LSE DS101L - Fundamentals of Data Science

2022/23 Lent Term

Author

Published

16 January 2023

Intro
	🗓️ Week 01	Lecture	Introduction, Context & Key Concepts
		Class	No class this week. (But there is a take-home formative assignment plus indicative readings)
		Formative	What: Personal data When: Throughout the week Deadline: 23 January 2023
		Readings	Indicative 📃 Journal Column: (Prince 2014) 📄 Pre-print: (Sachs, Wise, and Karell 2021) Recommended 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 1) - What is Data Science? 📕 Book Chapter: (Shah 2020, chap. 1) - Introduction 🕸️ Website: (“What Is Quantified Self?” n.d.) 🎙️ Podcast: (Gimlet n.d.) 📰 Magazine Article: (Schroeder 2022) 📜 Academic Paper: (Swan 2013) 📜 Academic Paper: (Lupton 2016) 📜 Academic Paper: (Lupton 2020) Go deeper 📘 Book: (Zuboff 2019) 💽 See all the data Google has on you 💽 See how to get your Instagram data
Basic concepts from Computer Science and Statistics
	🗓️ Week 02	Lecture	Data types and the concept of tidy data
		Class	Discussions: the boundaries of personal data
		Formative	What: Create a tidy spreadsheet When: Throughout the week Deadline: 30 January 2023
		Readings	Indicative 📕 Book Chapter: (Shah 2020, chap. 2) 📜 Academic Paper: (Wickham 2014) 📜 Academic Paper: (Broman and Woo 2018) 📰 Feature Article: (Perkel 2022) Recommended 📕 Book Chapter: (Wickham and Grolemund 2016, chap. 1) 🕸️ Website: Data types in R
	🗓️ Week 03	Lecture	Computational Thinking and Programming
		Class	Live Demo: How data scientists use programming to clean data
		Formative	What: Create some charts! When: Throughout the week Deadline: 6 February 2023
		Readings	Indicative 📕 Book Chapter: (Denning and Tedre 2019, chap. 1) Recommended 📕 Book Chapters: (Grolemund 2014, chaps. 1–5) 📕 Book Chapters: (Wickham and Grolemund 2016, chaps. 1–3) Go deeper 🏫 Online Course: Join the PS-R4DS 22/23 Moodle page and read the instructions carefully to gain a premium license to a Dataquest course about R.
	🗓️ Week 04	Lecture	Statistical Inference I
		Class	Live Demo: How data scientists use programming for data visualisation
		Summative 🌟	Worth: 10% of final marks Prepare for your group presentation next week Release date: 6 February 2023 Deadline: 13 February 2023
		Readings	Indicative 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 2) 📕 Book Chapter: (Wickham and Grolemund 2016, chap. 7) Recommended 📕 Book Chapters: (Grolemund 2014, chaps. 1–5) 📕 Book Chapters: (Bruce and Bruce 2017) 📕 Book Chapters: (Wickham and Grolemund 2016, chaps. 1–3) Go deeper 📙 Book: “Queer Data” (Guyan 2022) 📘 Book: “Data Feminism” (D’Ignazio and Klein 2020)
	🗓️ Week 05	Lecture	Statistical Inference II
		Class 🌟	Group Presentations (worth 10% of final grade)
		Formative	What: Answer questions about the indicative readings When: Throughout Weeks 05 & 06 Deadline: 27 February 2023
		Readings	Indicative 📰 Feature article: (Aschwanden 2015) Recommended 📕 Book Chapters: (Ismay and Kim 2020, chaps. 5, 6, 9) 📕 Book Chapters: (Shah 2020, chaps. 2–3) Go deeper 📕 Book Chapter: (Warne 2021, chap. 7) 📕 Book Chapter: “Data Feminism” (D’Ignazio and Klein 2020)
	🗓️ Week 06	Reading Week
Machine Learning & AI
	🗓️ Week 07	Lecture	Machine Learning I: Supervised Learning
		Class	Live Demo: Supervised Learning
		Formative	What: Start gathering academic papers in Zotero When: Throughout the week Deadline: 6 February 2023
		Readings	Indicative 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 3) Recommended 📕 Book Chapter: (James et al. 2021, chap. 2) 📕 Book Chapters: (Flach 2012, chaps. 1, 10, 12) ✍️ Keep on practicing Markdown writing 🕸️ Start saving articles you read on Zotero. Take a look at Zotero Quick Start Go deeper 📕 Book Chapters: (Flach 2012, chaps. 2, 3, 5, 7) (no need to fully understand the equations; an intuition about them is enough.)
	🗓️ Week 08	Lecture	Machine Learning II: Unsupervised Learning
		Class	Tutorial: Introduction to Zotero & (& Quarto Markdown)
		Formative ⭐	What: Start writing your first (formative) essay using Quarto markdown Release date: 6 March 2023 Deadline: 16 March 2023
		Readings	Indicative 📕 Book Chapters: (Flach 2012, chap. 8, epilogue) 🕸️ Online resource: (scikit-learn 2023) Recommended ✍️ Tutorial: Article Layout with Quarto ✍️ Tutorial: Advanced article formatting
	🗓️ Week 09	Lecture	Unstructured Data (Text, Audio, Video)
		Class	In-class activity: exploring Machine Learning metrics (with a case study)
		Readings	Indicative 📕 Book Chapters: (Flach 2012, chap. 12) Recommended 📘 Book Chapter: (Silge and Robinson 2017, chap. 1) 📦 R package: (Benoit et al. 2018) & (Benoit 2022) Go deeper 📘 Full Book: (Silge and Robinson 2017)
Decisions and Implications
	🗓️ Week 10	Lecture	Prediction vs. Explanation
		Class	Live Demo: Unsupervised Learning
		Summative 🌟	What: Start writing your first (summative) essay using Quarto markdown Worth: 30% of your final grade Release date: 20 March 2023 Deadline: 4 April 2023
		Readings	Indicative 📜 Academic Paper: (Hofman, Sharma, and Watts 2017) 📜 Academic Paper: (Hofman et al. 2021) 📜 Academic Paper: (Hullman et al. 2022) 📜 Academic Paper: (Verhagen 2022) 📜 Academic Paper: (Lazer et al. 2014) 📜 Academic Paper: (Rettberg 2022) Go deeper 📘 Book: (Pietsch 2022)
	🗓️ Week 11	Lecture	Privacy and ethical concerns of the current wave of Generative AI (e.g., ChatGPT)
		Class	Exploring Generative AI
		Deadline Approaching ⏲️	Keep working on your essays: Attend drop-in sessions Organise study groups
		Readings	📰 Newspaper Opinion Piece: (Bridle 2023)
After the Term
	🗓️ Week 11+1	Deadline ⌛	Submit your essay by 4 April 2023
	April/2023	Spring Break
Summer Term (May & Jun 2023)
	🗓️ Week 02	Summative 🌟	What: Start writing your second (summative) essay using Quarto markdown Worth: 60% of your final grade Release date: 9 May 2023 Deadline: 25 May 2023
			🗓️ Week 03	Deadline Approaching ⏲️	Keep working on your essays: Attend drop-in sessions Organise study groups
					🗓️ Week 11+1	Deadline ⌛	Submit your essay by 25 May 2023
				The End

