To the Stars
This post continues the Intro to Java Programming series, Part I of which can be found here. This is based on work done for the Open University module M813 Software Development and the free Udacity course in Java Programming as referenced below.
References:
Introduction to Java Programming, San Jose State University available on Udacity
This post summarises technical learnings from the free Java Programming course on Udacity, which is recommended to gain the required programming foundations before starting the OU’s post-grad Software Development module, M813. These notes are in addition to those I’ve been making while studying the Udemy Java Developer course: see my posts here and here.
How Long is This Course?
According to Question 4 of Homework 0, this Udacity course takes 180 hours over 10 weeks = 18 hours per week. Really? I just knocked out Lesson 1 (of 7) in a couple of hours – and that included ‘blogging’ time as well as ‘doing’ time.
Here’s the Syllabus:
Lesson 1: Intro to Java
Delving Into JAVA for the First Time
I learnt some great Python programming skills via Coursera last year (and a bit of R programming too, although that felt too much like work so I’ve knocked R on the head for the time being). I’ve been busy in the background applying that Python coding knowledge to various web scraping & data mining/data analysis projects.
But what’s up next for me in 2017 as part of my wider career-change programme & associated Personalised Training Program?
Well, if you’ve been follow the Systems Thinking category you’ll know I’m studying Systems Theory (or at least one approach within the bigger Systems field, called Systemic Inquiry) with the Open University. That’s a formal post-graduate course, with assignments and deadlines and all that good stuff, so it’s taking up quite a bit of my time. I’m also working on the Udacity Nanodegree in Machine Learning. Can’t quite believe I still find time to do a finance job as well as all that (thank god it’s only part-time!) but it keeps the cash flow flowing and helps take the pressure off and allows me the luxury (actually, I would describe it a necessity) of retraining into a new field.
But one other item on my original PTP (Programming Stream) was to learn Java programming. I need to get that under my belt if I want to progress onto the next OU post-graduate module on my list: M813 Software Development. So I booked onto the best-selling Java Developer course on Udemy, and it’s time to crack on with studying that now. [Read more…] about Java Developer (from start to end)
Part of the Python specialisation capstone (see Refs below) is to recreate a simple web search engine, modelled on the original Google search ranking algorithm (you can read the short version of Page and Brin’s 1998 Stanford paper here). The Google algorithm placed emphasis on information obtained from the HTML “link structure and link text” of all links found in all indexed web pages, and to use this information “for making relevance judgments and quality filtering”.
Google search algorithm:
The basic premise of the algorithm is a probability measure, expressed in laymen’s terms as: “how likely is it that a random surfer would alight on this particular web page if they just randomly surfed through all links on all pages on the web until they got bored and gave up”. The algorithm itself includes a measure of all incoming links to a web page (i.e. the number of “citations or backlinks” to that page), enhanced by the quality-ranking of each of those in-coming citation links. In this way, the search algorithm defines an objective page rank or search ranking for each web page. [Read more…] about Simple Search Engine in Python
My simple Python Tagging Engine has now been updated to incorporate learnings about tuples, and incorporates simplified ways of sorting and returning counted values from a dictionary. Code and simple documentation available on GitHub: https://github.com/debkr/blog_tagger.
As I’ve been going along in this mission of discovery that is my mid-life career-change search, I’ve come to realise just how important Learning & Development really is to me. It’s become a key area for me to look further into – especially in response to my wholly-positive experiences of MOOC’s so far and in relation to my budding interest in all things programming- and technology-led.
I’ve been thinking more and more about the ideas Accelerated Learning and Augmented Learning (specifically in relation to an ongoing developmental Programming Project). In brief, this is about how machine learning and artificial intelligence can be used to help and empower humans, rather than just be there to make rich people more money and put more people out of jobs and into long-term unemployment or underemployment. [Read more…] about Learning About Learning
This post follows on from my earlier post Coding 101, and is my responses and learnings from the highly-recommended Python programming book and course by Charles Severance (see References below).
Jargon:
I’m working on a glossary here, but still very unstructured and massively incomplete so I suggest staying with Google for now .
Some necessary concepts:
Programming consists of sentences or statements, which may include reserved words, which tell Python what we want it to do, but will also include some values in both numerical and text formats. These take the form of either constants, whose values don’t change, and variables, [Read more…] about Coding 101 (part 2)