses are full course

And certifite also

Useful

sir i am graduate in mathematics plz tell me from where or how i can start my journey to become a data scientist

In Hindi video

Sir, I want to learn basic learn of computer. Like DIPLOMA IN COMPUTER APPLICATION, ADVANCE DIPLOMA IN COMPUTER APPLICATION. any course you have on youtube

Does it requires coding knowledge?

data scientists is what I ever wanted to be

Being civil engineer is it possible

I want to join this course ,8750644292

can i get job learning this full course

I am from mechanical background…is it possible for me to go for it?…..like how much time it will take in general to understand?

MERN STACK

Sir jo aap ajenda padha rahe hai o agenda hindi me padhaiye

Sir please

Iam b.tech ME can i became data scientist?

Yes I am civil engineering I want to shift career in data science but I was poor in computer programming subject in my academic …will it be good option to shift & how.muvh time will it take to learn data science .

Nice

I have done B. Tech in Mechanical. Can I do data scientist course?

Sir I was completed Intermediate is I am eligible for data science

Is good for mechanical engineer

Sir how to get a job as a fresher there is no job for fresher everybody wants 3-4 years of experience even startup also ππ

Nice bro thanks yu

Please add subtitles, because I am deaf person.

Sir i need certificate, please suggest me something where i can get and the course should be for beginners.

What is difference between data science and data scientist or both are same with different name

Plz tell meπ

Which video is best to learn data science from ur channel ?? Whether this video or another video of 11:14 : 00. Hrs .. Can u suggest ??

Can a mechanical engineer who has zero knowledge on software can learn data science

In the linear regression topic, we got R**2=0.36, and then you mentioned that we should rotate the line until we get R**2 value close to 1. Is there any calculation to obtain the slope to which we should rotate upto.. or else it's just a trail and error method ?

What's the eligibility criteria and age ??

Good information…

You guys are doing a good job, no doubt on that. Can you guys please make a complete and full video on MERN stack.is there a need for me to know python before doing this course?

I am working on MNC as Oracle Database Administrator with more than 2years experience..Is this useful to me in anyways