# Data Visualization | Data Visualization Python | Intellipaat

November 8, 2019

hey guys welcome to the session by intellipaat so just a quick info the session would also be simultaneously

broadcasted on YouTube as well so in this session we’re going to learn how to

do data visualization with Python so let’s how quick glance at the agenda

we’ll start by understanding the importance of data visualization and

then we’ll see how to do data visualization with the matplotlib

package so simply put data visualization is the art of representing the raw data

in the form of beautiful and ascetic graphs now let’s say you’re working on a

huge dataset which comprise of around thousand columns and 1 million rows now

finding insights from this data is extremely hard so this is where we can

take the help of data visualization to find interesting patterns from the data

so we have this data set over you and this data set is represented in the form

of images now we see that these images help us to easily identify the features

of these animals so we have got mammoths over here and with the help of this

image we can see that the size of mammoths is right so sorry for that interruption so

as I was saying we’ve got these images over here and these images easily help

us to identify the features of these animals so this is mammoth and the

citizens of first record and this is saber-tooth cat which represents the

second record and with the help of this image we can see that the size of

mammoth us yeah sorry so there seems to be some

technical issue with the screen so now I hope that the screen is visible to

everyone all right so let me actually start from the beginning so as I was

saying data visualization is basically the art of representing raw data in the

form of beautiful and ascetic graphs now let’s take this example while you’re

walking on a huge data set which comprise off around thousand columns and

1 million rows now to find insights from this extremely

huge data it’s very very difficult so this is where we can take the help of

data visualization to find extremely meaningful insights from the data so

here we’ve got this data set and we have represented this data set in the form of

these images so this is the image of mammoth which represents the first

record and this is the image of Sabretooth cat which represents the

second record and we can easily find insights of these two animals from this

image so we see that the size of mammoth is 13,000 lbs and its aggressiveness is

very low and the speed of mammoth is around 25 kilometers per hour and then

we have got saber-toothed cat so we see that from this image the speed of

saber-toothed cat is 75 kilometres per hour its aggressiveness is very high and

its size is 400 lbs all right so now again let’s take this example so we’ve

got this Anscombe squatted so this data set basically comprised of four files in

total and these four files have these values of x and y now if you look

closely at this data set you’d see that these values are very similar to each

other now let’s actually go ahead and find the sum average and standard

deviation for x and y for all of these four files now again if we look at this

result we see that the values of x and y are same throughout all of these four

files so if you just have a glance at some average and standard deviation then

we might think that the X and y values for all of these four files are very

similar but let’s actually go ahead and plot these four files so when we

actually make a pictorial representation of this data set we see that the

relationship between x and y is very different fur these four files and we

were able to understand this with the help of data visualization so now that

we know the importance of data visualization let’s actually look at the

different visualization libraries with Python provides so python has got mad

rot lip GG plot plot leads your plot lab and see bond and in this session we want

to do a visualization with the my plot lip package so let’s go to Jupiter

notebook and stary I’ll also import the numpy package so I’ll type an import

numpy ass NP right right so I’ll just copy these two values over here and

paste them over you let me also copy these two lines over here right so now

what I’ll do is I will actually add some colors to this line plot so I’ve got the

value for X I’ve got the value for y now what I’ll do is I will change the color

of this so I will use the attribute Co lor and I’ll given the color let’s say

red do this and after this what I’ll do is I’ll assign a title to the plot so

for that I’d have to use PLT dot title and the title which I will be giving

this is line plot and I can also give labels to the x-axis as well as the

y-axis so to give labels to the x-axis I’d have to type in PLT dot X label and

I will give in the x-axis label as x-axis similarly I when you given the

label for the y-axis so this would be PLT dot Y label and the label which I am

giving to the y-axis is y-axis as simple as that right so we’ve customized a line

