Free Statistics Courses on Coursera

Today, we’re very excited to announce that the Biostatistics Department at Johns Hopkins is offering three new online courses through Coursera. These courses are

• Data Analysis: Data have never been easier or cheaper to come by. This course will cover how to collect, clean, interpret and analyze data, then communicate your results for maximum impact.
  Instructor: Jeff Leek
• Computing for Data Analysis: This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.
  Instructor: Roger Peng
• Mathematical Biostatistics Bootcamp: This course presents fundamental probability and statistical concepts used in biostatistical data analysis. It is taught at an introductory level for students with junior- or senior-college level mathematical training.
  Instructor: Brian Caffo

These courses will be offered free of charge through Coursera to anyone interested in signing up. Those who complete the course and meet a passing grade will get a certificate of completion from Coursera.

Computing for Data Analysis and Mathematical Biostatistics Bootcamp will start in the fall on September 24. Data Analysis will start in the spring on January 22, 2013.

Sunday Data/Statistics Link Roundup (7/15/12)

1. A really nice list of journals software/data release policies from Titus’ blog. Interesting that he couldn’t find a data/release policy for the New England Journal of Medicine. I wonder if that is because it publishes mostly clinical studies, where the data are often protected for privacy reasons? It seems like there is going to eventually be a big discussion of the relative importance of privacy and open data in the clinical world. 
2. Some interesting software that can be used to build virtual workflows for computational science. It seems like a lot of data analysis is still done via “drag and drop” programs. I can’t help but wonder if our effort should be focused on developing drag and drop or educating the next generation of scientists to have minimum scripting capabilities. 
3. We added StatsChat by Thomas L. and company to our blogroll. Lots of good stuff there, for example, this recent post on when randomized trials don’t help. You can also follow them on twitter.  
4. A really nice post on processing public data with R. As more and more public data becomes available, from governments, companies, APIs, etc. the ability to quickly obtain, process, and visualize public data is going to be hugely valuable. 
5. Speaking of public data, you could get it from APIs or from government websites. But beware those category 2 problems! 

Bits: Betaworks Buys What's Left of Social News Site Digg

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GDP Figures in China are for "reference" only

GDP Figures in China are for “reference” only