Auto news on Youtube Jan 30 2019

Python and R are the two most commonly used languages in data science and

nowadays, most of the fresher's get confused, whether they should use R or Python to kick-start

their career in the field of data science domain.

Hey Guys! This is Shubham from Intellipaat and in this video, I am gonna tell you the

long and the short of both of these topics. So, without wasting more time, let's get

started. I am gonna start off with their basic definitions:

Starting off with R- R is a programming language made by statisticians

and data miners for statistical analysis and graphics supported by the R foundation for

statistical computing. R also provides high-quality graphics

and It also has some popular libraries which help in analytical parts such as R Markdown

and Shiny. Python, on the other hand, is a fully-fledged, Object-oriented & high-level programming language

made by programmers and developers' for general purpose programming.

Python is widely used in GUI based applications such as games, graphic designs, Web applications

and many more

So, we can say that R's functionality is developed by statisticians mind, thereby giving

it a field-specific advantages

while Python is often praised for being a general-purpose language with an easy-to-understand

syntax.

Let us start from the first factor, that is speed.

When it comes to speed, python is faster than R only till 1000 iterations but, after

the 1000 iterations, R starts using the lapply function which increases its speed, in that

case, R becomes faster than python. So, both have their own advantages.

Right? Moving forward to the next point: that

is, Code and Syntax. In this topic, I am gonna give you a brief

about the variable declaration, Data handling capacity with the scatterplot visualization

and.. the ClusPlot graphics.

Starting off with Variable Declaration. Let's take the case of String here. As R

uses the similar implementation to that of the S programming language, which uses arrow

signs in order to initialize the variable which was also present in case of S programming

language. These arrows can be used from right to left or left to right indicating whom to

assign the variables whereas python uses an assignment operator to initialize the variables.

Basically, R developers thought that it would be better to tell the direction of assignment

rather than just using an assignment operator, which could actually confuse any new programmer

about which variable is being assigned.

Next is the Data Handling capability, here, I am gonna show you the case of ScatterPlots,

by which you will see the visualizations in R and python.

These are the piece of codes in R and Python and after running these codes, you will get

the very similar plot results in both the cases, if you check the code here, then this

shows that how R data science ecosystem has many smaller packages like GGally, which basically

is a package that helps ggplot2 and also, it is the most-used R plotting package) whereas

In Python, matplotlib is the primary plotting package, and seaborn is a widely used layer

over the matplotlib. So, guys, these are the plot results that

I was talking about, you can see that the graph results for both R and Python are similar,

but the only difference is their visualization.

So guys, based on these points and plot results, we can conclude that R has Many packages supporting

different methods of doing things Whereas there is usually one way to do something in

python. Moving on to the next point that is Graphics

Here we will take the case of ClusPlots. So Guys, as we already discussed that R was

basically built for statistical analysis, so it has many specific libraries for plotting.

This is the reason R comes up with beautiful charts and graphs whereas Python's main

agenda was not a statistical analysis, so in the early stages of Python, packages for

data analysis was an issue, but it has improved a lot.

Here is the plot result: As you know that a picture says more than

a thousand words. Here You can see by yourself that R comes

up with beautiful graphical representations.

So here we can say that R is handy when it comes to Data Handling.

Our next point of attention is Deep Learning, which is today's trend. As you all know,

almost the majority of the companies are working on Artificial Intelligence, And Deep Learning

is the main part of Artificial intelligence So, When it comes to Deep Learning, Python

is more versatile than R as it provides more features to deep learning whereas R is new

to Deep Learning.

R has newly added APIs like Keras and KerasR which are written in Python.

Right? So now somewhere in your mind, this question

might be floating why Keras? Actually, Keras in Python has the capabilities to run over

python's strong APIs like tensorflow or Theano or Microsoft's CNTK

So we can say that Python has a greater advantage here.

Till now, we have seen that both are useful in their own terms.

