It’s been quite an intensive period recently. First, I was having two parallel courses at Coursera – on data analysis and on statistics. Second, Irina Radchenko and I were preparing to launch a new Russian-language data expedition under our Datadrivenjournalism.ru project and then we were actually coordinating it for two weeks (9 – 23 December). Third, I suddenly had a huge task at work with a really tough deadline, which actually ruined my plans a bit, but thankfully not all of them. So here’s a brief account of the resulting layout:
I had to drop the data analysis course after its sixth week. Due to that sudden workload I couldn’t afford doing the second assignment, which was somewhat upsetting. But on the other hand, I think I’ll be able to do it later either on my own or within the course iteration (I’m almost sure it’s going to be launched soon again). Anyway, I’m glad I’ve done at least something, because it turned out to be rather helpful, especially in terms of structuring things and my mind. And yes, the previous course Computing for Data Analysis (on R) was extremely helpful. (For those who might be interested: the next iteration of this course starts on 6 January 2014.)
On the other hand, I triumphantly completed Statistics One course and that’s really cool. There are contradictory reviews of this course online. Some of them claim that the course is inconsistent in terms of difficulty: sometimes too easy and even boring, sometimes too complicated. Well, after completeing it, I can’t say that I’ve digested all the material provided. But now I have a better vision of what statistics is like and how it approaches data. Also I can apply some techniques for data analysis with the help R, but I wouldn’t claim I completely understand the mechanisms underlying some of these operations. Next I’m actually going to focus on Open Intro Statistics, which is a great textbook, and revise the material in order to pack it into my head. To wrap up this segment, I’ll add that the material that had been provided within that course by the middle of the semester was enough to complete assignment one in Data Analysis course.
As to the data expedition, it was luckily completed yesterday. Its organisation was considerably different from the previous experience and demanded quite a bit of in-advance preparation, apart from participation as it is. Although I couldn’t participate in it myself as thoroughly as I would want to, I still have to admit that the result somewhat exceded my expectations. I’ll be writing about it in a greater detail after I analyse the the whole picture. For now I can say that the timing was horrible. So the lesson is: never launch learning projects right befor Christmas or the New Year. But nonetheless there are some very inspiring results and the participants were virtually great.
Also, here are some links as usual:
- Books on statistics and R: A very nice collection of downloadable books.
- Class Central: A helpful MOOC aggregator that searches for MOOCs by subject and across multiple platforms.
- I’m really tempted to take this Stanford MOOC on Statistical Learning. It begins on 21 January 2014 and will last for 8 weeks. And here’s a free PDF version of the textbook for this course. As far as I understand, it’s a lite version of The Elements of Statistical Learning.
- Lastly, they say a MOOC on data driven journalism provided by Datadrivenjournalism.net is going to be launched in ‘early 2014’. I’m not sure I’ll be able to afford to participate, but might be interesting.
And merry Christmas everyone who celebrates it now!