🗓️ Week 01
Structure of this course

DS101 – Fundamentals of Data Science

Dr. Jon Cardoso-Silva

LSE Data Science Institute

16 Jan 2023

What we will cover today:

• Who are we
• How did we get here?
• What do we mean by data science?
• The
  Data
  Science
  Workflow
• (Unsolved) questions about the generative AI revolution

Who are we

The Data Science Institute

• This course is offered by the LSE Data Science Institute (DSI).
• DSI is the hub for LSE’s interdisciplinary collaboration in data science

The Data Science Institute

Activities of interest to you:

• CIVICA Seminar Series
• Careers in Data Science
• Social events
• Industry “field trips”
• Summer projects

Our courses

DSI offer accessible introductions to Data Science:

DS101

Fundamentals of
Data Science

🎯 Focus:
theoretical concepts of data science

📂 How:
reflections through reading and writing

DS105

Data for
Data Scientists

🎯 Focus:
collection and handling of real data

📂 How:
hands-on coding exercises and a group project

DS202

Data Science for
Social Scientists

🎯 Focus:
fundamental machine learning algorithms

📂 How:
practical use of ML techniques and metrics

Your lecturer

Dr. Jon Cardoso-Silva
Assist. Prof. Lecturer
LSE Data Science Institute

• PhD in Computer Science (King’s College London)
• Background: Engineering, Bio & Health Informatics
• Former Lead Data Scientist

networks
optimisation
machine learning applications
data science workflow

Teaching Assistants

Dr. Stuart Bramwell
ESRC Postdoctoral Fellow
Department of Methodology

• PhD in Politics (Oxford)
• Background: Political Science
• Founder of WhoGov data set
  • won the Lijphart/Przeworski/Verba data set award conferred annually by the American Political Science Association

social identity democratisation
political elites political economy

Who are you

Course	Year	Department
BSc in Philosophy, Politics and Economics	3	Philosophy
BSc in Sociology	2	Sociology
BSc in Economics	1	Economics
BSc in Philosophy and Economics	3	Philosophy
BSc in Philosophy, Logic and Scientific Method	3	Philosophy
BSc in Sociology	2	Sociology
BSc in Economics	1	Economics
General Course	1	General Course
BA in Geography	3	Geography
General Course	1	General Course
BSc in Economics and Economic History	3	Economics
BA in History	1	History
BSc in Politics and Economics	3	Government
BA in History	3	History

Department	n
Economics	3
Philosophy	3
General Course	2
History	2
Sociology	2
Geography	1
Government	1

How did we get here?

• and how did we get to the point that we can collect, extract and analyse all of this data?
• Well, this abundance of data is strongly associated with the dramatic changes in technology we have experienced recently.
• Take a look of the technology people were using back in 2005.
• This photo was taken outside St. Peter’s Basilica in the Vatican at the time Ratzinger was ordained as pope
• Now, fast forward to 2013 — only 8 years later — when Pope Francis was elected the new pope, and you will see a lot of bright screens.
• Our habits have changed

This abundance of data is strongly associated with the dramatic changes in technology in the past few decades.

St.Peter’s Basilica at the Vatican in
📅 19 April 2005
when Ratzinger
was elected the 265th pope.

St.Peter’s Basilica at the Vatican in
📅 13 March 2013
when Pope Francis
was elected the 266th pope.

We changed how we consume music 🎧

We changed how we consume video 🎞️

Smartphones 📱 are a very recent thing

We spend a lot more time connected

… and our social media habits keep on changing

💡 Move the slider to explore the chart

The possibilities

• Humans and machines nowadays generate A LOT of data ALL THE TIME

• It has become cheap to collect and store this data

• This abundance of data opens up new possibilities for research & policy-making

New data to answer old questions:

How do rumours spread?

New questions enabled by new data:

Is social media a threat to democracy?

What do we mean by data science?

Data science is…

“[…] a field of study and practice that involves the collection, storage, and processing of data in order to derive important 💡 insights into a problem or a phenomenon.

Such data may be generated by humans (surveys, logs, etc.) or machines (weather data, road vision, etc.),

and could be in different formats (text, audio, video, augmented or virtual reality, etc.).”

(Shah 2020, chap. 1) - Emphasis and emojis are of my own making.

The mythical unicorn 🦄

knows everything about statistics

able to communicate insights perfectly

fully understands businesses like no one

is a fluent computer programmer

Of course, such a person does not exist!

See (Davenport 2020) for a more in-depth discussion about this

In reality…

We are all jugglers 🤹

• Everyone brings a different skill set.
• We need multi-disciplinary teams.
• Good data scientists know a bit of everything.
  • Not fluent in all things
  • Understands their strenghts and weaknessess
  • They know when and where to interface with others

See (Schutt and O’Neil 2013, chap. 1) for more on this.

The
Data
Science
Workflow

The Data Science Workflow

⚠️ Note that this is a simplified version of what happens in a data science project.
In practice, the process is not linear, and many feedback loops exist.

The Data Science Workflow

It is often said that 80% of the time and effort spent on a data science project goes to the abovementioned tasks.

The Data Science Workflow

• This is the bit that typically gets everyone excited about data science.
• Machine Learning is a sub-field of Artificial Intelligence that focuses on developing algorithms that can learn from data.

What do you think of this image?

• This image won the digital art category of the Colorado State Fair’s annual art competition
• I promise this is about Data Science!

An algorithm created this image!

• Instead of a 🖌️ brush (physical or digital), the author typed a few words into an algorithm

• It took him ⌛ 80 hours to find the “right words” to express his idea

• Jason Allen used a generative Artificial Intelligence (AI) algorithm called ‘Midjourney’
  • ⏭️ Let’s look at a few other examples

What about ChatGPT?

Links:

• Weale (2023)
• Sample (2023)

(Unsolved) questions about the generative AI revolution

Who owns the rights to the images?

“a horde of octopuses protesting during the French revolution, photojournalism”

What biases do these tools propagate?

“a futuristic octopus-like alien holding a jar that contains the source of true happiness”

Are these tools safe?

“a futuristic octopus-like alien holding a jar that contains the source of true happiness”

Time for coffee ☕

After the break:

• DS101L website
• Syllabus
• Assessments
• Tools
• This week’s coursework

References

Davenport, Thomas. 2020. “Beyond Unicorns: Educating, Classifying, and Certifying Business Data Scientists.” Harvard Data Science Review 2 (2). https://doi.org/10.1162/99608f92.55546b4a.

Fischer-Baum, Reuben. 2017. “What ‘Tech World’ Did You Grow up In?” Washington Post. https://www.washingtonpost.com/graphics/2017/entertainment/tech-generations/.

Kolawole, Emi. 2013. “About Those 2005 and 2013 Photos of the Crowds in St. Peter’s Square.” Washington Post. http://wapo.st/WKKTMh.

Sample, Ian. 2023. “ChatGPT: What Can the Extraordinary Artificial Intelligence Chatbot Do?” The Guardian, January. https://www.theguardian.com/technology/2023/jan/13/chatgpt-explainer-what-can-artificial-intelligence-chatbot-do-ai.

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.

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.

Weale, Sally. 2023. “Lecturers Urged to Review Assessments in UK Amid Concerns over New AI Tool.” The Guardian, January. https://www.theguardian.com/technology/2023/jan/13/end-of-the-essay-uk-lecturers-assessments-chatgpt-concerns-ai.