The number of MOOCs on offer can seem a little overwhelming so here's a look at computing related ones that will start during April 2013.
They are all free and this month they are almost all from Coursera.
Follow the links for full course descriptions and video introductions from the instructors.
Coursera was founded in 2012 by Daphne Koller and Andrew Ng and re-runs of each of the co-founders' "flagship" courses, originating from Stanford University, start again this month.
Daphne Koller's Probabalistic Gaphical Models is a challenging course pitched at advanced rather than introductory level. It expects you to be able to program in at least one language and it also helps to have some previous exposure to basic concepts in discrete probability theory (independence, conditional independence, and Bayes' rule).
Beginning on April 8, It is an 11-week course with a workload of 15-20 hours per week. To cope with the workload one student repeated the class twice:
The first time I focused on the 'theory' parts, and did the problem sets and sat the exam, but didn't do the Matlab/Octave homework projects (which are quite large and a lot of work). The second time I focused on the 'practice' and did the coding exercises (I also had to re-sit the theory parts of the assessment the second time through, but at least these were a little easier - though not totally easy - having done them once before!)
Andrew Ng's Machine Learning, which starts April 22 has a stated workload of 5-7 hours for 10 weeks but having done this class myself I think that's an underestimate - unless you are already expert in programming Matlab/Octave. It takes a statistical approach to Machine Learning and you need to be confident with matrix algebra and calculus before embarking on it.
Also listed in the CS: Artificial Intelligence section of Coursera's catalog you will find Computational Neuroscience taught by
Rajesh P. N. Rao and Adrienne Fairhall of the University of Washington. This course, that starts April 19, lasts 8 weeks and requires 4-6 hours per week, sets out to introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain and claims that you don't need to have any prior background in neuroscience. Again Matlab or Octave, its free open source alternative, will be required for homework assignments.
Looking at the CS Theory courses about to start there's Introduction to Logic taught by Michael Genesereth of Stanford University starting on April 1. This is pitched as a basic introduction to Logic that shows how to formalize information in form of logical sentences; how to reason systematically with this information to produce all logical conclusions and only logical conclusions. It also examines logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. The only pre-requisite is "high school mathematics". You need to understand sets, functions, and relations and be comfortable with symbolic manipulation techniques, as used, for example, in solving simple algebra problems. The course last 8 weeks with a workload of 5-7 hours per week.
Another Stanford CS: Theory course, also starting April 1, is Social and Economic Networks: Models and Analysis from Matthew O. Jackson. This course has some basic prerequisites in mathematics and statistics and provides an overview and synthesis of research on social and economic networks, drawing on studies by sociologists, economists, computer scientists, physicists, and mathematicians, and will look at models of how networks impact behavior, including contagion, diffusion, learning, and peer influences.
The courses listed so far exemplify one of the great aspects of being a programmer - your core ability of being able to approach things algorithmically means you can range over a wide spread of topics. However, if you are looking for something more mainstream, April sees the start on April 15 of two programming courses.
The first, from Rice University and lasting 9 weeks is An Introduction to Interactive Programming in Python, intended as a fun introduction to the basics of programming in Python with the main focus on building simple interactive games such as Pong, Blackjack and Asteroids.
The second, an 8-week course from the University of Washington is The Hardware/Software Interface and examines key computational abstraction levels below modern high-level languages; number representation, assembly language, introduction to C, memory management, the operating-system process model, high-level machine architecture including the memory hierarchy, and how high-level languages are implemented.
The other Computer Science MOOC starting in April is the first anniversary re-run of Caltech's Learning from Data, a machine-learning course that is billed as a "real Caltech course" and "not a watered-down version" which has bi-weekly "live" lectures broadcast at the same times on Tuesdays and Thursdays from April 2 to June 11.
If you are looking for a grounding in computer science, it is worth bearing in mind that you can join on of Udacity's classes at any time and take it at your own pace. Udacity offers a beginner's level Introduction to Computer Science (CS 101) plus 10 intermediate-level courses and five advanced ones, all of them free to study.
If you are looking for a class on any topic the MOOC aggregator Class Central lists classes from Coursera, Udacity, edX and others and lets you filter them by stream (Computer Science/ Business & Management/Humanities etc.) It also categorizes them in terms of time allowing you to distinguish those that are Recently Started or Starting Soon from those In Progress or Finished.
A new site RedHoop, that currently aggregates courses from Udemy, lynda.com, Khan Academy and Coursera, invites you type in the topic of a class to search for and provides a one-click enrollment for any you are interested in. It doesn't have any information about starting dates but once you've signed up you receive information.
If you know of any courses, but they have to be computer relevant and fall into the MOOC category, I've missed email me and let me know. If you have taken any relevant courses and would like to express an opinion or write a review again just email me (Sue Gee)