🔖 Week 01 - Appendix

DS202 - Data Science for Social Scientists

Author

Dr. Jon Cardoso-Silva

Published

05 September 2022

Indicative Reading

This week’s indicative reading: (James et al. 2021, chaps. 2, 2.1–2.2)

Recap

Need to recap probability and statistics concepts? Check the suggested readings below:

(Warne 2018, chaps. 1-3,5,6,11-12)
(Gelman, Hill, and Vehtari 2020, chaps. 1–4)
If you are a PBS student, you can revisit the content of PB130 (MT3, MT4, MT8-MT11)

Recommended (additional) reading

What are different ways one can approach a modelling problem?

Checkout the upcoming book ‘Modeling Mindsets’ (Molnar 2022, chaps. 2–3) (it’s free to read online) to learn about the traditional frequentist statistics vs Bayesian statistics vs Machine Learning approaches.

The following twitter thread also summarises the main points of these different paradigms:

In a perfect world, you could effortlessly switch between modeling mindsets (statistics, machine learning, causal inference, …).

Realistically, you only have time to master a few mindsets.

So what to do? A thread 🧵
— Christoph Molnar (@ChristophMolnar) August 30, 2022

LSE Digital Skills Lab

LSE Digital Skills Lab offers R and python workshops during Term time and they will also give DSI students access to self-paced programming courses via Dataquest.

Follow the links below to take the pre-sessional self-paced courses:

R for Data Science Pre-sessional Course 22/23

Also, keep an eye on the following pages for news of the in-person workshops:

R workshops

Other resources

Checkout this summer’s LSE Careers Skill Accelerator programme. Some of the self-paced courses will remain open to LSE students until the end of the year.

Ready to develop the key skills employers are looking for in 2022?

Join this summer's LSE Careers Skills Accelerator programme to expand your skillset!

⭐Apply on CareerHub by 11.59pm, Wed 15 June for your chance to join the programme⭐https://t.co/J5sI1NRaMA
— LSE Careers (@LSECareers) June 9, 2022

The book R for Data Science is free to read online and is a great resource to advance your R skills.

References

Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and Other Stories. 1st ed. Cambridge University Press. https://doi.org/10.1017/9781139161879.

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/.

Molnar, Christoph. 2022. Modeling Mindsets. 1st ed. Leanpub. https://book.modeling-mindsets.com/.

Warne, Russell T. 2018. Statistics for the Social Sciences: A General Linear Model Approach. https://www.cambridge.org/highereducation/books/statistics-for-the-social-sciences/716FF25785A6154CC6822D067A959445.