Now if we look at the Ease of Learning Point:

Python is easy to start with as its languages are based on standardized format, i.e. people

find it easy to read. It looks like you are reading English. R, on the other hand, is

an unstandardized language. It is quite hard to learn as compared to Python. Beginners

may find this hurdle in the starting. In the past years of research, the percentage

of people switching from R to Python are more as compared to Python to R.

Let's say, if 10% people are switching from Python to R then, 20% are switching from R

to Python, which is twice as compared to the before scenario

Next, we are gonna look at the trends, community support, and Jobs:

Before 2016, R was more in use. But here we can see that from 2016, Python is in trend.

So, it's more popular than R. And because of its popularity, it has overall

good support for general purpose programming. Well if we talk about the community support,

Then Python and R support aspects are almost similar as Python's support is found at:

Mailing list, user-contributed code & documentation & StackOverflow. Basically, it has more adoption

from developers & programmers end. Whereas R language support is also found at:

Mailing list, user-contributed documentation & active StackOverflow members. Basically,

R has more adoption from researchers, data scientist and statisticians end.

Now if we talk about Job trends, let's check the Google Job Trends graph right here,

this is the Job postings for R and Python in past 12 months "WORLDWIDE" where python

is asked more as compared to R. How is it possible? Because of its popularity and its

need in the current industry. Since Python is more versatile and an all-rounder programming

language which can be used for majority of the purposes such as web and application development,

game development, artificial intelligence, data science, statistical analysis etc, whereas

R language is used among statisticians and data miners for developing statistical software

and data analysis. Which clearly depicts that, there are more

jobs for python than R.

Now let's move forward! So, Which one to choose for Data Science R

or Python? Guys, this the frequently asked question by

the majority of the learners in this domain.

I would suggest using both if you have the choice.

They complete each other gracefully and will make your life better if you leverage their

strengths and avoid their weaknesses.

Everything has their own pros as well as cons, so as in the case of R and Python.

If we talk about pros in R, well, then

R is great for prototyping and for statistical analysis.

It has a huge set of libraries which are available for different statistical type analysis.

Even RStudio IDE is definitely a big plus as it eases most of the tedious tasks and

fastens your workflow.

Talking about its cons, well The syntax could be obscure sometimes.

And it is harder for it to integrate to production workflow.

In my opinion, it is better suited for "consultancy-type" tasks.

The libraries documentation isn't always user-friendly.

Talking about the pros in Python,

Python is great for scripting and automating your different data mining pipelines. It is

the de facto scripting language nowadays. And it also integrates easily in a production

workflow. Besides, it can be used across different parts

of your software engineering team (like for back-end, cloud architecture etc.

The scikit-learn library in python is awesome for machine-learning tasks.

Ipython (and its notebook) is also a powerful tool for exploratory analysis and presentations.

Talking of its cons Then python isn't as thorough for statistical

analysis as R, but it has come a long way these recent years

In my opinion, the learning curve is steeper than R, since you can do much more with Python.

To conclude it,

I'd like to that you can use R and Python both. Learn how they inter-operate together.

Start with one and then add the other to your workflow. It only adds another skill-set into

your resume, which comes as an added bonus to your career, Isn't it?

So, guys, now it's a wrap time. Thank you so much for watching this session.

I'd love to hear from you guys that which one according to you is better and why?

Please reply to us in the comment section below.

See you again!

For more infomation >> R vs Python - What should I learn in 2019? | R and Python Comparison | Intellipaat - Duration: 8:15.

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What Should Patients Look for in a Doctor To Do a Catheter Ablation? - Duration: 2:52.

What you want in your doctor who does your afib ablation is somebody who's smart,

who's capable, and who has a good track record, in afib ablations.

Not, like they do VT all week long and do an occasional afib.

The people who do multiple afibs a week.

So, you have to sort of take their word on it.

I mean, if you ask your doctor how many afib ablations do you do a year, and they say hundreds,

I mean, I don't know. Maybe they don't do hundreds,

and maybe they had a lapse of memory, or maybe they think they're doing hundreds.

I think you have to either believe them or not.

I don't know how to verify that without getting into billing records,

which you're not going to be privy to. Right?

And, you have to accept their results.