plot so we started off by creating a simple line plot where the color of the

line was blue after that we made a line plot and this time we’ve customized the

line plot so the color of the line is red and we’ve also assigned the labels

for the title for the x-axis and also for the by axis now we’ll see how to do

a bit of subploting notice we’ll see how to add two

plots in the same figure right so for this what I’ll do is I will actually

have two Y values so let me copy this and let me pass it over here so the

x-axis values would be the same and I’ll have y one over here so Y minus two

cross X plus five then I’ll also have Y 2 so y two would become three cross X

plus 10 and with the help of these values I will create two plots so to

have two subplots in the same faker I would have to use PLT dot subplot method

and this basically takes in three parameters so the first parameter is the

number of images in the rows and number of images in the columns so since I want

two images this would basically mean one comma two so I’ll have one row and two

columns and I’ll put in one over here so this one Basically means that I am

activating the for a subplot now let me go ahead and plot the so plt dot plot

and the values would be X comma y1 now let me go ahead and also at the second

subplot so time will be plt DOT subplot and I’ll given those two

parameters so 1 comma 2 so one row two columns and this time

I’ll activate the second subplot so I’ll add in two over you now let me copy this

piece it over here and I will plot the second graph so this time it is X comma

y2 and I’ll finally show that figure so PLT dot show now let me click on run

alright so now we have created two plots or two subplots in the same figure and

if you look at these values these values are different right so the x-axis values

are same but y1 and y2 are difference over here y 1 ranges from 525 and y2

would range from around seven point five to forty right so this was line plot now

let’s go ahead and see how to create a bar plot so let me just type in bar plot

over here now to update this bar plot let us first clear the dataset so I’ll

go ahead and create a dictionary first and Ill

name that dictionary as fruit so to create a dictionary we’d have to given

these curly braces so we have a key value pair in a dictionary now let me

assign the first key value pair so let’s see my first fruit is Apple and there

are 30 apples the second fruit is mango and there are 45 mangos after that I’ve

got banana and I’ve got around 10 bananas now I will add so now what I’ll

do is I’ll extract the individual keys and individual values and store them in

separate objects so over here I will create a new object and in that object

has names and I’ll extract only the keys from this dictionary so I’d have to type

fruit dot keys and I’ll store the keys and names

similarly I’ll extract the values so I’ll store the values and quantity so

I’ll have to type in fruit dot values all right so extracted the keys I’ve

extracted the values now I’d also have to store this in the form of a list so

let me just type in list and let me cut this and let me piece this inside a list

similarly I’ll do the same thing over here I’ll type in list let me cut this

from over here and let me piece this inside the list all right so we have

created the dictionary now let me just print out names and quantity over here

names quantity right so we’ve got the individual names of the fruits and the

quantity of the fruits over here now we’ll go ahead and create this bar plot

so to create the bar plot we’ll have to type in PLT dot bar and this basically

takes in two parameters so the first parameter would be the names of the

fruits or the categorical variable which contains the names and then we’ll have

the values so values I in quantity now after this I’ll just

show the so plt dot show all right so we have successfully created this bar plot

where we have the names of the fruits on the x-axis and the quality of the fruits

on the by axis so again this plot easily helps us to understand that the major

quantity of the fruits belongs to mangoes and the minimal quantity of the

fruits it belongs to bananas now let’s actually go ahead and also customize

this bar plot so I’ll just type in customizing bar plot I’ll copy this same

thing over here now let me add a title x-axis label and

y-axis label so plt dot title and I’ll assign the title to be lets say

distribution of fruits and then i’ll get the x axis labels so plt dot X label and

the x axis label would be fruits of that I’ll given the y-axis label so it’ll be

plt dot y label and this label would be quantity right now let me print this out

right so we’ve added the title we’ve added the x axis labels to added the y

axis label and let me use the argument color and I let’s say given the or an H

bar right so let me actually change this first plt dot bar so we’ve got this

let’s start off by creating the word data for the scatterplot so what I’ll do

is I’ll have my first data and I and some values over here so 10 20 30 40

50 60 70 so there is a relationship between X and a and they’re all that the

number of points are same let me at the x-axis label again so

it’ll be the same plt dot X label and I’ll just be X now let me add the Y

label so plt dot y label so this would be a and b now let me print this out

right so we’ve got the title they’ve got the x-axis label and we’ve got the

y-axis label now what we’ll do is we’ll change the size of these points right so

let’s see how can we do that so to change the size of these points we’ve

got a parameter which is s so this stands for the size now let me set this

to be 200 and let’s see what happens right so we see that the size of these

points where the relationship between X and a has changed right so this has

changed to 200 now let’s also change the size of these points so I’ll set this to