References

Aschwanden, Christie. 2015. “Science Isn’t Broken.” FiveThirtyEight. https://fivethirtyeight.com/features/science-isnt-broken/.

Benoit, Kenneth. 2022. “Quantitative Analysis of Textual Data.” https://quanteda.io/index.html.

Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. 2018. “Quanteda: An R Package for the Quantitative Analysis of Textual Data.” Journal of Open Source Software 3 (30): 774. https://doi.org/10.21105/joss.00774.

Bridle, James. 2023. “The Stupidity of AI.” The Guardian, March. https://www.theguardian.com/technology/2023/mar/16/the-stupidity-of-ai-artificial-intelligence-dall-e-chatgpt.

Broman, Karl W., and Kara H. Woo. 2018. “Data Organization in Spreadsheets.” The American Statistician 72 (1): 2–10. https://doi.org/10.1080/00031305.2017.1375989.

Bruce, Peter C., and Andrew Bruce. 2017. Practical Statistics for Data Scientists: 50 Essential Concepts. First edition. Sebastopol, CA: O’Reilly. https://ebookcentral.proquest.com/lib/londonschoolecons/detail.action?docID=4857224.

D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. Strong Ideas Series. Cambridge, Massachusetts: The MIT Press. https://ebookcentral.proquest.com/lib/londonschoolecons/reader.action?docID=6120950.

Denning, Peter J., and Matti Tedre. 2019. Computational Thinking. The MIT Press Essential Knowledge Series. Cambridge, Massachusetts: The MIT Press.

Flach, Peter A. 2012. Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge: Cambridge University Press. https://doi-org.gate3.library.lse.ac.uk/10.1017/CBO9780511973000.

Gimlet. n.d. “#177 Gleeks and Gurgles Reply All.” Accessed January 15, 2023. https://gimletmedia.com:443/shows/reply-all/z3h78d6.

Grolemund, Garrett. 2014. Hands-on Programming with R. First edition. Sebastopol, CA: O’Reilly. https://rstudio-education.github.io/hopr/index.html.

