The course is entirely in programming R and only covers the programming of statstical procedures. I do assume you already for example know what an ANOVA and regression are. If you don’t, you’d be better off to take a combined statistics+R course or a stats course first if you want the stats content.
I was hoping your video was showing how social scientists would use Data Science and the R language for their research processes. Have anything like that? thx
Annaka Johnson This is it! By the end of the course (all 13 weeks including completing the projects on your own before watching the debrief videos), you will able to do basic natural language processing, write basic AIs, and deploy web apps to support your research, in addition to more basic skills, like creating reproducible data pipelines, managing data more efficiently, presenting results more effectively, and creating figures to interactive visualizations. It’s important to start with fundamental programming skills though and really learn the whole skill set! Don’t skip the basics or you’ll be lost forever.
A rather tangential question, but are there certification courses in things like "R", "Python", etc.? I'm just curious because when people place something like "Python" in "additional skills" on a resume/CV, is there any standard for when your "R" knowledge or "Python" knowledge is fit to put on a resume/CV?
There are no generally accepted credentials; however, they are certainly credentials that you could get if that's what you were after. MOOCs (Coursera, EdX, etc) offer certificates for example and have many courses that you could take. There are also certificate programs that you can get, some of which claim more broad acceptance than others (e.g., pythoninstitute.org/certification/, bootcamp.umn.edu/). I think a problem you hit is that the breadth of programming, even within just R or Python, is so vast that there is no way you could be realistically an expert in all of them. Even in this course, I only spend 1 module on natural language processing, on machine learning, on web applications, etc., but each of these could be an entire course, or even an entire degree program. In the real world, people are also now usually not employed to do _all of data science_ but for example are hired as a "machine learning engineer." So I would recommend learning the fundamentals of R and/or Python in whatever way makes sense to you (like this course) and then identify which specific "advanced" skills you want to go deep on. Within one such area, you'll generally find more useful credentials (e.g., www.pce.uw.edu/certificates/natural-language-technology).
@@richardlanders Thanks for the reply! Could you send me an updated link from that UW website at the bottom of your reply? I got a 404 when I clicked on it.
@@ryanhjelle6924 Weird! When I click the link, it also doesn't work, but if I copy/paste, it does! UW must be doing some sort of broken RUclips referral detection.
Thank you so much for these! As a master's student in Industrial Psychology, these are quite helpful.
Your 1000th subscriber!! Hope you can get monetized now cuz this content was so helpful and amazing for me as a social science student!
Awesome, thank you! I don't know about monetization, but I am glad the content is helping people.
Thank you so much for the instructional videos. They continue to help me greatly. You are now also teaching German sociology students😁
Thank you for the explanation. I am a development studies student.. and this will help me to make more meaning of my findings.
Amazing & clear explanation! Thanks for such a great video.
Sir is this module for students with no mathematical knowledge? If not then from where can I begin to build my understanding?
The course is entirely in programming R and only covers the programming of statstical procedures. I do assume you already for example know what an ANOVA and regression are. If you don’t, you’d be better off to take a combined statistics+R course or a stats course first if you want the stats content.
Richard Landers Thank you for your reply.
Yep excellent stuff, hope you do more videos - best explained content on youtube
I was hoping your video was showing how social scientists would use Data Science and the R language for their research processes. Have anything like that? thx
Annaka Johnson This is it! By the end of the course (all 13 weeks including completing the projects on your own before watching the debrief videos), you will able to do basic natural language processing, write basic AIs, and deploy web apps to support your research, in addition to more basic skills, like creating reproducible data pipelines, managing data more efficiently, presenting results more effectively, and creating figures to interactive visualizations.
It’s important to start with fundamental programming skills though and really learn the whole skill set! Don’t skip the basics or you’ll be lost forever.
Dr. Landers, would you be able to expand on downloading Git? Is there a preferred website to download it from?
Sure, you find it here: git-scm.com/downloads
A rather tangential question, but are there certification courses in things like "R", "Python", etc.? I'm just curious because when people place something like "Python" in "additional skills" on a resume/CV, is there any standard for when your "R" knowledge or "Python" knowledge is fit to put on a resume/CV?
There are no generally accepted credentials; however, they are certainly credentials that you could get if that's what you were after. MOOCs (Coursera, EdX, etc) offer certificates for example and have many courses that you could take. There are also certificate programs that you can get, some of which claim more broad acceptance than others (e.g., pythoninstitute.org/certification/, bootcamp.umn.edu/). I think a problem you hit is that the breadth of programming, even within just R or Python, is so vast that there is no way you could be realistically an expert in all of them. Even in this course, I only spend 1 module on natural language processing, on machine learning, on web applications, etc., but each of these could be an entire course, or even an entire degree program. In the real world, people are also now usually not employed to do _all of data science_ but for example are hired as a "machine learning engineer."
So I would recommend learning the fundamentals of R and/or Python in whatever way makes sense to you (like this course) and then identify which specific "advanced" skills you want to go deep on. Within one such area, you'll generally find more useful credentials (e.g., www.pce.uw.edu/certificates/natural-language-technology).
@@richardlanders Thanks for the reply! Could you send me an updated link from that UW website at the bottom of your reply? I got a 404 when I clicked on it.
@@ryanhjelle6924 Weird! When I click the link, it also doesn't work, but if I copy/paste, it does! UW must be doing some sort of broken RUclips referral detection.
@@richardlanders HA! That IS strange. Thanks for the help!
AMAZING! You explain so well.
cool stuff Richard
Great Material, Valuable!
Great video!
Really helpful
The videos are really helpful, thank you!
seen