Hi there! Just wanted to say I'm an MD currently doing a PhD in pre-clinical research. Have been enjoying some of your content! I'm personally intrigued by Machine learning, have been reading loads of books and doing courses but am realizing that my biostatististics knowledge is not strong enough to grow much further. So I will now be focussing on a biostatistics book, as you recommended. Thanks for that. Subscribed and interested to see what else you'll create!
Hi! I’m glad you’re enjoying the content! I can’t even fathom doing a PhD after an MD, so massive respect. If you’re interested in machine learning, I can also recommend a book that’s widely respected and aimed towards beginners. It’s called ISLR, and can be found here: www.statlearning.com/. I’m not affiliated but I’ve used it before in course work. I hope it helps and good luck with your PhD!
Hey, I’m a recent graduate in statistics from Brazil. Working with data science in pharma (Market Access, a bit more business) would you be up to creating a discord or something to allow more direct communication among the community?
I’d probably say something similar to: Data is random and unpredictable. Statistics uses models based in probability to learn from the data, despite this randomness. Huge simplification, but it gets at the bigger picture
nah for most programs you’ll see the prerequisites are usually math classes. Most of the relevant biology you pick up once you start writing on applied problems, but not needed to get a degree
Also, Casella is insane for a first intro to probability, let alone math stats, especially without recommending any math books for background. I am mainly commenting this warn people from thinking it is a good idea.
It’s pretty common for students to have to pay for out of pocket for an MS, at least within my small niche of biostatistics. I would say most students were like that in my program. Perhaps this is a US thing? Also, fair point about Casella. I chose it mostly because I had to use it in multiple programs, but I forget that I was using it in grad-level courses. Do you have a recommendation for a gentler intro to probability?
@very-normal That's fair, and it is good to see videos on the topic, so keep it up! I am from Canada, and generally, thesis based masters are funded here. I believe this is the norm outside the US as well, even within biostatistics. I think for an introduction to probability theory, two books are commonly used. The standard is Ross's A first course in probability theory, but I actually prefer Blitzstein's Introduction to Probabilty theory. It is free online and comes with video lectures. For math stats, I used Hogg's book. The first three chapters also act as a good introduction to probability theory. Another advantage to the ladder two is the inclusion of R code as well as the variety of applied and theoretical questions. These two do assume familiarity with basic calculus up to multiple integrals and infinite series, but not much beyond that. That being said, I used Casella for a second course in math stats in my undergrad, and the exercises are invaluable for anyone looking to get into research statistics.
I cannot express how jealous I am of you lol. Thank you for the recommendations, I really appreciate you took the time to give me some. I’ll look into these and pass these along if others ask as well
@@very-normalMy masters course uses Hogg and Tanis but I am using my Dad's old books (casella). 😂. I think that Ross's First course should definitely be read before Casella because chp 1 already assumes one is familiar with some of the distributions. I also recommend Ross's Stochastic Processes as it felt like an "applied probability" course. However, I think that one would need considerable amounts of mathematical background: I'd say Proofs by Cummings, Calculus by Spivak, (insert probability) Elementary Linear Algebra by Larson, Real Analysis by Jay Cummings, Linear Algebra done right by Axler, Advanced Calculus by Buck (insert casella) and Real Analysis by Carothers (insert stochastic processes) is a good way to get the math background so one does not struggle. (I am a math major BTW) I did probability as a freshman in college and dropped it due to lack of proof writing abilities. As someone interested in the Economics side, I liked Kutner's linear models and then Introductory Econometrics by Wooldridge after finishing casella.
![Lil Uzi Vert - Chill Bae [Official Music Video]](http://i.ytimg.com/vi/D7F42F_JM3U/mqdefault.jpg)
Hi there! Just wanted to say I'm an MD currently doing a PhD in pre-clinical research. Have been enjoying some of your content!
