The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis. I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data, courtesy of Redditor fevaah , showing old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't). If this idea was help...
I'm an SPSS girl. I sit in my Psychology Department ivory tower and teach Introduction to Statistics via SPSS.
SPSS isn't the only way to do the statistics. In fact, it is/has been losing favor among "real" statisticians. I recently had a chat with a friend who has a Ph.D. in psychology and works as a statistician. She told me that statsy job postings rarely ask for SPSS skills. Instead, they are seeking people who know R and/or Python.
In order to better help our data-inclined students find work, I've gathered some information on learning R and Python. This probably isn't for every student. This probably isn't for 90% of our students. However, it may be helpful for an outstanding undergraduate or graduate student who is making noise like they want a data/research oriented career. Alternately, I think that an R class could be a really cool upper-level undergraduate elective for a select group of students.
Also, if anyone is brave enough to teach their undergraduate statistics students R, email me, I would love to pick your brain (hartnett004@gannon.edu).
Note: I have not tried out all of the resources I am listing (ain't nobody got time for that) but they ARE all interactive. Some require registration, some don't. All are free. Some are brief, some require several hours to complete
R
http://tryr.codeschool.com/
https://www.datacamp.com/
https://www.coursera.org/course/rprog
Python
http://www.codecademy.com/tracks/python
UPDATE: I received good feedback and suggestions from my blog readers (see below).
-Via Twitter, Michael Philipps suggested JASP Statistics, free data analysis software that acts a lot like SPSS.
-Via the Comment section, Juanjo Medina suggested this blog posting by Jeromy Anglim for more information on switching to R. http://jeromyanglim.blogspot.co.uk/2013/07/evaluating-potential-incorporation-of-r.html#more
Another resource for to get us as well as our students up to speed on different software and statistical techniques is Coursera, home of many a free MOOC. Here is a link to statistics and data analysis classes that will be taught in English. The classes cover an array of topics, including R as well as specialization topics in statistics.
SPSS isn't the only way to do the statistics. In fact, it is/has been losing favor among "real" statisticians. I recently had a chat with a friend who has a Ph.D. in psychology and works as a statistician. She told me that statsy job postings rarely ask for SPSS skills. Instead, they are seeking people who know R and/or Python.
In order to better help our data-inclined students find work, I've gathered some information on learning R and Python. This probably isn't for every student. This probably isn't for 90% of our students. However, it may be helpful for an outstanding undergraduate or graduate student who is making noise like they want a data/research oriented career. Alternately, I think that an R class could be a really cool upper-level undergraduate elective for a select group of students.
Also, if anyone is brave enough to teach their undergraduate statistics students R, email me, I would love to pick your brain (hartnett004@gannon.edu).
Note: I have not tried out all of the resources I am listing (ain't nobody got time for that) but they ARE all interactive. Some require registration, some don't. All are free. Some are brief, some require several hours to complete
R
http://tryr.codeschool.com/
https://www.datacamp.com/
https://www.coursera.org/course/rprog
Python
http://www.codecademy.com/tracks/python
UPDATE: I received good feedback and suggestions from my blog readers (see below).
-Via Twitter, Michael Philipps suggested JASP Statistics, free data analysis software that acts a lot like SPSS.
-Via the Comment section, Juanjo Medina suggested this blog posting by Jeromy Anglim for more information on switching to R. http://jeromyanglim.blogspot.co.uk/2013/07/evaluating-potential-incorporation-of-r.html#more
Another resource for to get us as well as our students up to speed on different software and statistical techniques is Coursera, home of many a free MOOC. Here is a link to statistics and data analysis classes that will be taught in English. The classes cover an array of topics, including R as well as specialization topics in statistics.