Introduction to Statistics

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Below are the top discussions from Reddit that mention this online Coursera course from Stanford University.

Offered by Stanford University. Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for ... Enroll for free.

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Taught by
Guenther Walther
Professor of Statistics
and 10 more instructors

Offered by
Stanford University

Reddit Posts and Comments

0 posts • 5 mentions • top 5 shown below

r/science • comment
4 points • greatleaderlmao

here's a high school level statistics course that covers the basics of how things are assessed.

Statistical methods are not perfect, but statistical methods are better than throwing your hands in the air and declaring defeat.

If you're able to do a controlled experiment, you give one group a vaccine and another group a placebo.

The stage 3 clinical trial for the Pfizer vaccine had ~43,000 people. For trials like that you can get away with counting the number of people who die in the unvaccinated group vs the vaccinated group. Also the number of hospital admissions... essentially a whole host of factors that you can keep track of.

So hypothetically if in one group of 20,000 people 3,000 reported getting seriously ill, 1000 of them were hospitalized and 200 died in a 3 month span, and in the same time span the other group had 150 people get seriously ill, 2 people land in the hospital and 0 deaths, then you'd generally conclude that it's stupid to self-select into the group that's having terrible life outcomes.

r/AskStatistics • comment
2 points • supp_noobs
r/universityofauckland • comment
2 points • IronFilm

Before taking it again, might be smart to look back at your history of taking it, and figure out WHY did you fail Stats 108 three times already?

Maybe you should take Stats 100 or Math 102 first before attempting Stats 108 again?

Perhaps also over the Christmas break you might like to take a Stats MOOC as well for prep?

Good luck! I'll be taking Stats 208 this summer school :-)

r/CoronavirusDownunder • comment
1 points • msmyrk

I'm not saying you should use the more advanced methods yourself. I am saying it's a bit rich to criticise the results of the study without understanding the maths they used.

I'm not meaning to insult you with my messages. I'm getting frustrated with a fundamental difference between us. There are a very few things I'm an expert at. I have a passing knowledge of a few more areas (including stats). I often have to deal in fields I'm not familiar, or have access to more qualified people than myself. In those cases, I'll absolutely defer to those people. And I'd instantly defer to you in an area you are more skilled or experienced. It's not about "faith", it's the fact I have to prioritise what I'm going to learn for myself, and what I am going to have to rely on others for.

I strongly suspect you didn't know there was such a thing as statistical analysis until yesterday. But rather than acknowledging that it's an area you don't know about, you dug in and sought to "prove" that your preconceptions were right. What frustrates me, is that you've had a lot of information spoon fed to you in this thread. I'm no stats expert, but clearly have a better understanding of this stuff than you. Any time I've pointed you to information or the writings of actual experts, you've ignored it, demanded I provide you something different, sought out evidence to contradict the experts, and complained that I won't explain a really advanced technique to you (which would require many hours or even weeks on your part to learn just the underlying concepts they're based on).

Seriously, if you want to learn about this stuff, start with the CDC course you linked to. That will give you a really good start on the epidemiological concepts but not the statistical analysis side.

It's the statistical analysis that allows us to develop these incredible medicines so quickly. Statistical analysis not only gives us a strong understanding of the benefits and risks of these medicines, but also tell us how strong our understanding is so we can decide if we're going to test things more or not.

If you want to learn the analysis methods, you'll need something more in depth. Something like from Stanford will set you up really well. It claims to be free, but I seem to recall something funky with Coursera's definition of "free". Your other options are uni courses, a night school, or your state's vocational training system (Like TAFE in NSW), but they're going to set you back a lot more than any Coursera subscription.

That Stanford course won't go into the specific methods used in these kind of trials, but looking at the syllabus it should set you up enough to be able to do your own simple analyses, and digest a lot of other ideas even just by reading their Wikipedia articles. The Sampling Distribution and Regression topics are reasonably advanced - the kind of stuff you'd see in a 1st year uni course, and form the basis of analysing most real-world stats.

r/ABCDesis • comment
4 points • buntyisbest

Sure. There's a couple of things you need to learn in order to develop a solid foundation in data analytics:

  • Statistics: You need to have a good working knowledge of basic and intermediary level statistics, including an understanding of mean, median, mode, etc., all the way up to sampling, distributions, linear regression, etc. The best course I can recommend for that is: Introduction to Statistics by Stanford University. Make sure to just audit the course, instead of paying for it. You'll have access to all the course materials and assignments. It's just that when you audit the course, you won't be able to submit your assignments and get graded for them, which is totally fine.
  • Programming Languages: You absolutely need to learn SQL and at least one of the following programming languages: Python, R or SAS. For SQL & R, I recommend taking this entire specialization: Google Data Analytics Certificate. Once again, you can access all the available courses under this specialization for free. But if you want to earn a certificate - which I highly recommend if you're new to Data Science - then consider paying for it. It'll cost you $40 per month and you should be able to complete the entire specialization within a span of 2 months, if you spend at least 20 hrs/week working on it. For Python, this is the specialization I would recommend for everyone: Python for Everybody. Also, make sure to learn the following Python packages: Numpy, Pandas, Matplotlib, Seaborn. Some great playlists to follow on YT: Pandas, Matplotlib, Seaborn.
  • Data Visualization: You need to be proficient in at least one data visualization software: Tableau or Power BI. You'll find a great course (course #6) on Tableau under the previously mentioned Google Data Analytics Certificate. Some great courses on Power BI on Udemy: MS Power BI Basics, Advanced DAX. Make sure you're on the lookout for Udemy's insane 90% off discounts. Those occur at least once a month and last for about 3-4 days.

Feel free to DM me if you have any further questions. I'm always happy to help!

EDIT: This guy produces quality videos on how to start a career in Data Analytics. So be sure to check him out: Alex the Analyst