#FLcoding16: weeks 3, 4 and course end

progressI recently took part in a FutureLearn course about learning how to code for data analysis. I really enjoyed the course and my interest in the programming language python was definitely piqued. I blogged about previous weeks of the course but in this post I want to summarise my experience of the second half and reflect on the end of the course. 

Week 3 of the course contained the content that at the outset I was most eager to learn: conditional statements (What if…). As an example we learnt how to write and test this kind of conditional statement:

if condition1:
elif condition2:

I arrived in the last week of the course and found a lot more to learn. While I didn’t manage to finish all the tasks of the week, I probably got the most inspiration out of this final part of the course. There was one exercise playing with pivot tables that was really interesting and the emphasis on showing us where to find and how to export large open data sets was what I wanted to learn about next.

All in all I learnt a lot during this course and the content and structure worked extremely well. Judging from the comments from others I was in the clear minority as I found the types of data we used rather uninteresting. But the useful list of resources in week 4 has pointed me to new possibilities. At 92% completion and with the last week being the only one which I did not complete fully, I feel the course delivered all I was expecting – and I recommend it if you are keen to try.

While I have plenty to be getting on with from the course materials, I also wanted to have a look around for other open courses and resources to help me learn more.

A quick search has led me to discover some possible next steps:

First, I found http://www.learnpython.org/ . As far as I can see the site covers basic tutorials as well as a tool into which you can enter code straight away.

Another search result led me to https://codeclan.com/ – a inititive which is more generally aimed at helping you learn how to code but also offers a specific Python course https://codeclan.com/courses/python/ . This is a much more committed programme of study and may not offer all the flexibility I am after.

A whole range of courses is also available from https://www.datacamp.com/courses?learn=python_programming including a course that includes a Python Data Toolbox, which sounds really interesting.

A more basic approach to starting with Python is also available via Code Academy https://www.codecademy.com/learn/learn-python .

Another open course provider, Coursera, also offers a set of courses on programming with Python from the University of Michigan. This is a seven week course which has recently started and covers basics for university students.

Plenty of options available to satisfy my curiosity and enable me to learn more about Python – probably many more that I haven’t yet discovered. One of the interesting aspects of looking at these sites is to find out more about how the techniques I am starting to learn about can be applied and what they are most useful for. I suppose what I really need to do next is to find a question that interest me and get started on my own. 

#FLcoding16: we meet graphs in the second week

week-2It’s the second week of the course ‘Learn to Code for Data Analysis‘ and we have started making graphs! Alongside my course participation on FutureLearn I am posting a short summary of my experience on my blog (you can read also my post from Week 1).

I found this week a lot quicker to get started, partly because I am now more familiar with the course structure but also because the Anaconda interface I am using is becoming easier to navigate. That was a good thing because I have less time this week. Picking tings up where we stopped in Week 1 this part of the course introduces new concepts and methods leading to learning how generate graphs using the plot function. I found generating my first few graphs and changing what they showed immensely satisfying. The data we are using this week is about the weather and the project of the week enables you to use weather data from your own location.

In order to practice some of the syntax and get more used to using the interface I have started creating my own fictional data set which I am experimenting with in a separate exercise book. Hopefully there will be more time to play with this and the weather project next week. For now, the course continues to engage and educate – enabling me to learn the basics at my own pace. One study resource that I have found particularly helpful is the weekly glossary. I have downloaded both of these and use them to help remember different concepts. See you in Week 3…

#FLcoding16: learn to code for data analysis week 1

flcoding1It’s the first week of the course ‘Learn to Code for Data Analysis‘ and I have just completed all required steps – so this short post is a quick reflection on my experience so far and it’s been a great week!

Once I got stuck in there were a couple of things that work extremely well for me: first, using the course instructions on the Futurelearn platform together with the Jupyter notebook and exercise data creates a clearly structured and engaging environment. I dipped into the comment sections for some exercises when I got stuck, but in general I found the first week’s exercises really interesting and helpful. I did end up getting things wrong and then learning from the errors that were generated, but it felt both challenging and achievable. Given that this is my first time with the software and the coding language, I think the course works extremely well. It provides a good balance between context and technical elements, both of which combined to draw me in even as a complete novice.

While I am interested in the data we were using in week 1 (mainly WHO data, similar to what I have come across during graduate study) what I found most rewarding was gaining a better understanding of the various approaches and how they can be applied to small and large sets of data. Again, the course structure demonstrates the thought process alongside the mechanical steps, which was very effective. My favourite interaction with the exercises was the little messages that appeared after completing one, referring you back to the course platform. It was a small thing, but it made a big difference to how easily I could navigate an alien environment.

I dipped into the course a couple of times during the week, first to have a look around and read some introduction material, then again to read some more and watch the first videos and then a third time to get stuck in with the actual exercises and quizzes. Sorting large tables by columns and calculating different averages was particularly satisfying… I installed the software etc the week before, so that things were ready when I got started. I liked the information and prompt provided to share the work from the course and I might do so in the following weeks, but this week I feel the whole process is still too new for me to want to share more than my thoughts.

So far, so good – a really engaging and rewarding first week with lots of practical knowledge gained. Looking forward to week 2!

23 things & coding… new open course adventures

Recently I have been writing about setting up a new CPD log using Google Apps for Education. After a couple of busy weeks (at ALT’s Annual Conference) I have been searching for a new open course to try for this autumn. I really enjoyed my experience taking part in the Digital Scholar course and now I am ready for a new adventure.

So, first up and already underway, I have registered for the 23 Things course from the University of Edinburgh. Recommended by a colleague on Twitter I found the course a really inspiring proposition.

While there are quite a few familiar topics, I hope it’ll be a good opportunity to keep up to date with current practice and find new tips and ideas. I might also see if any of my colleagues might join in and utilise the course to help us provide some internal CPD. One aspect of the course I am already finding really useful is the community blog.

Another new course I have signed up for gets underway next month and is run on FutureLearn for the Open University: Learn to Code for Data Analysis. More of a challenge given what little I know about this but it’s definitely an area I am keen to develop in. In addition to what looks like a great course tutor team and content, the course also runs on a platform that I have worked with myself and I am keen to experience another course as a participant. Judging from the hundreds of enthusiastic posts in the welcome forum, I am certainly not the only participants who is looking forward to the course getting underway and not the only novice either.