LSE DS101W - Fundamentals of Data Science

2023/24 Winter Term

Intro
	🗓️ Week 01 15 Jan 2024- 19 Jan 2024	🧑‍🏫 Lecture	Introduction, Context & Key Concepts
		💻 Class	Discussions: the boundaries of personal data
		✍️ 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 15 January 2024 When: Throughout the week Deadline: 19 January 2023
		📖 Readings	Indicative 📃 Journal Column: (Prince 2014) 📜 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) 📜 Academic Paper: (Lupton 2020) 📜 Academic Paper: (Isaak and Hanna 2018) 📜 Academic Paper: (Bakir 2020) 📄 Pre-print: (Sachs, Wise, and Karell 2021) 📄 Survey report: (Wike et al. 2022) 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 22 Jan 2024- 26 Jan 2024	🧑‍🏫 Lecture	Data types and the concept of tidy data
		💻 Class	Live Demo: How data scientists use programming to preprocess data
		⭐ Formative	What: Create a tidy spreadsheet from Wikipedia data Release date: Week 2 Lecture i.e 22 January 2024 When: Throughout the week Deadline: 25 January 2024
		📖 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)
	🗓️ Week 03 29 Jan 2024- 2 Feb 2024	🧑‍🏫 Lecture	Computational Thinking and Programming
		💻 Class	Live Demo: How data scientists use programming to visualise data
		🌟 Summative	Worth: 10% of final marks Prepare for your group presentation in two weeks Release date: 29 January 2024 Deadline: 16 Feb 2024
		📖 Readings	Indicative 📕 Book Chapter: (Denning and Tedre 2019, chap. 1) Go deeper 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 3)
	🗓️ Week 04 5 Feb 2024- 9 Feb 2024	🧑‍🏫 Lecture	Statistical Inference I
		💻 Class 🌟	Tutorial: Introduction to Zotero & Quarto Markdown
		📖 Readings	Indicative 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 2) Recommended 📕 Book Chapters: (Bruce and Bruce 2017) 📕 Book Chapters: (Shafer and Zhang 2012) 📕 Book Chapters: (Illowsky and Dean 2013) 📕 Book Chapters: (Enders 2022) 📜 Academic Paper: (Scheffer 2002) Go deeper 📙 Book: “Queer Data” (Guyan 2022) 📘 Book: “Data Feminism” (D’Ignazio and Klein 2020)
			🗓️ Week 05 12 Feb 2024- 16 Feb 2024	🧑‍🏫 Lecture	Statistical Inference II
💻 Class	Group Presentations (worth 10% of final grade)
⭐ Formative	What: Answer questions about the indicative readings Release date: 12 Feb 2024 When: Throughout Weeks 05 & 06 Deadline: 01 March 2024
✍️ Coursework	What: Practice Zotero and Quarto Markdown When: Throughout Weeks 05 & 06 Deadline: 26 Feb 2024
📖 Readings	Indicative 📰 Feature article: (Aschwanden 2015) Recommended 📕 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 19 Feb 2024- 23 Feb 2024	Reading Week
Machine Learning & AI
	🗓️ Week 07 26 Feb 2024- 01 Mar 2024	🧑‍🏫 Lecture	Machine Learning I: Supervised Learning
		💻 Class	Live Demo: Supervised Learning
		🆘 Drop-in session	We will host a drop-in session on Week 07 to help answer any questions you have about Quarto Markdown and Zotero
		⭐ Formative	What: Start gathering academic papers in Zotero When: Throughout the week Deadline: 01 Mar 2024
		📖 Readings	Indicative 📕 Book Chapter: (Schutt and O’Neil 2013, chap. 3) Recommended 📕 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 04 Mar 2024- 08 Mar 2024	🧑‍🏫 Lecture	Machine Learning II: Unsupervised Learning
		💻 Class	Peer-reviewing activity (Details about the activity will be given on the week 7 Lecture)
		⭐ Formative	What: Start writing your first (formative) essay using Quarto markdown Release date: 01 Mar 2024 Deadline: 15 Mar 2024
		📖 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 11 Mar 2024- 15 Mar 2024	🧑‍🏫 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)
Decisions and Implications
	🗓️ Week 10 18 Mar 2024- 23 Mar 2024	🧑‍🏫 Lecture	Prediction vs. Explanation
		🌟 Summative	What: Start writing your first (summative) essay using Quarto markdown Worth: 30% of your final grade Release date: 20 Mar 2024 Deadline: 16 April 2024
		💻 Class	Live Demo: Unsupervised Learning
		🆘 Drop-in session	We will host a drop-in session on Week 11 to help answer any questions you have about your summative essay
		✍️ Coursework	What: Work in groups, find examples of data science/AI applications with ethical issues and answer questions about them Release date: 18 March 2024 Deadline: 25 March 2024
		📖 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
		Deadline Approaching ⏲️	Keep working on your essays: Attend drop-in sessions Organise study groups
		📖 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
	🗓️ Week 11+1	Deadline ⌛	Submit your essay by 16 April 2024
		🌟 Summative	What: Start writing your second (summative) essay using Quarto markdown Worth: 60% of your final grade Release date: 16 April 2024 Deadline: 15 May 2024
Summer Term
	🗓️ Week 01	Deadline Approaching ⏲️	Keep working on your essays: Attend drop-in sessions Organise study groups
			🗓️ Week 2	Deadline ⌛	Submit your essay by 15 May 2024
		The End