be let’s say s equals 500 Over here so we’ve change the size of both of

these points over here and it can also change the shape of these points right

so I’ll change the shape of this so over here to change the shape we have this

argument called as marker and let me give in the value three so if I given

the value 3 I have this over here now instead of three let me put it to be one

and let’s see what do we get all right so here this is tilted downwards now let

me given the value two and let’s see what do we get right so this time it is

tilted upwards right so we can have different markers or different shapes

for these points over here right and we can also change the size of this figure

over here to change the size of the figure so about the plot method we would

have to use in PLT dot figure let me just type in PL d dot figure and the

stakes in a parameter whose name is fixed size and I’ll give it a tuple so

let me assign a value of 10 comma 10 over here

all right so over here I’ve increased the size of this figure all right now

after this let me go ahead and start with the next geometry so our next

geometry would be your histogram so let me give in the common histogram over

here now let me again create a list so I’ll give n different values for the

list over here so the numbers range from 0 to 9

so I’ll just given one to my 1 come out 1 and then let me just go ahead and keep

on giving some random values between 0 & 9 7 5 4 3

I’ll again give a 1 so I’m just making sure that there is a greater frequency

of ones over here right so this is all of the data which would be going into

the histogram and to create a histogram I’d have to use plt dot hist method and

I will pass in the data inside this now to show the image I have type in PLD dot

show let me run this alright so we have success we created this histogram so

this basically gives us the distribution of this data and with the help of this

we can easily see that there is a greater frequency of once in this data

and after that we’ve got three we’ve got five right so after one the frequency is

high for three and then there is frequency for five right and there is

not even a single seven or not even a single sorry there is not even a single

eight over here right so that is what this gap tells you right so this is how

you can interpret different insights from a histogram now again we’ll see how

can we create a histogram on top of a data set so what I’ll do is I’ll go

ahead and import the iris dataset first so for this I would require the pandas

library so I’ll type in import pandas as pd now let me go ahead and load the

iris dataset so I’ll type in PD dot read CSV and let me give in the name of the file

