The document summarizes the first class meeting of a data analysis course. It outlines the course goals of learning R and RStudio, expectations including weekly quizzes and assignments, and how students can succeed through preparation, practice, and participating in discussions. Remote participation and attendance policies are also addressed.

1. DATA ANALYSIS INWF ED
CLASS MEETING 1, 21 AUGUST 2017

2.  To achieve in this course.
 Installations of R and
Rstudio.
 Aims, processes, &
expectations for the
course.
 A brief rest break.
 Demonstration of data
science workflow.
 Discussion of participation
from remote location.

3.  Know what is
expected…and, then,
do it.
 Complete preparations
for class meetings.
 Practice after class
meetings.
 Post to Piazza.
 Engage in tutorials.
 Act audaciously.
TO DO…

4.  Know what is
expected…and, then,
do it.
 Complete preparations
for class meetings.
 Practice after class
meetings.
 Post to Piazza.
 Engage in tutorials.
 Tardiness or absence.
 Fear of action.
 Procrastination.
 Withdrawal from
engagement.
 Failure to
cooperate/collaborate.
 Lack of attention to
detail.
 Whining.
TO DO… TOAVOID…

6.  Both are supposed to be loaded on 202
Chambers computers.
 You should have R and Rstudio installed on
your own computer by today. Instructions in
Piazza and syllabus
(http://economywork.com/courses/data).
 Trouble with installations?After class or break!

9.  Modalities
• “Live” in 202 Chambers.
• Remote connection — Discuss after class meeting.
 Attendance
• Record on-time attendance, tardiness, absence.
• Important for Registrar and external agencies.
 Order
• Begin promptly at 6 — Quiz almost every week.
• One 15 minute rest break, or as needed.
• End at 9, or earlier.

14.  Weekly quizzes
• Twelve quizzes; only your best 10 scores retained.
• Emphasize low-level recognition and recall.
 Examinations
• Mid-term — One three-hour hour block on 16
October 2017; seek assistance from notes and
Internet “helpers ,” but no other person.
• Final — One three-hour hour block on 11
December 2017 ; may seek assistance from notes
and Internet “helpers,” but no other person.

15.  Practice assignments
• Eleven assignments; only your best 9 scores
retained.
• Chance to do data science with problems
requiring generalization from demonstrations in
class meetings.
 “A” grade assignment
• Required for PhD students; optional for others.
• Data science work specified by Passmore.

16.  No single textbook;
the world is our book,
our video.You can
expand.The Internet is
filled with R teaching
and examples.
 Open source, free
software.You can do
just about any type of
analysis found in
commercial software.
 Piazza for your Q&A. Get
help; help others.
 Opportunities to practice
through Practice
Assignments, “A” Grade
Assignment, and virtual
practice.
 Tutorials with the
TeachingAssistant.

17.  Teaching Assistant available approximately one
hour each week on Zoom.
 Teaching Assistant establishes schedule and
posts on Piazza.
 Discuss practice you have completed, other
queries to Piazza.

18.  Use Piazza exclusively:
• Public postings;
• Private, if personal
(grades, performance).
 No e-mail to Passmore
orTeaching Assistant.

19.  Tasks — the basis of your final course grade:
• Mid–Term and Final;
• Weekly Quizzes;
• Practice Assignments;
• AND, possibly, “A Grade Paper.”
 See link to “Student Coverage, Due Dates, Final
Course Grade Contributions, & Delivery Locations for
Submissions of Performance Requirements in Data
Analysis inWF ED 540, 2017” in online course syllabus.

21. If you are master’s or baccalaureate student, this gets you ≤A grade.
Of you are a doctoral student, this gets you ≤B grade.

23. “A” grade opportunity for PhD students; extra credit for others
PhD
MS,
MEd,
BS

24. TOUR OF DATA SCIENCE SKILLS DEVELOPED INTHIS COURSE

25. DATA ANALYSIS INWF ED
CLASS MEETING 1, 21 AUGUST 2017