Can I see unnecessary variables from 10 parameters that I will use to make future predictions with time series using lstm or neural network with shap?

Thank you richard for the posting the tutorial. i'm following along but it seems that the R package for the video is no longer in your git account. Could you please update it, thank you.

R is much easier to learn. I found Python hard to learn, and really struggled with all the environment issues..

Hi Richard I wanted to inquire whether it's necessary to complete all the 100+ hours of activities mentioned in this link www.cloudskillsboost.google/paths/17 I already have a good understanding of the concepts, so can I take the exam directly after practicing the sample questions? Your guidance would be much appreciated.

Hi, thank you for the video. I’m comfortable using SAS and SQL. How difficult it would be for me to switch to R? I hear R is more difficult than SAS.

I think the main point you should have learnt in the past 4 years is that it still does not make too much sense to compare R and Python. R is a statistical programming language, Python NOT. Python is an all purpose language, R NOT. Amen.

Do you do a lot of juggling?

Bro is a fucking king

Thank you man! Didn't understand how to even open an environment in the first place, so this is very useful Thank you again

For me it's all about how many jobs are available for any programming language, not just the 2 you covered.

Because this is right on target for exactly how step by step to accomplish the title of your video I subscribed to this channel. I'm impressed with the short amount of time it took to get and understand the info you provided, thank you.

I'm done with python, I'm learning r right now. From what I observe if you want to focus or you want data go for r then if you want to do more, go with python. If possible I think you should learn both haha😅

R is good for pre analysis but for production python is better.

Learn both

Inference is to find the logistic between the data, and prediction is to minimize the distance of expected value and true value. Prediction will take the mini bias between data into consinder.

I do scientific computations in both R and Python, depending on the project team choice. R is best -- fastest to code, easiest to debug, better for plotting. Python is riddled with package conflicts that hugely increase debugging and lead to endless repeated google searches and AI queries to resolve maddening tiny conflicts that R does not have. That's why google searches touching Python are so high. Everyone always has to hunt to resolve its conflicts. It's a bad sign, not a good. R is for smooth very complex computing, Python is for "Hello World" stuff.

Thank you

Really, every body needs to become a manager or director after 15 years? BS!

Very helpful. As a social scientist (not working in text-as-data), R is straightforwardly more useful. I superficially learned Python first. Then, I learned R and found it more useful for just about everything I need to do. One consideration I'd add (unless you said it and I missed it) is that R users tend to use R Studio as the IDE, which makes things easier while getting started and remains useful (knitr, markdown) as you gain skill. With R Studio, you are able to see all the objects in memory, as well as storage type. You can open a dataframe as a spreadsheet or pop it out as a new window and look at it side-by-side with any section of your code. When I learned Python (I really only use R now, so I'm probably biased), the best we had was Jupyter notebooks. I don't know if people are using something better, but when I learned Python, I found it pretty frustrating to have to constantly print things to check on objects' attributes and contents. My understanding is R Studio runs Python code now, but I don't much evidence of people using it. Have Python's IDE options improved since I learned five years ago?

Fiats are more common than tractors, but would you use a Fiat instead of a tractor as a farmer just because Fiats are more widely used?

pliase repy

i am indea contriy

sir which your contriy 😇

Is this certificate worth it?like does it give power to resume?

I will be taking the GCP machine learning Certification exam in some weeks to come , will be back with some good news, Thank you so much for this video,

I usually don't subscribe, to avoid unnecessary suggestions. But with current KZbin algorithm. It doesn't really matter whether I am subscribed or not. You should mention this as well. I doesn't hurt in anyway to subscribe to a channel. So hit the subscribe button😂😅 So why not.... I subscribed 😉👍

When did you receive the certificate after passing. Also are the google's badges and certificates same?

1:40

Hi Richard, thanks so much for your videos; they've been incredibly helpful! I’m currently working with a highly imbalanced dataset-1% positive class and 99% negative class-and I'm interested in adjusting the classification threshold using tidymodels. I’ve read that direct threshold modification might not be supported yet. Is there any workaround for this, or any alternative methods you recommend for handling this kind of class imbalance? I’d appreciate any advice or resources you could share!

👏👏👏👏

Both. I mainly do financial statistics. My main codebase is in R but I have Python helper files that can be loaded into an R script when needed, and then call those Python functions directly in the R script.

Out of all of the realms of IT and CS, Data Analysis was the one that peaked my interest in school. I hope this is just a cycle that will come to pass because finally landing with something I like within the subfields in my major, only for it to turn out into a nightmare when it comes to career propects would feel so defeating.

Thanks so much Richie! Loved your description of the exam!! Well done bro for an honest assessment..

Thanks a lot for this video

lol Rust 2% 11:10 there is a great implementation of pytorch in R, called torch

Re people eventually getting bored by machine learning: isn't it in part because the standard machine learning task of point prediction (which I understand is what 99% of times people mean by prediction) is actually quite limited as compared to scientific explanation and statistical inference? Finding a y_hat that minimises smth like MSE is a very limited way of doing 'science'. In contrast trying to infer something close to an approximate data generating process, or probabilistic causal model is fundamentally interesting if you care about economic or business or marketing problems (of medical/epidemiologic etc problems if you care about that stuff). And the thing is, from a decision science perspective the focus on prediction is also quite limiting. Decision science.-> you want probabilistic scenarios and causal effects. Data science would be more interesting if there was more probabilistic causal data science (there is, but it seems to me it's still a much smaller segment than standard predictive machine learning, and data analysis- and again, there's only so much you can learn about how a business or medical situation works from EDA). The thing, if you do more probabilistic causal model inference based on domain knowledge, is that really just data science? Isn't it more like applied empirical economics or sociology or epidemiology? Isn't data science potentially boring because the focus on just data is a limiting way to solve business problems?

Dont get into the typical data analyst, scientist, engineer etc. Get into DATABASE (developer, administration etc) Less competition and higher demand. Can't get data to analyze, etl, or make predictions without the database. Master database development and you'll never worry about a job again.

the bro is like: 🤌👁👄👁🤌

it is useful than other channel saying that degree is useless or something like that for data science, I have seen that most of real world data science jobs require PhD

Thanks!

Pls mic fix

I am a PhD candidate in data analytics looking for job for eight months right now not even a single interview it’s very tough😫

That was a really good explanation. Short and powerful.

R is more capable of doing amazing things better than python

I am AWS ML certified

Good to hear that you learnt R and then created the video. I can understand @6:41 - After learning c , c++, basic , cobol ie having a programming background. R really felt funny and weird because there are multiple ways you can do the samething. But later i fell in love with R . I have heard numpy and pandas are inspired from R datastructures. You have computer engineers backing up development and usage of python whereas bunch of academicians and statisticians for R. R initially looked like hotchpotch but after looking at numpy and pandas with basic python...... i just laugh at my judgements reversing over time. Python seems to be more in line with traditional expectation from OOPS syntax...i can go on ..... but both could have been more streamlined for the workflow of datascience

I can't believe FORTRAN is #12! I programmed my master's thesis project in 1995 in FORTRAN and I thought nobody used it anymore. As a statistician I'm guessing R is the way to go.

Nice honest and informative video. Thank you.

helpful video , thank you sir 🌟

It remains vague. What exactly can you do with R that is not possible with Python?