which would be IRS dot CSV and I’ll show this in a new object and I’ll name that

object to be iris now let me have a glance at the head of the data set I said

that is I’ll have a glance of the first few records of the data set so iris

dot head and this is our dataset over here now I would have a sorry I want to

make a histogram for this sepal length column from this iris dataset so all I

have to do is I’ll type in PLT dot hist and I’ll given the data so the data

would be coming from this iris dataset so I will type in iris I will give in

pieces over here and let me select the column for which I’d want to make a

histogram so I’d want to make a histogram for the sepal length column so

I’ll just type in sepal length now let me run this let me also just put in plt

dot show right so we have successfully created a histogram for this sepal

length column from this iris dataset so this basically tells us that when it

comes to sepal length of the flower so there is a greater frequency for the

sepal length to be around six point five or 5.5 or you can see that the sepal

length would vary mostly between five and six point five and there are very

very few flowers who sepal length would be greater than six point five right and

those flowers who sepal length is eight those are almost non-existent

all right now let’s actually also go ahead and change the color of the

histogram so I’ll type in color over here and I’ll assign the color to be red

all right so we have successfully changed the color now we can also change

the number of pins over here to you know increase the distribution so I’ll set

the number of bends to be equal to thirty right so we can also vary the

number of pins now let me change the number of pins to be hundred and let’s

see what we get so I’ll type in hundred over here right so this gives us a grade

distribution in the histogram right now let me actually keep this to be fifty

because fifty seems to be the perfect distribution for us right now

yep so this is basically how we can create histograms in Python or with

matplotlib now going ahead we’ll see how can we create a box plot so I’ll just

type in box plot over you now let’s go ahead and create data for this box plot

so I’ll create three lists so I’ll type in one two three four five six seven

eight and nine so this is our first list after that I’ll go ahead and create the

second list so when this the values would range between 1 and 5 so 1 2 3 4 5

4 3 2 1 7 let me just count 5 6 7 8 9 right let me create another list over

here and this would have values between 6 & 9 so let me type in 6 7 8 9 and

there will be 8 7 6 5 so I’ve got 1 2 3 4 5 6 7 8 9 values over here right so

I’ve got my data ready now I will create a list of lists over here so data equals

list and I’ll just pass in 1 2 & 3 inside us now I’ll use PLT dot boxplot

I’ll use this method and I’ll just pass in data inside this again to show the

box plot I will put in PLT dot show right so we have successfully created

our box lured over here so this basically states that there is a greater

distribution for this data 1 right so the values of this data

it basically ranges from 1 to 9 and the median value is 5 so this line which you

see at the center of the box this basically denotes the median value and

the median value for this data is fine then we have this box plot and this

tells us that the data it ranges from 1 and

Costel five and the median value yes three and then we have this so over here

though data points it starts at four and it goes to nine and the median value for

this is seven correct now similar to box plot we also have a violin plot so the

only difference between oh a box plot into a violin plot is that the sheep is

different so box court looks like a box and the violin plot looks like a violin

so that’s pretty much the only difference when it comes to the box

stored in the violin plot so I’ll copy this and I will paste it over here now

the only change is I’d have to make over here as instead of PLT dot box plot I’d

have to put in PL d dot violin plot alright so this is what we have over you

alright so this is the same thing it’s just that these three figures they are

in the form of a violin instead of a box and we can also add a grid to our data

so to add a grid I’d have to use PL d dot grid and then set the value to be

equal to true so this is how we can add a grade to a plot now again let’s also

add title to this so I’ll just put in PL d dot title and let me put in the title

to be distribution of data let me put in the X label so PL T dot X label and let

me just put in this as x axis and let me also give in the why label over here so

PL d dot Y label and this would be y axis alright so we have successfully

added the title the x-axis label and the y-axis label now similar to box plot we

can also add the median to this while in plot so to do that we have this argument

called us show median and I would have to set this to be true over here right

so we have added these lines for the median values so not just medians we can

also show the mean value over here so we’ll show the mean

you we have this argument show means and again I’ll set this to be equal to true

all right so we have added the meanwhile line as

well as the median line now if you look at this while and plaudits very

interesting observation so you see that the value of mean and median is same for

this distribution over here right so the mean value is Phi as well as the median

value is fight for this data right so we are done with while in plot as well now

let’s go ahead then create a pie chart so let me just type in pie chart over

here alright now let me go ahead and create some data for this pie chart so

again what I’ll do is I’ll create a list with the name fruits and I’ll add some

fruits over here so let’s see I’ll add Apple after that I’ll add mango and then

I like oranges a lot so I’ll also type in orange and well I’ve not eaten creeps

for a long time so I’ll also add creeps in my shopping basket right so we’ve got

the first list now we’ll also add the quantity for this so I’ll type in

quantity and let me give in the values for this so let’s say I would want 30

apples and since I like mangoes a lot I had want 45 mangoes I don’t like

oranges so I’ll put in at the b12 and well someone told me that grapes would

make my health better so I would want around 100 grapes right so I assign the

values for this now all I have to do is create a pie chart so for this I’ll type

in PL d dot pie and let me give them the values so this force takes in the values

so I’ll type in quantity over here and then I’ll give in the labels the labels

are stored in root right now I’ll just type in PL d dot show over here and let

me create this pie chart right now isn’t this a beautiful pie chart over here

right so this pie chart easily tells us that the major quantity of the fruits as

grapes and the least quantity of the fruits

as orange you can also add percentage over here salut add percentage we’ve got

this argument Auto PCD and over here I’ll just type in percent zero point one

F F F % percent now let me run this so we have successfully added percentage to

this pie chart so we see that from the entire basket fifty three point five

percent of the fruits are grapes and the least is six point four percent right so

