LSE DS101A - Fundamentals of Data Science

2024/25 Autumn Term

Intro
	🗓️ Week 01 30 Sep 2024- 04 Oct 2024	👩🏻‍🏫 Lecture	Introduction, Context & Key Concepts
		💻 Class	Discussions: the boundaries of personal data
		🛟 Ways to get support	Click here to see how to get help this week We love hearing from you! Truly! Don’t hesitate to contact us for help. In this first week, the best ways to get help are: Slack: Post any question you might have about the course (class or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 02 October 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers. Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 01 October 2024 from 09.30-10.45am. 📧 E-mail: Not sure if this course is for you? Or you have a valid reason to request a change of class? For these and other administrative queries, write , our Teaching & Assessment Support Officer at the DSI.
		✍️ Coursework	What: Read the indicative reading articles and answer questions about them to prepare for the week 1 class discussion Release date: Week 1 Lecture i.e 30 September 2024 When: Throughout the week Deadline: 04 October 2024
		📖 Readings	Indicative 📜 Academic Paper: (Lupton 2020) 📜 Academic Paper: (Swan 2013) 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) 📰 Newspaper Opinion Piece: (Wong 2019) 📜 Academic Paper: (Lupton 2016) 📜 Journal article: (Behne and Teuteberg 2020) 📃 Journal Column: (Prince 2014) 📜 Academic Paper: (Russo 2015) 📜 Academic Paper: (Isaak and Hanna 2018) 📜 Academic Paper: (Cinnamon 2020) 📜 Academic Paper: (Bakir 2020) 📜 Academic Paper: (Elias and Gill 2018) 📜 Academic Paper: (Gerdenitsch et al. 2023) 📄 Pre-print: (Sachs, Wise, and Karell 2021) 📄 Survey report: (Wike et al. 2022) Go deeper 📘 Book: (Zuboff 2019) 📜 Academic Paper: (Michał Wieczorek and Gordijn 2023) 💽 See all the data Google has on you 💽 See how to get your Instagram data
Basic concepts from Computer Science and Statistics
	🗓️ Week 02 07 Oct 2024- 11 Oct 2024	👩🏻‍🏫 Lecture	Basic blocks of data: getting familiar with the most common data types and file formats
		💻 Class	Live Demo: How data scientists use programming to preprocess data
		🛟 Ways to get support	Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. If so, don’t hesitate to contact us for help! In this second week, the best ways to get help are: Slack: Post any question you might have about the course (class or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 09 October 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 08 October 2024 from 09.30-10.45am. 📧 E-mail: For any administrative queries, such as class change, write , our Teaching & Assessment Support Officer at the DSI.
		📖 Readings	Indicative 📕 Book Chapter: (Shah 2020, chap. 2) 📜 Academic Paper: (Wickham 2014) 📜 Academic Paper: (Broman and Woo 2018) 📰 Feature Article: (Perkel 2022) Go deeper 🕸️ Online resource: Basic types in Python (Sturz 2023) 🕸️ Online course: Basic types in Python (Jones 2023) 🕸️ Online resource: Floating Point Arithmetic in Python: Issues and Limitations (Python documentation 2023) 🕸️ Online course: Introduction to Python (Real Python 2023) 🕸️ Online resource: Integer overflow attacks (Lake 2023) 🕸️ Online resource: Integer overflows (Pliska 2018)
			🗓️ Week 03 14 Oct 2024- 18 Oct 2024	👩🏻‍🏫 Lecture	Computational Thinking and Programming
💻 Class	Live Demo: How data scientists use programming to visualise data
🛟 Ways to get support	Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also want some clarifications regarding the hot-off-the-press first summative topic (i.e group presentation topic). If so, don’t hesitate to contact us for help! In this third week, the best ways to get help are: Slack: Post any question you might have about the course (class, lecture or presentation) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 16 October 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 15 October 2024 from 10.30-11.45am. 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
🌟 Summative	Worth: 10% of final marks Prepare for your group presentation in two weeks Release date: 14 October 2024 Deadline: 1 November 2024
📚 Homework	Tutorial: Introduction to Zotero & Quarto Markdown
📖 Readings	Indicative 📕 Book Chapter: (Denning and Tedre 2019, chap. 1) Go deeper 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 3)
	🗓️ Week 04 21 Oct 2024- 25 Oct 2024	👩🏻‍🏫 Lecture	Statistical Inference
		💻 Class	OFQUAL algorithm case study
