- 1 780
- 201 046
Data Science Learning Community Videos
United States
เข้าร่วมเมื่อ 30 ธ.ค. 2017
Our mission is to help everyone learn data science! We post videos from DSLC.io book clubs, tutorials, and other data science content.
Note that we are a separate entity from the Data Science Learning Community. Membership at DSLC.io is absolutely free!
Note that we are a separate entity from the Data Science Learning Community. Membership at DSLC.io is absolutely free!
Python for Data Analysis: Plotting and Visualization (py4da02 9)
Abdou Daffeh leads a discussion of Chapter 9 ("Plotting and Visualization"') from Python for Data Analysis by Wes McKinney on 2024-06-01, to the DSLC py4da Book Club. Cohort 02
Read along at dslc.io/py4da
Join the conversation
Read along at dslc.io/py4da
Join the conversation
มุมมอง: 48
วีดีโอ
Spatial Statistics for Data Science: Spatial interpolation methods (spacestats06 13)
มุมมอง 4021 ชั่วโมงที่ผ่านมา
Floris Vanderhaeghe (Research Institute for Nature and Forest (INBO), Brussels, Belgium) leads a discussion of Chapter 13 ("Spatial interpolation methods"') from Spatial Statistics for Data Science: Theory and Practice with R by Paula Moraga on 2024-06-01, to the DSLC spacestats Book Club. Cohort 06 Read along at dslc.io/spacestats Join the conversation at dslc.io/join!
R for Data Science: Web scraping (r4ds10 24)
มุมมอง 792 ชั่วโมงที่ผ่านมา
Jon Harmon leads a discussion of Chapter 24 ("Web scraping"') from R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund on 2024-05-31, to the DSLC r4ds Book Club. Cohort 10 Read along at dslc.io/r4ds Join the conversation at dslc.io/join!
Advanced R: Names and values (advr09 2)
มุมมอง 424 ชั่วโมงที่ผ่านมา
Steffi LaZerte leads a discussion of Chapter 2 ("Names and values"') from Advanced R by Hadley Wickham on 2024-05-31, to the DSLC advr Book Club. Cohort 09 Read along at dslc.io/advr Join the conversation at dslc.io/join!
Introduction to Statistical Learning Using R: Survival Analysis and Censored Data (islr06 11)
มุมมอง 9112 ชั่วโมงที่ผ่านมา
Umair Durrani leads a discussion of Chapter 11 ("Survival Analysis and Censored Data"') from Introduction to Statistical Learning Using R by Gareth James, Daniela Witten, Trevor Hastie, & Rob Tibshirani on 2024-05-28, to the DSLC islr Book Club. Cohort 06 Read along at dslc.io/islr Join the conversation at dslc.io/join!
Mastering Shiny: Your first Shiny app (mshiny07 1)
มุมมอง 7612 ชั่วโมงที่ผ่านมา
Ahmed leads a discussion of Chapter 1 ("Your first Shiny app"') from Mastering Shiny by Hadley Wickham on 2024-05-28, to the DSLC mshiny Book Club. Cohort 07 Read along at dslc.io/mshiny Join the conversation at dslc.io/join!
Probabilistic Machine Learning: An Introduction: Logistic Regression (first half) (pml01 10)
มุมมอง 6312 ชั่วโมงที่ผ่านมา
Russ Hyde leads a discussion of Chapter 10 ("Logistic Regression (first half)"') from Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy on 2024-05-28, to the DSLC pml Book Club. Cohort 01 Read along at dslc.io/pml Join the conversation at dslc.io/join!
Explanatory Model Analysis: Local-dependence and Accumulated-local Profiles (ema01 18)
มุมมอง 4914 ชั่วโมงที่ผ่านมา
Angel Feliz leads a discussion of Chapter 18 ("Local-dependence and Accumulated-local Profiles"') from Explanatory Model Analysis by Przemyslaw Biecek & Tomasz Burzykowski on 2024-05-26, to the DSLC ema Book Club. Cohort 01 Read along at dslc.io/ema Join the conversation at dslc.io/join!
R for Data Science: Hierarchical data (r4ds10 23)
มุมมอง 16716 ชั่วโมงที่ผ่านมา
Floris Vanderhaeghe (Research Institute for Nature and Forest (INBO), Brussels, Belgium) leads a discussion of Chapter 23 ("Hierarchical data"') from R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund on 2024-05-24, to the DSLC r4ds Book Club. Cohort 02 Read along at dslc.io/r4ds Join the conversation at dslc.io/join!