the least amount of fruits available in this basket are oranges now we can also

add a shadow to this pie chart so to add a shadow I have this argument known as

shadow and I just have to set it to be equal to true all right and so let’s say

if I want to highlight one of these slices right we can also do that so to

highlight a slice I have this argument called as explode now let’s say I want

to highlight the slice of creeps because this is the largest piece so I’ll Luis I

will type in zero comma zero comma zero and then I’ll give in 0.1 over here and

let’s see what do we get right so you see that this portion over here is

separated from this entire pie now the zero zero zero basically means that all

of these three slices would be intact together and when I given 0.1 this means

that this explodes from the original pie now let’s see if I just put it to be one

instead of zero point one right so this increases the distance between this

alright so now let’s see if I also want to separate this over here which

basically represents mango so I will put in zero point one over here and I can

also separate that right so I’ve also separated mango from this original bike

right now similar plot which is you know a plot with similar pie chart is known

as a donut plot so everything is seen between a donut plot and a pie chart it

says that the donut plot looks like and the pie chart looks like a pipe

right so what I’ll do is I’ll copy all of this and I’ll pass it over here

now let me remove all of this over here I’ll remove the shadow right so what

your world was I’ll create do plots so this is my first plot and I will store

this in PI 1 now I will go ahead and create my second

plot I’ll name this second pie chart to be equal to PI 2 and over here I’ll just

give in quantity so let’s say the quantity is equal to Phi over here and I

will give in the color over here right so let me also give in the color now the

color has to be equal to white all right so now let me just click on run and

let’s see what do we get over here right so we have this let me remove this over

you we’ve got PI one they’ve got PI 2 now

what we have to do was we just have a white circle and we’ve got this over

here now we’d have to given the value for the radius so I’ll set the radius

for the original pie chart to be equal to 2 and I’ll set in the radius further

second pie chart to be equal to 1 right so now we’ve got a donut plot and it

looks like this right so if you have a glance at this and this it’s pretty much

the same the only difference is this looks like a pie and this looks like a

doughnut now all we did is we created two pie charts over here

the first pie chart is the original one the second pie chart it’s just a white

circle over here right so this is a white circle with a sign the color to be

equal to W and the radius of this inner pie chart should be lesser than the

radius of the outer pie chart so for the outer pie chart I’ve assigned the radius

to be equal to 2 and for the inner pie chart I’ve assigned the radius to be

equal to 1 all right now yep so this is how we can create a

donut plot and then finally we have something known as an area plot so let’s

go ahead and also create an area plot over here

so um let me create some values let me just type an area plot and create a

bunch of values over here so let’s say x-axis it goes from 10 to 90 so I’ll

type in 10 20 30 40 50 60 70 80 and 90 after that I’ll give in values for the

by axis so this would be let’s say 1 comma 1 comma 2 comma 5 comma 6 comma 4

comma 7 let’s see how many values again 1 2 3 4 5 6 7 and then I’ll I can give

it a 4 and I’ll give it a 6 or a 3 4 5 6 7 8 9 alright now let me go ahead then

create this area plot so I’ll just type in PL d dot stack plot so to create an

area plot we’ve got this method known as stag plot and I’ll given the values for

x and y after that I’ll just type in PLT dot show alright so we have successfully