		🛟 Ways to get support	Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also have questions about your group presentation or your Zotero/Quarto homework. If so, don’t hesitate to contact us for help! In this fourth week, the best ways to get help are: Slack: Post any question you might have about the course (class, lecture or presentation) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 23 October 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Thursday, 24 October 2024 from 13.30-14.45. 🆘 Drop-in session: We will host a drop-in session in Week 04 to help answer any questions you may have about your upcoming first summative 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
		📖 Readings	Indicative 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 2) 📰 Feature article: (Aschwanden 2015) Recommended 📕 Book Chapters: (Bruce and Bruce 2017) 📕 Book Chapters: (Shafer and Zhang 2012) 📕 Book Chapters: (Shah 2020, chaps. 2–3) Go deeper 📙 Book: “Queer Data” (Guyan 2022) 📘 Book: “Data Feminism” (D’Ignazio and Klein 2020) 📕 Book Chapter: (Warne 2021, chap. 7)
	🗓️ Week 05 28 Oct 2024- 1 Nov 2024	👩🏻‍🏫 Lecture	Exploratory data analysis and visualisation
		💻 Class	First summative: Group Presentations (worth 10% of final grade)🌟
		🛟 Ways to get support	Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also have questions about your group presentation or your Zotero/Quarto. If so, don’t hesitate to contact us for help! In this fifth week, the best ways to get help are: Slack: Post any question you might have about the course (class, lecture or presentation) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 30 October 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 29 October 2024 from 10.30-11.45am. 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
		⭐ Formative	What: Answer questions about a set of articles and media Release date: 28 Oct 2024 When: Throughout Weeks 05 & 06 How: The document you submit will be an HTML generated with Quarto Deadline: 14 November 2024
		✍️ Coursework	What: Practice Zotero and Quarto Markdown When: Throughout Weeks 05 & 06 🕸️ Start saving articles you read on Zotero. Take a look at Zotero Quick Start You might need them in your case studies!
		📖 Readings	Indicative 🕸️ Online resource: (Ribecca 2024) 📕 Book Chapters: (Illowsky and Dean 2013) 📕 Book Chapters: (Enders 2022) 📜 Academic Paper: (Scheffer 2002) Recommended 📕 Book: (Knaflic 2015) 🕸️ Online resource: (Agency 2024)
	🗓️ Week 06 04 Nov 2024- 08 Nov 2024	Reading Week
Machine Learning & AI
	🗓️ Week 07 11 Nov 2024- 15 Nov 2024	👩🏻‍🏫 Lecture	Machine Learning I: Supervised Learning
		💻 Class	Case study: Supervised Learning
		🛟 Ways to get support	Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also have questions about your Zotero/Quarto. If so, don’t hesitate to contact us for help! In this seventh week, the best ways to get help are: Slack: Post any question you might have about the course (class or lecture) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 13 November 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 12 November 2024 from 10.30-11.45am. 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
		📖 Readings	Indicative 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 3) Recommended 📕 Book Chapters: (Flach 2012, chaps. 1, 10, 12) 📜 Academic Paper: (Choi et al. 2020) (no need to fully understand all the technical details; an intuition about them is enough.) ✍️ Keep on practicing Markdown writing 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 18 Nov 2024- 22 Nov 2024	👩🏻‍🏫 Lecture	Machine Learning II: Unsupervised Learning
				💻 Class	Case study: Unsupervised learning
⭐ Formative	What: Start writing your first (formative) case study using Quarto markdown Release date: 18 Nov 2024 Deadline: 5 Dec 2024
🛟 Ways to get support	Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also have questions about your hot-off-the-press formative case study or your Zotero/Quarto. If so, don’t hesitate to contact us for help! In this eighth week, the best ways to get help are: Slack: Post any question you might have about the course (class, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 20 November 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 19 November 2024 from 10.30-11.45am. 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
📖 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 25 Nov 2024- 29 Nov 2024	👩🏻‍🏫 Lecture	Unstructured Data (Text, Audio, Video)
💻 Class		Peer-reviewing activity (Details about the activity will be given on the week 8 Lecture)