Advanced R: Introduction (advr09 1)
มุมมอง 10421 ชั่วโมงที่ผ่านมา
Olivier Leroy kicks off a new book club and leads a discussion of Chapter 1 ("Introduction"') from Advanced R by Hadley Wickham on 2024-05-24, to the DSLC advr Book Club. Cohort 09 Read along at DSLC.io/advr Join the conversation at DSLC.io/join!
Web APIs with R: Process other response types (wapir01 7)
มุมมอง 84วันที่ผ่านมา
Jon Harmon leads a discussion of Chapter 7 ("Process other response types"') from his in-progress book, Web APIs with R, on 2024-05-22, to the DSLC wapir Book Club. Cohort 01 Read along at dslc.io/wapir Join the conversation at dslc.io/join!
Probabilistic Machine Learning: An Introduction: Linear Discriminant Analysis (pml01 9)
มุมมอง 112วันที่ผ่านมา
Derek Sollberger leads a discussion of Chapter 9 ("Linear Discriminant Analysis") from Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy on 2024-05-21, to the DSLC pml Book Club. Cohort 01 Read along at DSLC.io/pml Join the conversation at DSLC.io!
R Packages: Releasing to CRAN (rpkgs06 22)
มุมมอง 50วันที่ผ่านมา
Jon Harmon leads a discussion of Chapter 22 ("Releasing to CRAN") from R Packages by Hadley Wickham and Jenny Bryan on 2024-05-20, with the DSLC rpkgs Book Club. Cohort 06 Read along at dslc.io/rpkgs Join the conversation at dslc.io/join!
Mastering Shiny: Shiny modules (mshiny06 19)
มุมมอง 81วันที่ผ่านมา
Angel Feliz leads a discussion of Chapter 19 ("Shiny modules") from Mastering Shiny by Hadley Wickham on 2024-05-19, to the DSLC mshiny Book Club. Cohort 06 Read along at DSLC.io/mshiny Join the conversation at DSLC.io!
Spatial Statistics for Data Science: Geostatistical data (spacestats01 12)
มุมมอง 4414 วันที่ผ่านมา
Floris Vanderhaeghe (Research Institute for Nature and Forest (INBO), Brussels, Belgium) leads a discussion of Chapter 12 ("Geostatistical data") from Spatial Statistics for Data Science: Theory and Practice with R by Paula Moraga on 2024-05-18, with the DSLC spacestats Book Club. Cohort 01 Read along at dslc.io/spacestats Join the conversation at dslc.io/join!
Python for Data Analysis: Data Cleaning and Preparation (py4da02 7)
มุมมอง 28014 วันที่ผ่านมา
Python for Data Analysis: Data Cleaning and Preparation (py4da02 7)
Probabilistic Machine Learning: An Introduction: Linear Discriminant Analysis (pml01 9)
มุมมอง 9314 วันที่ผ่านมา
Probabilistic Machine Learning: An Introduction: Linear Discriminant Analysis (pml01 9)
Explanatory Model Analysis: Partial-dependence Profiles (ema01 17)
มุมมอง 8121 วันที่ผ่านมา
Explanatory Model Analysis: Partial-dependence Profiles (ema01 17)
Python for Data Analysis: Data Loading, Storage, and File Formats: Part II (py4da02 6)
มุมมอง 9021 วันที่ผ่านมา
Python for Data Analysis: Data Loading, Storage, and File Formats: Part II (py4da02 6)
R for Data Science: Arrow (r4ds10 22)
มุมมอง 19921 วันที่ผ่านมา
R for Data Science: Arrow (r4ds10 22)
Web APIs with R: How do I tell the API who I am? (wapir01 6)
มุมมอง 9821 วันที่ผ่านมา
Web APIs with R: How do I tell the API who I am? (wapir01 6)
Probabilistic Machine Learning: An Introduction: Foundations (pml01 1)
มุมมอง 18921 วันที่ผ่านมา
Probabilistic Machine Learning: An Introduction: Foundations (pml01 1)
R Packages: Software development practices (rpkgs06 20)
มุมมอง 7021 วันที่ผ่านมา
R Packages: Software development practices (rpkgs06 20)
Spatial Statistics for Data Science: Disease risk modeling & Areal data issues (spacestats01 10 11)
มุมมอง 7828 วันที่ผ่านมา
Spatial Statistics for Data Science: Disease risk modeling & Areal data issues (spacestats01 10 11)
Explanatory Model Analysis: Variable-importance Measures (ema01 16)
มุมมอง 8228 วันที่ผ่านมา
Explanatory Model Analysis: Variable-importance Measures (ema01 16)
Python for Data Analysis: Data Loading, Storage, and File Formats: Part I (py4da02 6)
มุมมอง 50328 วันที่ผ่านมา
Python for Data Analysis: Data Loading, Storage, and File Formats: Part I (py4da02 6)
R for Data Science: Databases (r4ds10 21)
มุมมอง 18028 วันที่ผ่านมา
R for Data Science: Databases (r4ds10 21)
Thanks
You're welcome! Join us at DSLC.io to participate!