created this area plot over here and this looks something like this so this

area plotters are very similar to histogram right so the area plot and the

histogram both of them show the distribution of your numerical data so

if we look at this we basically understand that you know there is a

creator distribution or there is a greater frequency of the higher order

numbers right if we look at this as well so we can get to know that right there

are two sixes there are there is 1/7 and there are two fours over here right so

this is what this tells us right there is a greater distribution of the numbers

after 5 right so this is basically how we can create an area plot right so

yes guys so this brings us to the end of this session and when this will

comprehensively understable a lot of geometries so we started off with the

line plot and then we learned how to customize the line plot after that we

work with the bar plot now you guys have to understand the difference between a

bar plot in a histogram so a bar plot is used for categorical values and a

histogram is used for numerical values so this is the basic difference between

a bar plot and a scatter plot so after that we also learned how to create a

horizontal bar plot so the only difference between a vertical bar plot

and the horizontal bar plot the same things would be so over here you will

type in PLD dot bar and over here you will type in PLD dot bar edge and then

you will create a scatter plot so we can clear the scatter plot by using this

command PL d dot scatter and then B so how can we change the size and the you

know markers style for these gala plots of that we created a histogram and then

we also created a histogram on top of a dataset and then we went ahead and

created some box plots and while in plots and then we finally understood

about pie charts and area plots right so guys were attending the webinar if you

have any questions you know you can ask me right now right so people who have

questions so let me actually unmute you guys oh hi can you listen to me yes it’s

just I’m sorry I’ll be back in one second yeah hey um so I believe this is Shubham

bra No uh yeah may I know your name yeah um so

I’m hanging over here – so this was a very nice session so it was quite

informative so now I wanted to know something that whatever the donut plot

that you were creating over there right you correct correct

what if I want to add another circle in that one up inside the donut plot how

would I do it okay um so as you see over here to create a donut plot I’ve just

created two pie charts so to add another circle inside this I’d have to add

another pie chart so what I’ll do is I’ll add another pie chart I’ll name

this to be pi/3 and I’d have to do the same thing so I’ll type in PLT dot pie

over here I’ll given the size of this which will be five again and then I’ll

give it a different color so I’ll use the colors argument and let me give this

a blue color over here now the important thing to note over here is the radius of

the inner circle needs to be lesser than this radius over here so this time I

would set the radius to be equal to 0.5 all right so this is our outer circle

whose radius is 2 and then we have this inner circle whose radius is 1 of that

we have we are creating this new circle inside this whose radius is 0.5 now let

me click on run right so we have another circle inside this donut plot seems

correct okay and one more if you don’t mind so yeah today I have the line plot

on top of the area plot um yes you can do that and it’s a very simple step over

here so all you have to do is below this you’d have to type in PLT dot plot

because this is how you create a line plot and then you give them the same

values over here so X comma Y now I click on run now you can also change the

color to this line so let’s say I’ll type in color over here and I will give

you no green color to this right so this is how you can add a line

plot on top of this area blood so does that help you yeah sounds convenient

correct and about the pie chart how do I change the colors in it all right so

you’re talking about this pie chart over here right okay so for this what you’d

have to do is you’d have to use the colors parameter as we’ve been using for

you know other geometry as well and since we’ve got all four slices over

here what I’ll do is I will go for four different colors so let’s say the first

color which I given as black the second color which I given us gray and then I

give in jello and then let me just give in green over here yep yep all right so

this has changed all right so all you have to do is you’d have to check the

number of slices you have so if you have ten slices you have to create a list

which comprised of ten elements if you got five slash you’d have to create a

list which comprised of five elements all right so yeah that’s right guys then

so if no one else has any mood outs what I’ll do was um yeah so this brings us to

the end of the session now you know just for an info over here so in telepath

actually has this Python for data science training course you know where

you can learn all the major concepts of Python and data science thoroughly so in

deliberate has done a lot of research so in telepods has consulted of industry

experts who’ve you know worked as data scientist in top companies and in

consultation with them they’ve come for this Python for data science course

which will you know given 360-degree proficiency in data science

all right so let me actually go through this course content over here so you’ll

start off by understanding the basic concepts of data science you’ll

understand what exactly is the need of the assigned so why did data science

come and then you look at various

applications of data science you’ll understand how its data science is used

in different sectors so let’s say you’re working in a telecom sector or you’re

working in healthcare sector you’ll understand how is data science used in

all of these different sectors after that you’ll understand the basics of

Python so you’ll understand about the different data types how to install

Python and then you will work with looping and control statements and then

we’ve just worked with the numpy package over here right so you will

comprehensively learn about the number package and how to do mathematical

computing with numpy and then there is something else I buy which is built on

top of numpy so over here you’ll see how to do scientific calculations with Syfy

and then you’ve got the machine learning concepts over here right so this is

where you learn how to do data wrangling and data visualization so you’re you

know or you’ll see how to manipulate the data with the help of the pandas library