References

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.

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.

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.

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.

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/.

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.

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

Mehrabi, Ninareh, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. “A Survey on Bias and Fairness in Machine Learning.” ACM Comput. Surv. 54 (6). https://doi.org/10.1145/3457607.

Nauta, Meike, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. 2023. “From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI.” ACM Comput. Surv. 55 (13s). https://doi.org/10.1145/3583558.

Parsons, Lian. 2020. “Ethical Concerns Mount as AI Takes Bigger Decision-Making Role. Harvard Gazette.” October 26, 2020. https://news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai-takes-bigger-decision-making-role/.

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

Perry, Neil, Megha Srivastava, Deepak Kumar, and Dan Boneh. 2022. “Do Users Write More Insecure Code with AI Assistants?” https://arxiv.org/abs/2211.03622.

Pessach, Dana, and Erez Shmueli. 2022. “A Review on Fairness in Machine Learning.” ACM Comput. Surv. 55 (3). https://doi.org/10.1145/3494672.

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

Podoletz, Lena. 2022. “We Have to Talk about Emotional AI and Crime.” AI & SOCIETY, May. https://doi.org/10.1007/s00146-022-01435-w.

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.

Python documentation. 2023. “Floating Point Arithmetic: Issues and Limitations.” Python Documentation. https://docs.python.org/3.10/tutorial/floatingpoint.html.

Rastogi, Charvi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, and Richard Tomsett. 2022. “Deciding Fast and Slow: The Role of Cognitive Biases in AI-Assisted Decision-Making.” Proc. ACM Hum.-Comput. Interact. 6 (CSCW1). https://doi.org/10.1145/3512930.

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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.

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. “"Why Should i Trust You?": Explaining the Predictions of Any Classifier.” https://arxiv.org/abs/1602.04938.

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.

Scheffer, Judi. 2002. “Dealing with Missing Data.”

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.

Segura, Thomas L. 2023. “Yes, GitHub’s Copilot Can Leak (Real) Secrets. GitGuardian Blog - Automated Secrets Detection.” October 12, 2023. https://blog.gitguardian.com/yes-github-copilot-can-leak-secrets/.

Shafer, Douglas S., and Zhiyi Zhang. 2012. Introductory Statistics. Saylor Foundation. https://saylordotorg.github.io/text_introductory-statistics/.

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.

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

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.

Sweeney, Latanya. 2013. “Discrimination in Online Ad Delivery: Google Ads, Black Names and White Names, Racial Discrimination, and Click Advertising.” Queue 11 (3): 10–29. https://doi.org/10.1145/2460276.2460278.

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.

Verma, Sahil, and Julia Rubin. 2018. “Fairness Definitions Explained.” In Proceedings of the International Workshop on Software Fairness, 1–7. FairWare ’18. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3194770.3194776.

Viswanathan, Giri. 2023. “ChatGPT Struggles to Answer Medical Questions, New Research Finds. CNN.” December 10, 2023. https://www.cnn.com/2023/12/10/health/chatgpt-medical-questions/index.html.

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.

Wike, Richard, Laura Silver, Janell Fetterolf, Christine Huang, Sarah Austin, Laura Clancy, and Sneha Gubbala. 2022. “Social Media Seen as Mostly Good for Democracy Across Many Nations, but US Is a Major Outlier.” Pew Research Center’s Global Attitudes Project. Retrieved January 27: 2023.

Wong, Julia Carrie. 2019. “The Cambridge Analytica Scandal Changed the World – but It Didn’t Change Facebook.” The Guardian, March. https://www.theguardian.com/technology/2019/mar/17/the-cambridge-analytica-scandal-changed-the-world-but-it-didnt-change-facebook.

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/.