If they say, "I have a 60 percent result success," I guess if you say, "No, you don't,"

you have to prove that.

I mean, they know what their results are.

Be careful of the outliers.

If somebody quotes you less than one percent complications and a greater than 90 percent success,

I'm going to tell you to look elsewhere.

I mean, would that be the one person that you lucked into

that beats the entire world's literature? Maybe.

Maybe you have that one jewel at the bottom of the ocean that no one knew about,

but I kinda doubt it.

So, there is a lot of data out there you can look at that tells you the average success rates.

If you're not sure, get a second opinion.

Patients will come to me.

And, get it from someone who has a national profile, who has no iron in the fire that

they're trying to steal anyone's patients.

It's not uncommon I get second opinions.

And, I know what a second opinion is.

A second opinion is not me to take over your care.

Second opinion is to give you guidance.

You know, sometimes patients may want to go to you.

That's not a second opinion though.

It's to give you guidance, and I kind of know who does what, you know.

So, sometimes, I'll hear them out, and if somebody has quoted a ridiculous number,

I'll be gentle about it, but I'll just say, "You know, he must be, or she must be,

the only one in the world getting those numbers.

Don't you think that's a little strange?

While I'm not going to call them a liar, that would be wrong.

I just want to tell you; nobody else is getting those numbers.

So, you should think about that."

So, if you're not sure, ask around.

There's just no registry I know of that says Bob down the street does this many,

and Billy does that many. I don't know of any such registry.

So, I guess you sort of have to take them on their word.

For more infomation >> What Should Patients Look for in a Doctor To Do a Catheter Ablation? - Duration: 2:52.

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What should you know about equity-based compensation plans? - Duration: 2:01.

One of the most confusing and complicated aspects of an executive's compensation is

equity incentives like stock awards, restricted stock, and stock options.

These equity-based compensation arrangements provide you with the opportunity to create

lasting wealth, but they must be managed for risk, tax and regulatory consequences.

Because this is so important, we make it a major focus of our book,

Personal Financial Planning for Executives and Entrepreneurs.

It's no surprise then that this is one of the most overlooked areas of financial planning.

Plan agreements, tax and securities laws, company structure and retention guidelines

can be difficult to integrate and navigate.

And all of this needs to be balanced against your personal tolerance for risk, your immediate

and future cash needs and, ultimately, your aspirations for personal wealth.

How much of your net worth is tied to the company stock?

Is the amount of risk you are taking appropriate for you and what is the most tax efficient

way to reduce your ownership?

What special actions should you consider if you work for an early stage company?

When should you exercise options?

Should you cash-out or hold the stock?

If you are considered a company insider then how do you navigate the challenges of

the securities rules?

In our book, we answer these questions, and many more, in an effort to demystify the details

of your stock-based grants and equity-based compensation arrangements.

Consider it your ultimate guide to understanding and maximizing their value, while being educated

about the inherent risks.

Your ownership in your company is too important to ignore.

Our book will help you consciously plan to create lasting wealth.

For more infomation >> What should you know about equity-based compensation plans? - Duration: 2:01.

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What Should I do to Prepare for a Mommy Makeover? - Duration: 1:02.

- What I'd say is, you should be about within 10%

of the weight you wanna be.

I don't have a hard and fast rule that says

you have to be at the number that you say

you have to be at in order to have surgery.

'Cause inevitably after surgery people will lose weight.

Inevitably, people, because they look in the mirror

and say my stomach is really flat;

they have dietary and exercise changes

that I see them install and that we discuss

at the time of our perioperative period and recovery.

Perioperative means before and after surgery

we're gonna have a conversation about lifestyle,

a conversation about what you're gonna do in your life.

So I would say to you, the preparation is,

I wanna get you walking, if you're not walking a mile a day,

I want you to walk a mile a day.

And the reason I do that is by a week or so

after my surgery, I want you to start thinking

about walking a mile a day in week one,

two miles in week two, three miles in week three,

because I'm not only gonna intervene surgically,

I'm also gonna intervene with some lifestyle

that's gonna help you maintain your smaller body habits.