Thanks for this lesson
We're glad you found it helpful! Join us at DSLC.io if you'd like to participate!
Jim is the best one to explain everything simply and efficiently.
Jim's great! Join us at DSLC.io to let him know that you appreciate him!
Just found this set of videos, is the slack channel still active ?
Absolutely! Head over to DSLC.io to join the community! We aren't reading this book right now, but we have dozens of other book clubs available!
finished watching the 1st one.Looking forward to finish the 1st chapter myself and then come here to learn from your discussions.Although I am a Pythonista,I am optimistic.
I hope you find it useful! We don't do book clubs for this book anymore (we only do books with free online versions), but the dual-language nature IS nice! Stop by our Slack via DSLC.io to join one of our current clubs!
looking forward for this series.
Sadly that club fizzled out due to outside events in the participants' lives. We'd love to have you at DSLC.io to participate in a new cohort!
Amazing ❤ Can you please tell which book is it ?!
As noted in the description, it's Chapter 9 ("Support Vector Machines") from Introduction to Statistical Learning Using R by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani. Read along at dslc.io/islr !
suuuuuper helpful as I wanted to know if the answers to my exercises were right, I also got a better explanation to differences that I didn't understand well. thank you!
I'm glad to hear it was helpful! We'd love to have you on our Slack at r4ds.io/join if you aren't there already!
sorry but I was not able to read properly as the book and jupyter notebook were not clear
You likely won't be able to read notebooks and/or books in our videos. I recommend joining our Slack community at r4ds.io/join to follow along with the club and discuss!
Brilliantly explained.
We're glad you found it helpful! Join our Slack to discuss more if you haven't already! r4ds.io/join
Excellent presentation of chapter 11, thank you! Is the code from the examples accessible somewhere? It would be very useful :)
The code may be available in the club's repo via r4ds.io/fpp, or in the original book. Good luck!
awesome tutorial®
Glad you think so!
Heat equation by Crank-Nicolson! Brings back fond memories of graduate school Numerical Analysis. “Numerical Solution of Partial Differential Equations: Finite Difference Methods” by Gordon D Smith: my favorite textbook. 😉 👍
I'm glad the memories are fond! We'd love you to share them (and to read along with us) at r4ds.io ! 🤓
very glad for sharing my knowledge in R for data science
We're glad to have you involved!
Thanks for the sharing!
You're welcome! If you haven't already, join our Slack at r4ds.io/join, and then head to the #book_club-py4da channel at rfordatascience.slack.com/archives/C03P2J90L30 !
No audio
Yes, unfortunately the sound is missing from the Zoom recording of this meeting.
Is it just me or is there no audio?
Unfortunately it looks like Zoom didn't record the audio for this meeting! We've never seen this happen before, so I'm not sure what might have caused it.
This is cohort 1 for this book club?
No, this is cohort 2. The first cohort is at th-cam.com/play/PL3x6DOfs2NGh7IQIQ_pXNkjLVKa-7lgCw.html This is the first meeting of this new cohort, though, if that's what you meant to ask! Stop by r4ds.io/join to learn more!
Wonderful!
I'm glad you found this club helpful! You can join our Slack at r4ds.io/join to participate!
Interesting to listen to the discussions. I read the chapter on Environments for the first time and I really struggled to understand the concepts and thought they would have been been much easier to grasp if peppered with practical use cases. I made a mental note to revisit the chapter later on to see if I understand better. Nice discussion!
I'm glad you found them interesting! Environments can definitely be tough to wrap your mind around! I think the way I've used them most is passing them around for error messages, so the error message appears as if it came from function A, even though function D is actually producing the error. Are you on our slack at r4ds.io/join? Check it out if not!
8:09 My understanding is data-masked expressions are expressions such as sum(my_col) where my_col is the bare name of the column without quotes, and data-masked symbols are just simple column names, such as my_col without quotes. You can pass both of them into a function argument. For example: my_func <- fuction(data, x) { data |> summarise(mean({{ x }}) } my_func(my_data, my_col) OR my_func <- fuction(data, x) { data |> summarise({{ x }}) } my_func(my_data, mean(my_col)) Both of them return the same result.
Yes, that's a great explanation! Thanks!
Hey, how can I join this zoom meeting live?
Join our Slack at r4ds.io/join Go to the #book_club-shinyui channel. The zoom link appears 10 minutes before the meeting time (this meeting is at 10am CDT Tuesdays). See r4ds.io/events for the full book club schedule!
It's nice to join this community, I started the second edition of r4ds two weeks.
