What is this course?
This course is an introduction to data science that is designed for
psychologists. It emphasizes statistical thinking and best practices.
–
Q - What data science background does this course
assume?
A - None.
–
Q - Is this an intro CS course?
A - Although statistics and computer science \(\ne\) data science, they are very closely
related and have tremendous of overlap. Hence, this course is a great
way to get comfortable with those topics. However this course is
not your typical course.
–
Q - Will we be doing computing?
A - Yes.
–
Q - What computing language will we learn?
A - R.
–
Q: Why not language X?
A: We can discuss that remotely over ☕.
Where is this course?
Contract Grading
- What is contract grading?
- Assessment based on effort
- More representative of the scientific process
- Specifics are in the syllabus and course notes
Diversity & Inclusion:
Intent: Students from all diverse backgrounds and
perspectives be well-served by this course, that students’ learning
needs be addressed both in and out of class, and that the diversity that
the students bring to this class be viewed as a resource, strength and
benefit. It is my intent to present materials and activities that are
respectful of diversity: gender identity, sexuality, disability, age,
socioeconomic status, ethnicity, race, nationality, religion, and
culture. Let me know ways to improve the effectiveness of the course for
you personally, or for other students or student groups.
–
- If you have a name or set of pronouns that differ from those that
appear in your official records, please let me know.
- If you feel your performance is being impacted by your experiences
outside of class, please don’t hesitate to come and talk with me. If you
prefer to speak with someone outside of the course, your advisor is an
excellent resource.
- I (like many people) am still in the process of learning about
diverse perspectives/identities. If something was said in class (by
anyone) that made you feel uncomfortable, please talk to me about
it.
How to get help
- Course content, logistics, etc. discussion on the course discussion
forum.
- Please post on the FAQ instead of direct messaging.
- Use proper formatting: When asking questions involving code, please
make sure to use inline code formatting for short bits of code or code
snippets for longer, multi-line chunks.
- Often it’s a lot more pleasant an experience to get your questions
answered in person. Make use of my remote office hours, I’m
here to help!
Tips for asking questions
- First search existing discussion for answers. If the question has
already been answered, you’re done! If it has already been asked but
you’re not satisfied with the answer, add to the thread.
- Give your question context from course concepts not course
assignments.
- Good context: “I have a question on filtering”
- Bad context: “I have a question on HW 1 question 4”
- Be precise in your description:
- Good description: “I am getting the following error and I’m not sure
how to resolve it -
Error: could not find function "ggplot"”
- Bad description: “R giving errors, help me! Aaaarrrrrgh!”
More Tips for asking
questions
- You can edit a question after posting it.
- Format your questions nicely using markdown and code
formatting.
- Where appropriate, provide links to specific files, or even lines.
- Sharing code will help others understand your question.
Sharing/reusing code
- I am well aware that a huge volume of code is available on the web
to solve any number of problems.
- Unless I explicitly tell you not to use something, you can use any
online resources (e.g. StackOverflow, RStudio Community), but you must
explicitly cite where you obtained any code you directly use (or use as
inspiration).
- You are welcome to discuss the problems together and ask for advice,
but you may not create code for your classmates.
- You won’t learn anything if other people write your code for
you!
