🗓️ Week 01
Welcome to the course
LSE DS202 – Data Science for Social Scientists
29 Sep 2023
Your lecturers (W01 - W05)
Assistant Professor of Data Science (Education)
LSE Data Science Institute
lecturer
course convenor
- PhD in Computer Science (King’s College London)
- Background: Engineering, Bio & Health Informatics
- Former Lead Data Scientist
networks
optimisation
software engineering
data science workflow
machine learning applications
Your lecturers (W07 - W11)
Assist. Prof. (Education)
LSE Data Science Institute
lecturer
- PhD in Computer Science (University of Twente, Netherlands)
- Background: Engineering, Databases, Health Informatics, ML for cybersecurity
- Formerly Research Associate at King’s College London and the University of Edinburgh (School of Informatics)
decision support systems
machine learning applications
databases
provenance
ethical AI/XAI
Teaching Assistants
Data Scientist
The Economist
MSc in Applied Social Data Science (LSE)
guest teacher
Data Scientist
Microsoft
MSc in Data Science (LSE)
guest teacher
Postdoctoral researcher
New News Project
Royal Holloway
guest teacher
PhD Candidate
SpaceTimeLab
University College London (UCL)
support
Administrative Support
Teaching and Learning Administrator
LSE Data Science Institute
Write an e-mail to Kevin:
- if you cannot find the lecture recording on Moodle
- when you need an extension for an assignment
(👉 check LSE’s extension policy) - to request a class group change
(you will be asked to provide a reason for this) - to inform us of any other issues that may affect your studies
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
- ⏭️ Let’s see a few activities that might be of interest to you
CIVICA Seminar Series
Industry “field trips”
Summer internships
Who are you? (cont.)
![]()
Course Brief
Course BriefWhat is this course about?
Focus: learn and understand the most fundamental machine learning algorithms
How: practical use of machine learning techniques and its metrics, applied to relevant data sets
![]()
Course Brief
What is this course about?
- Focus: learn and understand the most fundamental machine learning algorithms
- No neural networks, no deep learning, no large-scale data- How: practical use of machine learning techniques and its metrics, applied to relevant data sets
- Some but not a lot of theory, math proofs and derivations
- Lots of coding, examples and exercisesMore about 🧑🏻💻 Labs (90 min each week)
Each week, you will have a roadmap of what to do.
The roadmap will typically contain the following elements:
| Type of activity | Description |
|---|---|
| 🧑🏻🏫 TEACHING MOMENT | Your class teacher deserves your full attention |
| 🎯 ACTION POINTS | Time to follow the steps in the roadmap. Try it for a bit, but if you get stuck, call your class teacher. |
| 👥 IN PAIRS/GROUPS | You will benefit from completing that task with your peers more than doing it alone |
| 🗣️ CLASSROOM DISCUSSION | Your class teacher will facilitate a discussion about the task |
| 📝 SUBMISSION | Submit your work |
👉 Now, let’s navigate our 
Moodle page to see the 📓 Syllabus and to talk about ✍️ Assessments & Feedback.
Programming
- Programming Language:
- Integrated Development Environment (IDE) options:
- You choose:
- RStudio is the most popular IDE for R
- VS Code is a more general IDE, good for many programming languages. It is more lightweight than RStudio, but it requires more configuration.
Software and Tools
Teaching Philosophy
- My teaching approach is grounded in empiricism.
- I see learning as a transformative process, something that conduces to change, which is best facilitated by active, experience-focused, and exploration-driven activities.1
- In summary: learning by doing serves as the cornerstone of this course.
What does that mean in practice?
Image created with DALL·E via Bing Chat AI bot. Prompt: “An illustration of a person trying to solve a puzzle with pieces that have different symbols and formulas on them. The person is looking at a screen that shows the 📋 Getting Ready guide and has a smile on their face.”
- Occasionally, I’ll present you with tasks before diving into the corresponding theory or background knowledge.
- For example: asking you to consult the
tidyversedocumentation instead of explaining it directly.
- For example: asking you to consult the
- Reasoning: letting your ‘struggles’ guide the learning process.
- 👉 allow yourself to make silly mistakes and to ask ‘dumb questions’.