🛟 Ways to get support		Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also have questions about your formative case study or your Zotero/Quarto. If so, don’t hesitate to contact us for help! In this nineth week, the best ways to get help are: Slack: Post any question you might have about the course (class, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 27 November 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 26 November 2024 from 10.30-11.45am. 🆘 Drop-in session: We will host a drop-in session in Week 09 to help answer any questions you have about your upcoming case study formative (due in week 10) 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
📖 Readings		Indicative 📕 Book Chapters: (Flach 2012, chap. 12)
Decisions and Implications
	🗓️ Week 10 02 Dec 2024- 06 Dec 2024	👩🏻‍🏫 Lecture	Prediction vs. Explanation
		💻 Class	Case study: Gathering insights as a data scientist
		Deadline ⌛	Submit your (formative) case study by 5 December 2024
		✍️ Coursework	What: Work in groups, find examples of data science/AI applications with ethical issues and answer questions about them Release date: 02 Dec 2024 Deadline: 09 Dec 2024
		🛟 Ways to get support	Click here to see how to get help this week As we continue our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also have questions about your formative case study (due this week!) or your Zotero/Quarto. If so, don’t hesitate to contact us for help! In this tenth week, the best ways to get help are: Slack: Post any question you might have about the course (class, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 04 December 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 03 December 2024 from 10.30-11.45am. 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
		📖 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 25 Mar 2024- 29 Mar 2024	👩🏻‍🏫 Lecture	Ethical issues of AI and ethical AI: an overview
		💻 Class	Exploring Generative AI
		🌟 Summative	What: Start working on your final summative case study using Quarto markdown Worth: 80% of your final grade Release date: 09 Dec 2024 Deadline: 23 Jan 2024
		🛟 Ways to get support	Click here to see how to get help this week As we end our exploration of the data science world, some things might feel rather unfamiliar or confusing. You might also have questions about your upcoming summative case study (released this week) or your Zotero/Quarto. If so, don’t hesitate to contact us for help! In this last week of term (time flies!), the best ways to get help are: Slack: Post any question you might have about the course (class, lecture or assignment) in the #help channel. Ghita (as well as your class teachers) will be checking for messages every now and then throughout the week. 💬 Office Hours: If you want 1 to 1 in-person support or you want to discuss anything about the course, go to StudentHub and book a 15-minute slot with Ghita on Wednesday, 11 December 2024 from 12.30-2.30 pm. Also, check for availability of office hours of some of your class teachers (you’ll find the timings of your class teachers’ office hours on the 📟 Communication page). Sara will also be running a support session at the Visualisation studio (COL 1.06) on Tuesday, 10 December 2024 from 10.30-11.45am. 📧 E-mail: For any administrative queries, such as extension requests, write , our Teaching & Assessment Support Officer at the DSI.
		📖 Readings	Indicative 📰 Newspaper Opinion Piece: (Bridle 2023) 🕸️ Online article: (Parsons 2020) 🕸️ Online article: (Bossman 2016) Recommended 📜 Academic Paper: (Floridi et al. 2018) 📜 Academic Paper: (Bender et al. 2021) 📜 Academic Paper: (Pessach and Shmueli 2022) 📜 Academic Paper: (Verma and Rubin 2018) 📜 Academic Paper: (Mehrabi et al. 2021) 📄 Pre-print: (Greshake et al. 2023) 📄 Pre-print: (Perry et al. 2022) 🕸️ News article: (Viswanathan 2023) 🕸️ Blog post: (Segura 2023) Go deeper 📜 Academic Paper: (Li et al. 2023) 📜 Academic Paper: (Nauta et al. 2023) 📜 Academic Paper: (Cheng, Varshney, and Liu 2021) 📜 Academic Paper: (Rastogi et al. 2022) 📜 Academic Paper: (Podoletz 2022) 📜 Academic Paper: (Ribeiro, Singh, and Guestrin 2016) 📜 Academic Paper: (Gramegna and Giudici 2021) 📜 Academic Paper: (Sweeney 2013)
After the Term
		Deadline Approaching ⏲️	Keep working on your essays: Attend drop-in sessions Organise study groups
Winter Term
	🗓️ Week 01	Deadline Approaching ⏲️	Keep working on your essays: Attend drop-in sessions Organise study groups
		Deadline ⌛	Submit your case study by 23 January 2024
The End

