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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.

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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:

Code sharing options

As I’m proceeding with Python MOOC, I had to choose a way to share my code with my peers. There are many options in fact. Here are some of them:

GitHub 

This was recommended by the MOOC instructions. This is a multifunctional platform that allows you to create repositories, gists and forks, follow users, publish privately or openly, download codes and leave comments. What I also like about it is that you can follow users. For sharing homework gists might be the best option.

github

There are two shortcomings though:

  • You can’t publish your code without registration
  • Some users complain that the interface is a bit too complicated, so it takes time to get used to it

Pastebin

This is an extremely easy to use sharing tool. Actually, what you first see at the main page is a box where you can paste your code. You don’t have to register to do it (so you simply have a link that you can later share). You can also set the expiration time for each publication (from 10 minutes to never). And you can make it public, unlisted or private (for members only). You can also register if you like (I did to keep my homework in order).

Pastebin

Shortcomings:

  • I haven’t seen any commenting option, which might be good for feedback and revision while learning
  • I also couldn’t find any option to follow other members.

DPaste 

This is a very minimalistic service. You can’t register, you only can paste your code and save it. After you do it, it will stay there for 30 days and then it’ll be automatically deleted. So it’s good for quick sharing purposes, but not for continuous and systematic use.

dpaste

Also I recently found Bitbucket 

But I haven’t explored it yet. If anyone has some experience, please share. There are some explanations as to how to use it though: Bitbucket 101.

As for me, I’m currently using GitHub and Pastebin, because GitHub looks like a wonderful working space and Pastebin is good for sharing with those who are scared of GitHub:

Python MOOC – Week 2

I’m going to sum up the experiences of the past week and share what I managed to find out.

First off, I really like the way the MOOC is organised. Especially the way it encourages team work and p2p-learning process. First the instruction was to sign up for OpenStudy, which is very good in terms of mutual help and revision. But there’s a problem there. You can ask questions there alright, but you can ask only one question at a time. That is, after you asked your question, it appears on the questions wire and everyone can see it and answer it. But if you want to ask another question, you’ll have to mark the current one as ‘closed’ and only then you’ll have an option to ask a new one. ‘Closed’ means that it is removed from the wire shown by default (to the list of closed questions) and if you haven’t received the answer so far, there’s a chance you’ll never have it because nobody will notice the question.

2013-06-30 20_32_53-OpenStudy

Ah yes, also OpenStudy is often down, so you sometimes simply can’t use it.

But there are great options outside. First is that the MOOC organisers divided all MOOCsters into teams and provided them with mailing list addresses, so some questions cans be asked and answered in small groups and you have no limitations here.

Finally, there’s one more learning space I discovered only yesterday and haven’t tried yet, but it looks great. I mean Groups at Codecademy (you have to sign in to see the page). Although I’ve been using Codecademy for quite a while now, I didn’t know about their existence. Of course I immediately joined Python for Beginners group. I hope it’ll be a great experience.

Now a couple of words about this week’s homework. This week was rather challenging for me, because I was struggling to understand how loops work, especially the for loop. One of the tasks was to write a code that calculates exponentials using a for loop. Thanks to my team mates who helped me figure out what the task was about in the first place  – that is that the task should be executed without using the in-built exponentiation (**) option.

Now, I had dealt with for loops at Codecademy and found them rather easy. This is what I basically imagined:

for i in range(1, 10, 2):

    print i

So it does what you tell it to with all the items in a range.

But in this case a possible resulting code I got after many efforts (and quite a bit of guesswork, I admit) looks like this:

base = input("Enter base: ")

exp = input("Enter exponent: ")

x = base

for n in range(1, exp):

    x *= base

print x

So after I wrote it, I still had a question: how are for n in range(1, exp): and x *= base connected if there are no obvious operations in which n (the items from the range) are mentioned? The answer is obviously that they don’t have to be mentioned. That is, the for loop in this case is used to show the computer how many times the operation must be repeated.

This is what I realised after reading this awesome article about loops in Python. And I also realised that there’s a great way to see what programme does by adding print statements that reflect the process step by step. Like so:

base = input("Enter base: ")

exp = input("Enter exponent: ")

x = base

for n in range(1, exp):

    x *= base

    print x # This shows what's going on in the process

print x

So for instance if we have base 5 and exp 4, the output will be:

25

125

625

625

Also one of my team mates kindly recommended me to read Learning Python by Mark Lutz (I found out on the way that there’s a whole site about it).

Finally, I played with PyScripter IDE and explored some code sharing options, which I’m going to describe soon.

Oh, by the way, if some peers want to have a look at my whole homework (with the exception of optional tasks I’ll get back to them a bit later), it’s here: https://gist.github.com/ansakoy

Python MOOC – Week 1 UPD

I know by experience that ‘next week’ is always full of unpredictable work, sudden meetings and other distracting stuff, so I decided to do my best at the weekend to play with Python. Kudos to Codecademy, once again. The first week’s homework was really easy (but good for revision), while only a month ago I’d feel totally frightened by it.

Just tried out OpenStudy. I was absolutely resolved to be using IDLE for the rest of my life the course, but one peer there endorsed another IDE (Interpreted Development Environment) called PyScripter. So I went and checked it out. Not that at my level it made a huge difference, but I like it that PyScripter has a compact layout that works in one window instead of two, unlike IDLE that has separate shell and text editor.

PyScripter:

2013-06-23 19_19_51-PyScripter - module1_

 

IDLE:

 

2013-06-23 19_21_19-Python Shell

 

I think I’ll try using both and see which is best for me.

Also we had an illustrative task in natural language processing (exercise 1.11). We were given a sentence Alice saw the boy on the hill with the telescope. And we had to sketch the two possible interpretations of this sentence. Drawing in MS Paint with a mouse – what a pleasure!

Exercise 1

Python MOOC – Week 1

2013-06-23 07_43_22-The Mechanical MOOC – A Gentle Introduction to Python _ Free range open learning

So, a new (the fourth, as far as I understand) sequence of Python Mechanical MOOC officially started a week ago. This week happened to be extremely busy in my case, so I actually had less time for learning than I hoped I would. But thanks to the Codecademy lessons I took some time ago, the first bunch of tasks didn’t contain too much new information for me. But at the same time it contained quite a number of fascinating and revealing details. For one, I found out from this video lecture that some languages allow using false indentation. That is, unlike Python where indentation is the only way to make a script work properly, many other languages use punctuation to separate statements. But indentation is still required by convention to make a programme clearly readable and its semantics more obvious from its structure. So to make people think that the programme does something different from what it really does, some coders may use this false indentation e.g. in Java or C. But not in Python however.

Also, as I think that during these 8 weeks’ period Python is supposed to be my primary learning focus, I decided to take into account some additional Python courses that might provide a better understanding of what’s going on. One of them is Python Programming 101 at P2PU. And actually there’s a lot of additional information there. For instance, there’s a list of Python compatible text editors. What I like best about it is peer reviews of the editors they tried. So I’ll have to save this for the future:

But for now I’m using IDLE, because I don’t have enough time to try all of them right now. Although I’ve installed Notepad ++ just in case.

Also I’m looking forward to getting involved in OpenStudy communication, but I haven’t yet, because I’ve been a bit overloaded (like a + operator) with work.

Getting started with Python MOOC

idle

Finally I seem to have pulled myself together and started following the instructions of the Python MOOC, which officially begins on 17 June, but has already started sending some tasks for preparation. And they actually turned out to be rather instructive for me. For instance, after about a month of toying with Codecademy, I had a very vague idea of how to work with IDLE. I mean, I tried it, but it didn’t make much sense and I didn’t get into details, because I was quite happy with Codecademy Labs at my disposal. And I also found out that IDLE stands for “Integrated DeveLopment Environment” (but still I suspect it has something to do with Eric Idle from Monty Python Flying Circle). Also, I know there are several versions of Python, but I still can’t understand what it really means. Well, hopefully, I’ll find out later. Right now I had to install Python 2.6 (instead of 3.0 I installed previously), according to the course requirements.

What has been a really bad experience so far is the way to calculate square root by way of raising a number to the 0.5-th power. This procedure just won’t fit into my mind. Not yet anyway.

Now, I’ll just post here some links in order to save them: