So, what are you taking?

Currently Enrolled and taking seriously:

• MITx: 6.00x Introduction to Computer Science and Programming
• PH207x: Health in Numbers: Quantitative Methods in Clinical & Public Health Research
• Writing in the Sciences
• Greek and Roman Mythology

Took seriously and recently completed:

• Statistics One
• Computing for Data Analysis

Took seriously and completed in the past:

• Internet History, Technology, and Security
• Fantasy and Science Fiction: The Human Mind, Our Modern World
• Introduction to Artificial Intelligence
• Learning from Data (I dropped out about 2/3 of the way through, but that was more work than any other online course.  Plus, with fewer than 100 students completing the homework assignments, it wasn't really a Massive Open Online Course.)

Enrolled while course shopping but not completed:

Too many to list. Perhaps this explains why Massive Open Online Course (MOOC) completion rates are so low, ~10%. If they offer a "sample the course" option, they can collect more accurate course completion statistics.


I'm loving 6.00x from MIT. The content, interface and projects are excellent. If you have 8-10 hours a week to spare, I highly recommend this class.  This is the best use of the MOOC platform I've ever seen for the sciences.  In the first four weeks, we've implemented bisection search, Newton-Raphson, Hangman and Words with Friends (but really against a greedy search computer algorithm).  I can't wait to see what else we'll do in the next eight weeks.

 I've always wanted to learn more about biostatistics and epidemiology*. So far Harvard's Public Health 207 is underwhelming.  I can complete the homework before watching the often slow and confusing lectures.  Fortunately, EdX let's you speed the lectures up by increments of 0.25 between 0.75 to 1.50.  (Coursera allows [0.75-2.00].)  The CC transcripts also let you zip through review materials.

 I'm also taking Writing in the Sciences and Greek and Roman Mythology through Coursera.  I recommend WitS with Kristin Sainani for anyone that wants to write better; you need not be a scientist. Sainani has degrees in Philosophy, Statistics and Epidemiology and teaches BioStatistics and Writing at Stanford. Someone posted one of her talks in the discussion board for PH207 after a headscratcher of an explanation of life tables in the class.  I wasn't the only one confused by it.  Fortunately, a classmate posted a ppt that she found helpful and I laughed out loud when I saw Sainani's name on the opening slide.  She's an excellent teacher and writing coach.

Bad Dad and I have recently completed Statistics 1 and Data Analysis through Coursera and agree that both are very good and worthwhile.  We became comfortable with R after using it to complete homework and programming assignments for the two courses.   Stats 1 is geared toward the social sciences and Data Analysis is more useful for sciences that don't include science in their title.

However, Stats 1 is not easy.  I give kudos to Princeton's Andrew Conway for explaining the subtleties of experimental design in the social sciences.  There's less math, but plenty of mental heavy lifting.  As a result of Stats 1, this physical scientist has learned to respect well-designed social science experiments.  Conway is planning to repeat Stats 1, using the lessons he learned from the inaugural class.  I highly recommend signing up for that when it repeats.

*I had never heard of epidemiology until my senior year in college. One of my friends/classmates mentioned that she was applying to grad school in epidemiology at UC Berkeley's school of Public Health. Then my academic advisor in the Chemistry department told me that roughly one third of their graduates went on to med school, one third to PhD programs in Chemistry and the remaining third were evenly split between graduate programs in related fields like epidemiology and industry. Two mentions of epidemiology in one week!  I never forgot and always meant to go back and learn more about it later.