Unlock the data driven power of networked computer systems and smart cards.
Everything we do generates data, from shopping at the supermarket, riding on public transport, to phoning a friend. This data is opening up a new era for our understanding of human behaviour, policy making and business processes which depend upon this understanding. Research shows how data can give us insight into the risk of an upcoming stock market crash; decrease delays in measuring the spread of illness; or even allow us to predict where crimes may occur. You will gain an overview of the state of the art in big data research across a range of domains, including economics, crime and health.
The course is designed to be accessible to all.
To participate in the exercises that start in Week 2 you will need to install the free software ‘R’ and ‘RStudio’. Practical skills are taught via video in a walkthrough fashion, with both video tutorial and written instructions describing and demonstrating the simple steps learners should follow. Learners will also need to make sure they have a (free) Google account to access and download raw data. These exercises are not a requirement of the course, but will provide you with knowledge needed for the tests.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
is taking this course right now, spending 2 hours a week on it and found the course difficulty to be medium.
Very interactive and engaging course. This is the first course I've ever taken where the comment section is not about students asking questions, but more about students reflecting on what they learn, areas where big data can be used, and the potential ramifications of big data. The pace of the courses are easy to follo
Very interactive and engaging course. This is the first course I've ever taken where the comment section is not about students asking questions, but more about students reflecting on what they learn, areas where big data can be used, and the potential ramifications of big data. The pace of the courses are easy to follow and the videos are downloadable to watch whenever and view the comments later. I really can't stress how interesting and smart the commenters are. It really motivated me as a user to read the articles and chime in with my opinion.
There is a new, interesting Big Data topic for each week (from disease to crime to stock markets to happiness, etc) and at the end of each week, the presenters compile some of their favorite questions and answer them in a recorded webcast.
The average course week involves watching ~45 minutes of video, reading 1-2 pdfs and maybe an activity to complete in R. The reading isn't too lengthy and the videos aren't too long either. Another thing I like is that the course is varied with videos of interviews, lectures, TedX talks, etc.
Some people find R a little frustrating but the code is written out exactly as it needs to be if the videos on R are too fast to follow. Stick with it because R is well worth the time investment.