Excel
Please work thru at least the first 18/19 lessons of the Excel 2013 / Excel 2016 introductory tutorial (not specific to statistics). The rest of the tutorials are useful, but I would consider more advanced than what we need for the class. [For those who will be using mainly MS Office 2007 or 2010, there are similar tutorials on the same site.] In years past, Excel has been the center of my version of the course. However, this semester it will be largely center around R.
excelnotes and excel2010_stats are guides more specific to statistics.
Python
Python is an object-oriented programming language that is becoming more common in data science. I suggest that you look at the code academy intro course when you get a chance. While this language will remain optional this semester, there may be opportunities to make use of it both in this course and in your future work with statistics.
R
You should install R and Rstudio on your machine. (Rstudio is a convenient user-friendly interface for using R.)
Some brief tutorials to get you going: 1a, 1b. Here is a nice portal for you to access all kinds of R resources. This is the somewhat comprehensive tutorial that I myself used.
This class is supported by DataCamp, an intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. You can select from over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalized feedback on every exercise. As part of the course, you have free access to all their courses for the duration of the semester. Here’s the link to our group. You will be receiving an invitation to join via email. Your first assignment will be the intro course on R. If you already know R and don’t need this course or the subsequently assigned intermediate courses, let me know and I can find alternative assignments for you.