References

Agency, United States Environmental Protection. 2024. “Exploratory Data Analysis.” https://www.epa.gov/caddis/exploratory-data-analysis.

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

Bakir, Vian. 2020. “Psychological Operations in Digital Political Campaigns: Assessing Cambridge Analytica’s Psychographic Profiling and Targeting.” Frontiers in Communication 5 (September): 67. https://doi.org/10.3389/fcomm.2020.00067.

Behne, Alina, and Frank Teuteberg. 2020. “A Healthy Lifestyle and the Adverse Impact of Its Digitalization: The Dark Side of Using eHealth Technologies.” In Band-1. https://doi.org/https://doi.org/10.30844/wi_2020_f2-behne.

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.” In, 610–23. Virtual Event Canada: ACM. https://doi.org/10.1145/3442188.3445922.

Bossman, Julia. 2016. “Top 9 Ethical Issues in Artificial Intelligence. World Economic Forum.” October 21, 2016. https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/.

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.

Cheng, Lu, Kush R. Varshney, and Huan Liu. 2021. “Socially Responsible AI Algorithms: Issues, Purposes, and Challenges.” J. Artif. Int. Res. 71 (September): 1137–81. https://doi.org/10.1613/jair.1.12814.

Choi, Rene Y., Aaron S. Coyner, Jayashree Kalpathy-Cramer, Michael F. Chiang, and J. Peter Campbell. 2020. “Introduction to Machine Learning, Neural Networks, and Deep Learning.” Translational Vision Science & Technology 9 (2): 14–14. https://doi.org/10.1167/tvst.9.2.14.

Cinnamon, Jonathan. 2020. “Data Inequalities and Why They Matter for Development.” Information Technology for Development 26 (2): 214–33. https://doi.org/10.1080/02681102.2019.1650244.

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.

Elias, Ana Sofia, and Rosalind Gill. 2018. “Beauty Surveillance: The Digital Self-Monitoring Cultures of Neoliberalism.” European Journal of Cultural Studies 21 (1): 59–77. https://doi.org/10.1177/1367549417705604.

Enders, Craig K. 2022. Applied Missing Data Analysis. Guilford Publications.

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.

Floridi, Luciano, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, et al. 2018. “AI4People—an Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations.” Minds and Machines (Dordrecht) 28 (4): 689–707.

Gerdenitsch, Cornelia, Till Bieg, Myriam Gaitsch, Philip Schörpf, Manfred Tscheligi, and Simone Kriglstein. 2023. “Tracking to Success? A Critical Reflection on Workplace Quantified-Self Technologies from a Humanistic Perspective.” In Proceedings of the 2nd Annual Meeting of the Symposium on Human-Computer Interaction for Work, 1–7.

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

Gramegna, Alex, and Paolo Giudici. 2021. “SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk.” Frontiers in Artificial Intelligence 4. https://doi.org/10.3389/frai.2021.752558.

Greshake, Kai, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023. “Not What You’ve Signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection.” https://arxiv.org/abs/2302.12173.

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.

Illowsky, Barbara, and Susan L. Dean. 2013. Introductory Statistics. Houston, Texas: OpenStax College. https://openstax.org/details/books/introductory-statistics.

Isaak, Jim, and Mina J. Hanna. 2018. “User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection.” Computer 51 (8): 56–59. https://doi.org/10.1109/MC.2018.3191268.

Jones, Darren. 2023. “Basic Data Types in Python.” Real Python. https://realpython.com/courses/python-data-types/.

Knaflic, Cole Nussbaumer. 2015. Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, New Jersey: Wiley.

Lake, Josh. 2023. “What Is an Integer Overflow Attack? Understanding the Threat and Examples.” Comparitech Information Security Blog. https://www.comparitech.com/blog/information-security/integer-overflow-attack/.

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.

Li, Bo, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, and Bowen Zhou. 2023. “Trustworthy AI: From Principles to Practices.” ACM Comput. Surv. 55 (9). https://doi.org/10.1145/3555803.

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.

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