Guyan, Kevin. 2022. Queer Data: Using Gender, Sex and Sexuality Data for Action. Bloomsbury Studies in Digital Cultures. London: Bloomsbury Academic. https://web-s-ebscohost-com.gate3.library.lse.ac.uk/ehost/detail/detail?nobk=y&vid=2&sid=a8efeedd-6bfc-459a-9f0c-a67dabcc75d1@redis&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ==#AN=3077276&db=nlebk.

Hofman, Jake M., Amit Sharma, and Duncan J. Watts. 2017. “Prediction and Explanation in Social Systems.” Science 355 (6324): 486–88. https://doi.org/10.1126/science.aal3856.

Hofman, Jake M., Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, et al. 2021. “Integrating Explanation and Prediction in Computational Social Science.” Nature 595 (7866): 181–88. https://doi.org/10.1038/s41586-021-03659-0.

Hullman, Jessica, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, and Arvind Narayanan. 2022. “The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning.” In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 335–48. Oxford United Kingdom: ACM. https://doi.org/10.1145/3514094.3534196.

Ismay, Chester, and Albert Young-Sun Kim. 2020. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. Chapman & Hall/CRC the R Series. Boca Raton: CRC Press / Taylor & Francis Group. https://moderndive.com/.

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/.

Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343 (6176): 1203–5. https://doi.org/10.1126/science.1248506.

Lupton, Deborah. 2016. “The Diverse Domains of Quantified Selves: Self-Tracking Modes and Dataveillance.” Economy and Society 45 (1): 101–22. https://doi.org/10.1080/03085147.2016.1143726.

———. 2020. “Data Mattering and Self-Tracking: What Can Personal Data Do?” Continuum 34 (1): 1–13. https://doi.org/10.1080/10304312.2019.1691149.

Perkel, Jeffrey M. 2022. “Six Tips for Better Spreadsheets.” Nature 608 (7921): 229–30. https://doi.org/10.1038/d41586-022-02076-1.

Pietsch, Wolfgang. 2022. On the Epistemology of Data Science: Conceptual Tools for a New Inductivism. Philosophical Studies Series, Volume 148. Cham: Springer.

Prince, J. Dale. 2014. “The Quantified Self: Operationalizing the Quotidien.” Journal of Electronic Resources in Medical Libraries 11 (2): 91–99. https://doi.org/10.1080/15424065.2014.909145.

Rettberg, Jill Walker. 2022. “Algorithmic Failure as a Humanities Methodology: Machine Learning’s Mispredictions Identify Rich Cases for Qualitative Analysis.” Big Data & Society 9 (2): 205395172211312. https://doi.org/10.1177/20539517221131290.

Sachs, Jeffrey, Rahshemah Wise, and Daniel Karell. 2021. “The TikTok Self: Music, Signaling, and Identity on Social Media.” Preprint. SocArXiv. https://doi.org/10.31235/osf.io/2rx46.

Schroeder, Stan. 2022. “TikTok’s in-App Browser Can Monitor Your Every Click and Keystroke.” Mashable. https://mashable.com/article/tiktok-browser-monitoring.

Schutt, Rachel, and Cathy O’Neil. 2013. Doing Data Science. First edition. Beijing ; Sebastopol: O’Reilly Media. https://ebookcentral.proquest.com/lib/londonschoolecons/detail.action?docID=1465965.

scikit-learn. 2023. “Scikit Learn User Guide: Clustering.” Scikit-Learn. https://scikit-learn/stable/modules/clustering.html.

Shah, Chirag. 2020. A Hands-on Introduction to Data Science. Cambridge, United Kingdom ; New York, NY, USA: Cambridge University Press. https://librarysearch.lse.ac.uk/permalink/f/1n2k4al/TN_cdi_askewsholts_vlebooks_9781108673907.

Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. First edition. Beijing ; Boston: O’Reilly. https://www.tidytextmining.com/index.html.

Swan, Melanie. 2013. “The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.” Big Data 1 (2): 85–99. https://doi.org/10.1089/big.2012.0002.

Verhagen, Mark D. 2022. “A Pragmatist’s Guide to Using Prediction in the Social Sciences.” Socius: Sociological Research for a Dynamic World 8 (January): 237802312210817. https://doi.org/10.1177/23780231221081702.

Warne, Russell T. 2021. Statistics for the Social Sciences: A General Linear Model Approach. Second edition. Cambridge, United Kingdom New York, NY Port Melbourne, Australia New Delhi, India Singapore: Cambridge University Press. https://doi.org/10.1017/9781108894319.

“What Is Quantified Self?” n.d. Quantified Self. Accessed January 15, 2023. https://quantifiedself.com/about/what-is-quantified-self/.

Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software 59 (10). https://doi.org/10.18637/jss.v059.i10.

Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. First edition. Sebastopol, CA: O’Reilly. https://r4ds.had.co.nz/.

Zuboff, Shoshana. 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. First edition. New York: PublicAffairs. https://www.publicaffairsbooks.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/.