Thanks for the suggestions, we will be doing lot of statistics 🧐. I don't think I discussed Logistic regression last year, since it's only a small part of the whole regression spectrum, but I'll make sure to put more focus on it.
I think a valuable video would be dealing with ugly input data. A lot of tutorials start with cleaned data in the exact format the particular package requires but if you look at stackoverflow people seem to constantly have trouble completing tasks like coercing the data into the relevant structure or removing, adding, binding, subsetting, renaming, etc. I know I personally I struggled with this when I moved to S4 in R.
This is a good idea and a very common problem in bioinformatics, the main issue is that it's not something general, since cleaning steps are often very much tied to the data set being discussed... But I'll think about how to present it in a lecture, since it is a very important topic
Hi sir! I just found your channel and I'm really excited to go through this course and the one about bioinformatics for animals and plants. I only have a quick question, I see you already have done data analysis using R (2021), and I'm wondering if they're gonna be a difference between this course and the one you did last year? thank you so much for making sharing your knowledge.
There will be minor differences, but in general the structure of the course will be similar. The main difference will be the 1 a 2 lectures at the end. These are new every year, because I try and make 1 or 2 completely new lectures based on data/topics that students are interested in. E.g. last year we did "Fishy data" fish population data from the Baggersee project, the year before it was Block Designs in field studies. Joining the live streams does have the advantage that you are able to ask questions and it helps grow the channel/gets slightly more visibility for the lectures on the YT algorithm. Newer content is prioritized over older content
Could you maybe compare python and r for data analysis. What are the benefits and shortcomings compared to the other one? Which one do you prefer and why?
I can't really comment on Python, since I almost never use it the comparison with R would not be fair to make for me. Friends of mine seem to like it and use it all the time, similar to how I use R I'm more used to coding in R, C, C++ and D
TH-cam, multiple regression, and other econometric applications. Thank you so much
Thanks for the comment and topic suggestion !
👍👍👍☺
Thanks for the comment
Descriptive statistics in R, Statistical inference with R, and Linear/Logistic Regression in R.
Thanks for the suggestions, we will be doing lot of statistics 🧐. I don't think I discussed Logistic regression last year, since it's only a small part of the whole regression spectrum, but I'll make sure to put more focus on it.
I think a valuable video would be dealing with ugly input data. A lot of tutorials start with cleaned data in the exact format the particular package requires but if you look at stackoverflow people seem to constantly have trouble completing tasks like coercing the data into the relevant structure or removing, adding, binding, subsetting, renaming, etc. I know I personally I struggled with this when I moved to S4 in R.
This is a good idea and a very common problem in bioinformatics, the main issue is that it's not something general, since cleaning steps are often very much tied to the data set being discussed... But I'll think about how to present it in a lecture, since it is a very important topic
Hi sir! I just found your channel and I'm really excited to go through this course and the one about bioinformatics for animals and plants. I only have a quick question, I see you already have done data analysis using R (2021), and I'm wondering if they're gonna be a difference between this course and the one you did last year? thank you so much for making sharing your knowledge.
There will be minor differences, but in general the structure of the course will be similar. The main difference will be the 1 a 2 lectures at the end. These are new every year, because I try and make 1 or 2 completely new lectures based on data/topics that students are interested in. E.g. last year we did "Fishy data" fish population data from the Baggersee project, the year before it was Block Designs in field studies.
Joining the live streams does have the advantage that you are able to ask questions and it helps grow the channel/gets slightly more visibility for the lectures on the YT algorithm. Newer content is prioritized over older content
Can you show some Hi-C content?
Could you maybe compare python and r for data analysis. What are the benefits and shortcomings compared to the other one? Which one do you prefer and why?
I can't really comment on Python, since I almost never use it the comparison with R would not be fair to make for me.
Friends of mine seem to like it and use it all the time, similar to how I use R
I'm more used to coding in R, C, C++ and D
Please use in TH-cam for the course
Live R course every Thursday on TH-cam
youtube is perfect
Live R course every Thursday on TH-cam
And please use TH-cam...
Just replying :) other wise it keeps showing up, thanks for the sub and learn R
How to create plots
Are there any types of plots in particular you want to learn how to do?
You Tube. R-Text Analysis
Thanks for the comment and topic suggestion !