In case you missed the announcement yesterday, Coursera added 12 new universities and over 100 new courses. The exciting part for people learning data science is a new category of courses: Statistics, Data Analysis, and Scientific Computing. None of the courses have started yet. Most are scheduled for this fall or early 2013. The courses look very good.
Are you excited about these new courses?
Last week, Udacity started a course on Introduction to Statistics, Making Decisions Based on Data. This is a beginners level course on statistics, so it should be accessible to everyone. The course consists of seven units, which are intended to last about one week each. Udacity does not enforce any time limits though. Homework problems are also a part of the course, so you will get a chance to practice what you learn.
Udacity is a learning environment similar to Coursera. I would say the presentation is more focused on the web and the experience is a bit more enjoyable. Courses at both sites are taught by professors from top universities and other leading experts in the field. Both sites offer lots of knowledge for free, and I say try them both. Then let you own personal preference decide which you like better.
What do you think about Udacity? Have you tried it?
Yesterday, I posted about some traditional strategies to acquire data science skills. Today, I will post a nontraditional strategy.
There is hoards of data science information available on the internet for free. With enough personal motivation, a person could learn all the skills necessary for free (or cheap) online. Coursera is probably a great place to start. There are also other good sites such as Udacity, the Kaggle Wiki, other blogs and websites.
The problem with this approach is knowing exactly what to learn. A course in machine learning is great, but data science is more than just machine learning. How do you know what to learn? It would be really nice to have a collection of data science topics and the associated online training materials.
Would this strategy work for you?
May Strata 2012 will occur online this year. The cost is zero, and the event is tomorrow (May 16, 2012). The only catch is that you must register first. The entire conference is scheduled to take place in the morning, so the format looks quick. Judging from other Strata videos I have seen, I would guess this will be an event of high quality.
Thanks to DataGeeks-MSP for alerting me to the conference.
Just yesterday, MIT and Harvard University announced a new partnership to offer online education. The goal is to increase learning for students on-campus and others throughout the globe. Both schools plan to study the results of edX to better understand how students learn and how technology affects learning.
See the official announcement here.
EdX Video Announcement
How will this affect Data Science Learning?
It is too early to know exactly what courses will be offered, but given MIT’s strength in engineering, those courses would seem reasonable. I am guessing (and hopeful) that many courses pertinent to data science will be offered by edX. Also, the announcement is most likely a response by MIT and Harvard to compete with Coursera, a company started by 2 Stanford University faculty. Obviously, the elite schools do not want to be outdone by each other. In any case, I only see these new and different methods for education as a good thing. Happy Learning!
Having trouble keeping track of what schools offer what courses for free online? Problem solved!
Class Central maintains a updated list of courses from Coursera(Stanford), Udacity, MITx, and others as they become available. Not all of the courses are related to data science, but I still thought it was valuable to share the link.
Check it out and start learning.
Previously I mentioned that online statistics learning resources are not abundant.
Well, here is a new online book for learning statistics. It is geared towards programmers, and it looks to be a great fit for people wanting to learn data science. Here is a small excerpt from the Preface:
It emphasizes the use of statistics to explore large datasets.
I have only had time to quickly browse the book, but it looks quite good.
Think Stats: Probability and Statistics for Programmers
(The book has a Creative Commons license, so it is free and OK to download)
Statistics – This is a topic that could use some more attention from the online community.
I would love to see Stanford (or Coursera) offer a free statistics course online much like the other free courses online.
I did find a series of Youtube videos by Daniel Judge, a Professor in the East Los Angeles College Mathematics Department. The videos start at the very beginning of statistics. I have watched a couple of the videos, and they seem quite good. Daniel does a nice job of explaining the information. Here is the first video in the series.
Stay tuned to the blog in case other stats options appear online. Also, please leave a comment if you know of some good online statistics resources.