Teaching Approach and Expectations#
I’m assuming you are here because you want to be here and want to learn to program and acquire some foundational data science skills. In other words, I don’t believe you are taking this course just to earn credit towards your degree or as a “GPA booster”.
This belief influences how I believe you should view the members of the teaching team (instructor, TA). We are here to teach, evaluate progress, and also serve as guides and consultants. However, you should not expect that we will provide you with everything you need to succeed. For one, that is unrealistic, but more importantly, that would take you as the active learner out of the equation. You are expected to take responsibility for your own learning and performance in this course. Concretely, this means you must be actively engaged in the scheduled course activities, seek out the information you need, and even at times teach yourself how to do things. We, the teaching team, are not here to give you all the answers; we are here to provide you with the skills to learn and discover the answers you need. This may involve finding documentation, tutorials, and examples on the web, talking with your peers, or asking specific questions of the teaching team — questions that demonstrate that you’ve already tried to solve the problem yourself and have identified the thing that you’re stuck on. This is to say, you should not view the course instructor as a “sage on the stage”, but rather as your “guide on the side”.
For this course, we will be providing mini-lectures and tutorials–but again, your active participation is necessary to maximize the learning outcomes. The more you prepare, ask questions, and engage, the better we can hone in on how to help get you to the next level.
About the class format#
One of the key differences in the format of this course from other courses you may have taken in the past is that we will frequently employ a flipped classroom. This means that class time is not solely used for conveying information, the way a normal lecture-based class operates. In the lectures you are expected to code along with what is being demonstrated. Then, class time is used as an opportunity for students to discuss the course material, practice their skills, and ask questions from the teaching team. As a practical lab course, this course requires you to spend a lot of time at a computer, writing and debugging code to solve data science problems (rather than primarily memorizing). Think of the computer as your lab bench, with the added bonus that you can take it with you anywhere!
This section was adapted from Aaron J. Newman’s Data Science for Psychology and Neuroscience - in Python.