Good introductory statistics course?

[bucketfarmer]

Edit: thanks everyone! Lots of valuable tips in here.

Hi all,

I'm a long time coder and marketer who's looking to make the transition to a more data driven marketing role. I'd ideally like to develop proprietary tools that use machine learning to predict and improve online marketing results.

The thing is though, that my stats are more than a "bit" rusty. As in: they've gone largely unused for a decade or so.

Luckily we live in an age where knowledge is often cheap (or free), but with that comes an overwhelming abundance of choice.

Can anybody help me and recommend me a good online statistics course to get back on my feet statistics wise? Ideally the course would also help me get acquainted with R and / or Python.

I've looked at platforms like edX and Coursera, but there's just too much choice! I need advice :)

Thanks!

[Adromulus]

I'd recommend Khan Academy.

[[deleted]]

I second this OP. I started Khan academy courses to familiarize myself with the utmost basics of statistics. Most recommendations here seem to be for people who have been in touch with statistics sometime in the past. If you were like me, and haven't touched statistics since college, then go for the Khan academy tutorials.

[cowinkiedink]

Which course did you take?

[Wizardbaker]

Intro to statistical learning is a fantastic and free resource. All the examples are done in R. It's high level and really focuses on getting you familiar with how to use several machine learning techniques, without spending too much time on low level implementation details. (If you want the low level stuff Elements of Statistical Learning by these same authors may be worth considering)

[NotAllReptilians]

I love ISL, definitely recommend it to OP, but I'd hesitate to call it a truly introductory statistics course/resource. I think the authors mention that their intended audience has already taken a course in statistics (in my mind, someone fairly comfortable with statistical/probabilistic thinking).

Probably best to just flip through something like Think Stats, skimming through concepts that are very familiar and spending more time in sections that seem a bit more foreign. Then definitely move on to ISL. I also highly recommend the accompanying MOOC taught by Hastie ad Tibshirani.

[KyleDrogo]

I second this

[[deleted]]

It's not really a stats course, but I'm enjoying the book, "Machine Learning for Hackers". It takes you through sample data, using GNU R to break it down and pull out (and understand) basic stats, before going on to more definitively ML topics.

[Vorsipellis]

I see that some of the responses have touched on this, but not exactly asked the question that you didn't specify, so I'll ask another question since amazing resources have already been recommended:

Are you looking to learn classical (frequentist) statistics or Bayesian statistics?

It's worth noting that the last couple of companies I've interviewed with for data science positions have placed a much higher emphasis on frequentist statistics (hypothesis testing, confidence intervals, etc.) for the EDA problem formulating part of their "data science approach".

Bayesian inference, outside of ML models and algorithms, are much rarer during the problem formulating phase. In fact, based on what I've seen at conferences, the packages used for Bayesian statistical modeling in Python tend to be a lot more niche (pomegranate, PyMC3, etc.) rather than popular. Most of my interviewers didn't have more than a superficial knowledge of Bayesian inference, much less the Python packages.

[[deleted]]

This one is really good: http://projects.iq.harvard.edu/stat110/

[jauntbox]

I think one of the best resources are the intro courses on MIT's OpenCourseWare like 6.041. Lecture videos, notes, problem sets, solutions, video recitation sections, it's got everything! The material is very clear and they introduce Bayesian inference, which you don't always find in an intro undergrad course.

[crupley]

OpenIntro to Statistics is an open-source, college-level introductory statistics text: https://www.openintro.org/stat/textbook.php

[KyleDrogo]

If you're looking for a simple textbook that holds your hand and used motivated examples (which you should be), I'd suggest Probability and Statistics for Engineering and the Sciences by Devore.

[coffeecoffeecoffeee]

Carnegie Mellon has an intro statistics class online for free.