Upd

I haven’t posted anything for quite a while, but actually I keep learning. It’s always somewhat sad to see these abandoned blogs created for peer-learning with a couple of posts and then no updates, so you just don’t know if their authors are still learning or gave up on it. Well, I haven’t. True, I’m more into platform MOOCs at the moment, so I’m not using this blog for peer-learning purposes directly. But I generally like this international open peer-learning project and I’m going to update this blog from time to time.

There’s a good occasion for this post: I’ve just completed An Introduction to Interactive Programming in Python at Coursera. I’ve finally done it having failed two previous attempts. It was challenging and I’m not sure I’d have made it if I hadn’t done some preparational job at Codecademy and with the help of Zed Shaw’s ‘Learn Python: The Hard Way‘ (a great educational project by the way).

Just for show, here are the links to the mini-projects I completed during the course. I’m providing the links to my code in Codesculptor, an online application created by one of the instructors for writing and running Python code. In case someone wants to have a look, the best way to do it is by using Chrome (using Mozilla and other browsers may lead to some bugs).

This is actually the first part in Fundamentals of Computing specialisation. Next course in this sequence, Principles of Computing, is going to start in February 2015 and I’m totally going to try it. Before it begins, I’m going to have some fun at Khan Academy.

Finally, some courses I’d like to have a closer look at at a certain point. Maybe someone will find them fascinating as well. If somebody has already dealt with some of them, it would be great if you shared your opinion.

Big plans for my 2nd semester

As the previous experience has shown, it’s hard to cover more than one course in one semester (this way of measuring my learning time seems most appropriate), if you have to work at the same time. Or rather one course and a half. Last semester, these were an introduction to statistics and a bit of R. Initially, I had huge plans for the upcoming semester. While learning statistics and earlier some Python basics I got a bit tired of constant guesswork and having to learn separate pieces of underlying fundamentals, without getting the whole picture. So I totally felt like taking two basic courses in this semester, namely some refresher in math and some intro to computer science.

As to computer science, I really liked the description of CS50, a Harvard CS course by David Malan, which has its online representation both as a static archive and as a MOOC at edX.org. The thing is that:

  • it lasts 10 weeks
  • it has 2 lectures every week, about an hour long each
  • it has 1 seminar a week, about an hour long
  • it includes 9 problem sets, estimated completion time 10 to 20 hours each
  • it includes 1 final project

Well, that’s definitely not what I’m likely to be able to cover before summer, especially if it is combined with a math course. Time for tough decisions. After some hesitation I decided that math comes first:

  • as a more basic subject
  • the thing I really needed while learning stats
  • more realistic to complete by the end of this semester.

There are actually two courses that seem quite appropriate for my needs (and I need to refresh some real basics):

I’m not sure about the latter, but Precalculus looks very promising in terms of at least answering some unresolved questions (simple, but very annoying) I already have after dealing with statistics.

So that’s what going to be my core subject for the semester, just like Statistics was last semester. Now, what about the remaining ‘half a course’ to complete my schedule? Well, I failed to complete Data Analysis last semester and I also want to have some revision of what I learnt about statistics last year. That’s what I think I’m going to be dealing with for the rest of my learning time. Stanford is offering a course in statistical learning (as far as I understand this stands for statistics combined with some machine learning approaches). I hope it won’t be as challenging as it could be after I have acquired some basic skills in handling R (and this course is based on R).

So these are my one and a half courses I’m going to take in this semester. As to CS, I do hope to take it in the summer.

A couple of links for those who also might need some school math refresher:

Is it Christmas already?

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:

And merry Christmas everyone who celebrates it now!

Links Links Links

A new bunch of links to the resources regarding statistics etc. that seem to me helpful:

Introduction to Statistics

This is an archive of an introductory statistics course at Coursera Statistics: Making Sense of Data by Alison Gibbs, Jeffrey Rosenthal (University of Toronto).

The authors of the course kindly provided a list of recommended literature. I don’t think it would be a crime to reproduce it here. So, they recommended three ‘traditional books’:

  • Introduction to the Practice of Statistics, by David S. Moore and George P. McCabe. (The book is currently in its fifth edition, but any edition will do.)
  • Stats: Data and Models, Canadian edition, by Richard D. De Veaux, Paul F. Velleman, David E. Bock, Augustin M. Vukov, and Augustine C.M. Wong. (The original version of the book, by the first three authors only, is also recommended.)
  • Statistics, by David Freedman, Robert Pisani, and Roger Purves.

And three online resources:

  • OpenIntro Statistics, by David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel. The cool thing about this one is that it’s not just a book, it’s a whole learning tool including labs and some instructions on using R.
  • Online Statistics Education, by David M. Lane, David Scott, Mikki Hebi, Rudy Guerra, Dan Osherson, and Heidi Zimmer
  • HyperStat Online, by David M. Lane
  • StatPrimer, by B. Burt Gerstman

R

Statistics and Python

And last, a couple of books kindly recommended by a great person at P2PU. These connect statistics to programming in Python: