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.

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Design troubles

I must admit I don’t like infographics. I feel genuinely jealous when people look at a picture and find it more revealing and comprehensive than text, because I never do. I even thought I might need a special tutorial on how to read infographics. I’ve seen quite a number of online instructions about how to read newspapers or how to make yourself read a text on the Internet. I’ve got no problem with it. And having been rather far from design issues until recently, I didn’t bother about ‘reading’ pictures at all. But now, it’s another story, as I’m trying to study data journalism. And data journalism, among all, presupposes visualisation. Not necessarily of course, but still I must learn some basics. Besides, I must learn how to ‘read’ others’ work. In order to do so, I need at least to try using some design elements for expressing or illustrating my ideas from time to time. And not only when it comes to data visualisation as such.

Thankfully, there are people who are willing to share their knowledge. So, when I saw this post by Denise Cheek I took it as a challenge. She actually posted her awesome Creative Process Cheat Sheet and asked her audience what their creative process looks like. Although the easiest way for me would be to simply describe the process word by word, I decided to make it in a kind of a similar cheat sheet format, whatever awkward the result might be. Which I actually did (see below). Not that I really enjoyed the process or the result, but at least I tried. Denise, thanks for providing me with a task.

Now, if someone has ever faced similar problems, please share your methods to deal with them. If, on the contrary, you like infographics, it would be great to hear how exactly you read it.

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Data Expedition Recap

I can hardly believe it, but my assignment at School of Data seems to be completed. The last step was to produce some output, that is to tell the story. Now I think I should somehow summarize my experience.

Now, first off, what is Data Expedition at School of Data? It can be very flexible in terms of organisation. Here are the links to the general description and also to the Guide for Guides, which is revealing. In this post, I’ll be talking about this particular expedition. Also, a great account of it can be found on one of my team mates’ blog. So, this expedition was technically very similar to the principle of Python Mechanical MOOC. All the instructions were sent by a robot via our mailing list and then we had to collaborate with our team mates to find solutions.

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(Image CC-By-SA J Brew on Flickr)

First of all, we were given a dataset on CO2 emissions by country and CO2 emissions per capita. Our task was to look at the data and try to think about what can be done about it. As a background, we were also given the Guardian article based on this very dataset so that we could have a look at a possible approach. Well, I can’t say I was able to do the task right away. Without any experience of working

with data or any tools to deal with it, I felt absolutely frustrated by the very look of a spreadsheet. And at that stage peers could hardly provide any considerable technical support, because we all were newbies.

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Then we had tasks to clean and format the data in order to analyze certain angles. Here our cooperation began and became really helpful. Although nobody among us was an expert here, we were all looking for the solutions and shared our experience, even when it was little more than ‘I DON’T UNDERSTAND ANYTHING!!11!!1!’.

Our chief weapons were:

  • the members’ supportive and encouraging attitude to each other
  • our mailing list
  • Google Docs to record our progress
  • Google Spreadsheets to work with our data and share the results
  • Google Hangout for our weekly meet-ups (really helpful, to my mind)
  • Google Fusion Tables for visualisation (alongside with Google Spreadsheets)

And that is it actually. I’m not mentioning more individual choices, because I’m not sure I even know about them all.

Now some credits.

Irina, you’ve been a source of wonderful links that really broadened my understanding of what’s going on. And above all, you’re extremely encouraging.

Jakes, you’ve contributed a huge amount of effort to get the things going and I think it paid off. You have also always been very supportive, generous and helpful even beyond the immediate team agenda.

Ketty, you were the first among us who was brave enough to face the spreadsheet as it is and proved that it is actually possible to work with. I was really inspired by this and tried to follow suit. Same was in the case of Google Fusion Tables.

Randah, I wish you had had more time at your disposal to participate in the teamwork. And judging by your brief inputs, you would make a great team mate. You were also the person who coined the term dataphobia and in this way located the problem I resolved to overcome. I hope to get in touch with you again when you have more spare time.

Zoltan, you were also an upsettingly rare contributor, due to your heavy and unpredictable workload. But nevertheless, you managed to provide an example of a very cool approach to overcoming big problems just by mechanically splitting them into smaller and less scary pieces.

Vanessa Gennarelli and Lucy Chambers, thanks for organising this wonderful MOOC!

So, as a result, I

  • seem to have overcome my general dataphobia
  • learnt a number of basic techniques
  • got an idea of what p2p learning is (it’s a cool thing, really)
  • got to know great people and hope to keep collaborating with them in the future

Well, this is kind of more than I expected.

Next, I’m going to learn more about data processing, Python, P2P-learning and other awesome things.

Data journalism: Learning insights

Today my learning is focused on data journalism (I’ve got to finish my story as a challenge within Data Expedition). And also, today I decided to have a look at the product rather than the technique, as I previously did. To this end, I went to read Guardian Datablog and it seems to be quite an enlightening experience.

But first off, I have to give credit to Kevin Graveman, whose post actually provoked me to think in this direction. Kevin gave some tips on learning CSS by looking at both HTML and CSS sources of a page and also comparing it to the way the page looks in order to better understand how it works.
Now, this approach (quite natural, but not always obvious) can be replicated in many other areas. So today, I’m applying it to The Guardian by learning the anatomy of their data driven materials (just as if I was looking at the source code of their product). And I’m also making notes about my observations on the way.

  1. They ALWAYS provide links to their datasets. Under each piece of visualisation, they post a link to a small particular spreadsheet with the data regarding this piece.
  2. After the article they also provide a link to the full spreadsheet.
  3. A spreadsheet contains not only data, but also notes (on a separate sheet) with sources and some explanations. Like so  (for this article).
  4. Guardian Datablog is a great source of datasets. Although somewhat random.
  5. But these datasets are not always very trustworthy.
  6. Their visualisations are normally interactive.
  7. Some entries to the blog are very short in terms of writing, but provide complicated visualisations. Others rely on text substentially.
  8. Most underlying datasets in the materials I’ve seen are organised as single Google spreadsheets with several sheets (or tabs) containing particular spreadsheets. A good example is a recent Simon Rogers and Julia Kollewe’s material. The dataset is here.
  9. It seems to be a good idea to place some charts on separate sheets. (In order to do this, l-click the chart anywhere to open the quick edit mode, then hit the small triangle in the top corner on the right and choose ‘move to own sheet’.)

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Python: An Upcoming Mechanical MOOC

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I’ve just had an astonishing experience. I was kind of looking for a pic for this post and I decided to be trivial and to simply use Python logo. It can’t be a problem to find it online, can it? Just type “python” in Google, switch to images and here you are. Oh wait. There are also snakes called pythons…

*Okay face*

I had totally forgotten about their existence.

I won’t post those pythons here, because I know some people are afraid of snakes and detest the way they look. Although I’d love to actually.

Now, what I was actually going to say is that a cool Python mechanical MOOC is just about to start. I’ve already subscribed. It’s beginning in June and, judging by the archive of its previous round, it lasts 8 weeks. What is special about this course, is that there are no instructors there whatsoever. But there are peers with whom you can discuss the learning problems, tasks and what not. And well, there’s also a great Q&A Forum at Codecademy. And many other forums and communities online.

By the way, the link to this MOOC was kindly sent to me by my awesome Data Expedition teammate. That’s what I call a p2p community.