βœ… Week 03 - Checklist

DS202 - Data Science for Social Scientists

Author

Dr. Jon Cardoso-Silva

Published

09 October 2022

Follow the suggested list of actions below to get the most out of this course:

Your Checklist:

  • πŸ“™ Read (James et al. 2021, chap. 3) to reinforce your theoretical knowledge of Linear Regression. The textbook is available online for free.

  • πŸ§‘β€πŸ’» If you already know linear regression from previous courses you have taken, why not take this knowledge to next level?

    • Try to find a dataset online that contains a numerical variable you could predict by fitting a linear regression to it. I will be curious to see what you find. Share your findings on the #week03 channel in our Slack.

  • πŸ–₯️ Before you come to the class, skim the W03 lab roadmap page to have an idea of what we are going to do.

    • This week, instead of just typing things in the terminal, we will use R Markdown. You can read about it here. This is also how you will be submitting solutions to formative and summative assignments in the future.

    • I will post solutions to the practical exercises at the end of the week.

  • πŸ’» Assess yourself: did you understand all the exercises in the lab?

    • If you are new to linear regression and you are enrolled in the Monday sessions, it is likely that you will struggle a bit in the lab. During the week, reserve some time to read about Linear Regression and then practice the exercises again.

  • πŸ“Ÿ Struggling with something? Don’t know what a particular R command do? Share your questions on the #week03 channel in our Slack.

    • I will also be posting follow up questions on Slack during the week.

  • πŸ“ Keep in mind that: after the lecture on Friday, 14 October 2022, we will post the first formative assignment on Moodle.

    • You will have until Thursday of the following week (20 October 2022) to submit your solutions.

    • This assignment is not marked, it doesn’t count towards your final grade, but you will receive feedback if you submit.

    • The assignment will have a similar format as the questions we explore in the lab.

  • πŸ‘¨β€πŸ« Attend the lecture. It will help you remember concepts more easily when revising later.

References

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in R. Second edition. Springer Texts in Statistics. New York NY: Springer. https://www.statlearning.com/.