I'm personally intrigued by Machine learning, have been reading loads of books and doing courses but am realizing that my biostatististics knowledge is not strong enough to grow much further. So I will now be focussing on a biostatistics book, as you recommended. Thanks for that. Subscribed and interested to see what else you'll create!
Hi! I’m glad you’re enjoying the content! I can’t even fathom doing a PhD after an MD, so massive respect.
If you’re interested in machine learning, I can also recommend a book that’s widely respected and aimed towards beginners. It’s called ISLR, and can be found here: www.statlearning.com/. I’m not affiliated but I’ve used it before in course work. I hope it helps and good luck with your PhD!
Good and nice
Hey, I’m a recent graduate in statistics from Brazil. Working with data science in pharma (Market Access, a bit more business) would you be up to creating a discord or something to allow more direct communication among the community?
Stats Msc from Germany here, pondering a 2nd, part-time, Msc in Epidemiology, next month. Let's get together!
Thank you for your videos
If a student of yours asked you what is the relationship of statistics with propability .what will you answer?
I’d probably say something similar to:
Data is random and unpredictable. Statistics uses models based in probability to learn from the data, despite this randomness.
Huge simplification, but it gets at the bigger picture
Can u tell plz if this roadmap include master or phd degree in biostatistics?
And can u recommend to me some master degrees online ?
Do you need a background in biology to get a degree in biostats?
nah for most programs you’ll see the prerequisites are usually math classes. Most of the relevant biology you pick up once you start writing on applied problems, but not needed to get a degree
I am surprised you had to pay for your msc. Was it a professional rather than research role? I've never heard of an unfunded thesis based masters.
Also, Casella is insane for a first intro to probability, let alone math stats, especially without recommending any math books for background. I am mainly commenting this warn people from thinking it is a good idea.
It’s pretty common for students to have to pay for out of pocket for an MS, at least within my small niche of biostatistics. I would say most students were like that in my program. Perhaps this is a US thing?
Also, fair point about Casella. I chose it mostly because I had to use it in multiple programs, but I forget that I was using it in grad-level courses. Do you have a recommendation for a gentler intro to probability?
@very-normal That's fair, and it is good to see videos on the topic, so keep it up! I am from Canada, and generally, thesis based masters are funded here. I believe this is the norm outside the US as well, even within biostatistics. I think for an introduction to probability theory, two books are commonly used. The standard is Ross's A first course in probability theory, but I actually prefer Blitzstein's Introduction to Probabilty theory. It is free online and comes with video lectures. For math stats, I used Hogg's book. The first three chapters also act as a good introduction to probability theory.
Another advantage to the ladder two is the inclusion of R code as well as the variety of applied and theoretical questions. These two do assume familiarity with basic calculus up to multiple integrals and infinite series, but not much beyond that.
That being said, I used Casella for a second course in math stats in my undergrad, and the exercises are invaluable for anyone looking to get into research statistics.
I cannot express how jealous I am of you lol. Thank you for the recommendations, I really appreciate you took the time to give me some. I’ll look into these and pass these along if others ask as well
@@very-normalMy masters course uses Hogg and Tanis but I am using my Dad's old books (casella). 😂.
I think that Ross's First course should definitely be read before Casella because chp 1 already assumes one is familiar with some of the distributions. I also recommend Ross's Stochastic Processes as it felt like an "applied probability" course.
However, I think that one would need considerable amounts of mathematical background: I'd say Proofs by Cummings, Calculus by Spivak, (insert probability) Elementary Linear Algebra by Larson, Real Analysis by Jay Cummings, Linear Algebra done right by Axler, Advanced Calculus by Buck (insert casella) and Real Analysis by Carothers (insert stochastic processes) is a good way to get the math background so one does not struggle. (I am a math major BTW)
I did probability as a freshman in college and dropped it due to lack of proof writing abilities.
As someone interested in the Economics side, I liked Kutner's linear models and then Introductory Econometrics by Wooldridge after finishing casella.