Glad to hear it! For anybody else reading this: We always welcome new members to our Slack community at r4ds.io/join!
En realidad ese capítulo trata sobre cómo hacer modelado sin usar el paquete tidymodels, sino haciendo uso de R base.
¡Sí, así es!
Hello, Can you share this code?
Anything the club shares can be found at r4ds.io/mshiny, and in the associated github repository. Stop by our Slack at r4ds.io/join if you have more questions!
Cómo participamos?
Este club está terminado, pero únete a nuestro Slack en r4ds.io/join y encuentra #chat-r_en_español!
@@dslcvids Genial, muy agradecido, pero el enlace que me enviaste ya no está activo. Puedes enviarme otro por favor.
Hmm. ¡inténtalo de nuevo, por favor! Intenté actualizarlo, pero el enlace aún no ha caducado.
The link to join the community is no longer valid. How to join?
@FlippieCoetser Slack randomly expires that link from time to time. It should work now. Sorry about that!
Thanks for taking the time to make these videos.
I'm glad you find them helpful! Join our Slack at r4ds.io/join if you'd like to participate in our book clubs live!
Great lecture, thank you
Glad it was helpful! Join our Slack at r4ds.io/join if you'd like to participate in the club!
Awesome.. I like this lecture
Glad to hear that! I added you to the #book_club-islr channel on the R4DS Slack (r4ds.io/join) in case you'd like to participate in the club!
@@dslcvids yes
Hi Mateo, may i join this class?
It looks like you found your way to the Slack. The Zoom link posts in the #book_club-mshiny channel at 3:50pm CDT/CST every Thursday. Everyone is welcome to join!
With a screen with such a small font size it is very difficult to follow the actual commands and the output... almost unreadable. Even switching the video quality to high resolution does not help.
Sorry you're having trouble with that! We post the videos as a bonus so people can catch up. I highly recommend joining the meetings live for a better experience! Join our Slack at r4ds.io/join for details!
@@dslcvids Thanks for answering. 🙂 Except for this minor video issue (after all, there is the book), the content is very good.
No video
It takes about 20 seconds for the video to start moving. Sorry, that's an artifact of the automatic editing/posting process!
I rerally enjoyed this - thanks guys. The debate about duplicate columns was interesting and I tried both approaches suggested. Sparked by a comment, I also found enframe() is a really easy way of converting a named vector into a two column tibble containing name and value columns.
I'm glad you found it interesting! I personally love `enframe()`, it's definitely useful! Stop by the community Slack at r4ds.io/join if you'd like to continue the conversation!
Really good, that was a great introduction
I'm glad you found it helpful! Stop by our Slack at r4ds.io/join if you haven't already to discuss the book in depth!
The sound quality is quite poor.
It sounds ok on my end. Sorry you're having trouble with it! Stop by our Slack community at r4ds.io/join if you'd like to discuss details with the group!
I hope you already got it right...SRTM is Shuttle Radar Topography Mission. Nice talk!
You might want to bring it up in the #book_club-geocompr channel on our Slack (r4ds.io/join), if you haven't already. The people in these videos don't get notified about comments, unfortunately. Thanks for watching, though!
Great video! Succinct. Thank you for the session.
Glad it was helpful! Join our Slack at r4ds.io/join if you'd like to participate in a club!
@@dslcvids Thank you!
Very useful, Thanks for sharing
I'm glad you enjoyed it! Join our slack at r4ds.io/join if you haven't already!
Thanks for the webinar , very helpful
Glad it was helpful! Come join our Slack at r4ds.io/join (if you haven't already) to discuss the club and any R-related questions you might have!
L I K E 👍 👍 👍 👍 👍 🍀 🍀 🍀 🍀 🍀🤩🤩 🤩🤩
Thanks!
thanks for doing this
You're welcome! Join our slack at r4ds.io/join to participate!
:-( bigger fonts please
This club met 2 years ago. Some of the more recent cohorts might have done a better job making the slides readable. If not, join our slack at r4ds.io/join to participate more directly!
larger fornts please
We'll try to keep that in mind! Join a new cohort through our Slack at r4ds.io/join!
Cool, enough reason for me to revise the first three chapters in preparation for next week.
Glad you found it helpful! I've added you to the #book_club-mshiny channel on the R4DS Slack (r4ds.io/join)!
Thanks for the webinar, Very useful
I'm glad you found it helpful! It's more of a club than a webinar, though! Come join our Slack at r4ds.io/join if you aren't already there!
Nice one!
Thanks! Join our Slack at r4ds.io/join to participate!
Cool 👍🏾👏🏾
I'm glad you liked it!
Very useful, Thanks
Glad it was helpful!
Very Informative Video, Thanks
You are welcome