you’ll learn how to extract the data you will learn how to cleanse the data

you’ll learn how to find the outliers and all of that stuff right and then alright so and then we’ve got the soap

there are signs with Matloff lip so over here you know you just what we’ve done

over here you’ll comprehensively learn how to create different plots right so

this was just a demo here but you know you saw a simple example for each of

these different plots so in this course you will comprehensively learn about

different plots right you’ll learn about different aspects of box plot different

aspects of the own applauded and different aspects of area plot and once

you could with this stuff you will competence we learn about machine

learning so we’ll start off with supervised learning you’ll understand

about classification and regression and different classification algorithms so

you’ve got algorithm such as decision tree random forest naive Bayes and SVM

shall comprehensively learn about this right so these are all the things which

you can learn with this course right so if you are interested in learning about

data signs and how to implement all of these data science concepts with Python

then you and Shore check out the description

which would be posting the link water right so also you can check this over

here now the intel pad also provides CTL assigns architect course so let me just

open that up and let me show you what is so what is so amazing about this course

over here alright so the best feature about this data science architect goes

by in telepath is that this is you know of this course has been created in

consultation with IBM in association with IBM so you would actually be

getting a certificate from IBM when you complete this course now that is very

good isn’t it right so this course basically comprised of 12 modules in

total where there are six instructor-led modules and six self-paced modules so

along with this you will also have multiple assignments you will have

multiple projects so that you know you have more hands-on expertise so in this

course you will basically start up by understanding the top three analytical

tools use in the industry so you’ll start up by Souder physically resides

with our course we will learn how to do our programming and how to do how to

implement your science concepts with our and then you will start with the Python

for a science course but you will comprehensively learn about Python

programming and also how to do statistical analysis with Python and

then you’ll learn data science with SAS where you learn SAS programming and you

know how to do regression analysis and classification analysis of its ass now

once you’re good with these analytical tools you will get on with big data so

you will see how to process big data in real time with Apache spark and Scala

and to be your complete data scientist you would also need to have good grasp

on the deep learning concepts so this entire science architect goes also

provides the ein deep learning module where you will comprehensively learn

about deep learning and how to implement different neural networks

such as convolutional neural network recurrent neural network and so on and

you’ll be implementing all of these different neural networks with Karass

tensorflow and also dia flown right and now once you’re done with processing the

data and analyzing the data you as a data scientist should also be able to

represent this data in the form of you know beautiful visualizations that is

why we also have this tableau desktop 10 right so with the you know when you

learn tableau desktop then you’ll learn how to create stunning dashboards and

you provide these insights to the decision-makers you know and with the

help of those insights they can come up with valuable business ideas and what is

this you also have this self-paced courses self-paced modules where you

learn about statistics and probability advanced Excel MongoDB MS equal machine

learning and Hadoop developer right now one more thing about this data science

architect courses you will have an N to n capstone project so the specialty of

this n to n capstone project does well you know you would need all of the

skills which you would have learnt from these different 12 modules so using the

skills which you’ve learned from these 12 modules you would have to solve this

capstone project now you will get the certificate by IBM once you complete

this capstone project so that is truly amazing isn’t it alright so guys if you

are interested to enroll in the zero science architect course and if you want

this certificate by IBM you know so you can check out the description and you

can also check out this link over here right so do feel free to reach us out

where you know we’re always there to help you out right so yes guys so if

there are any more doubts with respect to what we’ve covered today or with

respect to the courses which are provided by Intel apart I’ll just go

ahead and end this session right so right guys I’m Ann in session for today

thank you and happy learning

Attend this Live interactive session to learn Data Visualization in Python. 🙂

Nice video dude 👍👍👍👍

Plz upload a video on recursion also ✊