- But if you feel this is not working, drop me an e-mail or come to my office hours (see 📟 Communication)
AI tools in this course
Do you use ChatGPT, GitHub Copilot, or other AI tools?
What do you think of generative AI tools?
The ![]()
GENIAL project
- More and more students are giving ChatGPT a try during lectures and labs.
- So we are doing some research to try to figure out:
- When do AI tools like ChatGPT and GitHub Copilot help you learn better?
- Are they genuinely useful or just a distraction?
Participating Courses:
DS105
Data for Data Science
DS202
Data Science for Social Scientists
ST207
Databases
Sign up to 
GENIAL and help us find out!
The ![]()
GENIAL project
How will it work:
W01 lab
Normal lab
W02 lab
Normal lab
W03 lab
- 1st Half: Normal lab
- 2nd Half: Work independently
(just you + ChatGPT)
W04 lab
Participants will be split into two groups:
- Those who can use ChatGPT
- Those who can’t use ChatGPT
W05 lab
Normal lab
W07 lab
Participants will be split into two groups at random:
- Those who CAN vs CANNOT use GitHub Copilot/ChatGPT
W08 lab
Normal lab
W09 lab
Participants will be split into two groups at random:
- Those who CAN vs CANNOT use GitHub Copilot/ChatGPT
W10 lab
Normal lab
W11 lab
Normal lab
Sign up to GENIAL and help us find out!
The ![]()
GENIAL project
- Participation is voluntary but you MUST opt-in to participate in the study 👉
- You can opt-out at any time.
Sign up to GENIAL and help us find out!
☕️ Time for a break
Image created with DALL·E via Bing Chat AI bot. Prompt: “robots enjoying a coffee break. Circular tables, white room, pops of color, modern, cosy, clean flat design.”
Our first proper lecture will start in a few minutes.
“What really is data science? + R tips”
In the meantime, consider signing up for the GENIAL project:
Python vs R
- Python is a general-purpose programming language
- It is used for web development, scientific computing, data science, advanced machine learning tools (deep learning), etc.
- R is more niche. It is a programming language created for statistical computing
- You can do many other things with R, but it is mostly used for statistics and general data science (except for heavy Machine Learning)
R for Python users
Data types
- In R, you assign a variable using the operator
<-:
var <- 2- Some basic data types:
var <- "value" # A string. Single quotes are OK too
var <- 2.2 # A double (aka numeric)
var <- 2 # Also a double! 😱
# Want an integer? You have to be explicit:
var <- as.integer(2)
- Whereas in Python, assignments are done with
=:
var = 2- The python equivalent:
var = "value" # A string. Single quotes are OK too
var = 2.2 # A float
var = 2 # An int (🏅)R for Python users
R Vectors
- R is a vectorized language. Everything is a vector!
- Amongst other things, this means we can call
length()on any variable:
In the example below, var is a vector of length 1.
# This is a vector!
var <- 2.2
length(var)returns:
[1] 1Use the c( ) function to concatenate vectors:
# A vector of length 3
var <- c(2.2, 3.3, 4.4)
length(var)returns:
[1] 3R for Python users
R Vectors (cont.)
- ⚠️ R vectors can only have one data type!
This is straightforward:
# A vector of numbers
c(1, 2, 3, 4, 5)
# A vector of characters
c("a", "b", "c", "d", "e")
# A vector of booleans
c(TRUE, FALSE, TRUE, FALSE, TRUE)But beware! The code below is also valid:
my_vec <- c(2, "3", as.integer(4))It won’t throw an error, but once you inspect the type of the vector, you will see that typeof(my_vec) is a "character".
If you type:
my_vecYou will see:
[1] "2" "3" "4"R for Python users
Lists (not the same as vectors)
- If you need to keep elements of different data types, create a list instead.
my_list <- list(2, "3", as.integer(4))Now, if you type:
my_listYou will see:
[[1]]
[1] 2
[[2]]
[1] "3"
[[3]]
[1] 4- We see a list with a length of 3
- Each element of the list is shown after the double brackets,
[[ ]] - The first element of the list (
[[1]]) is a vector of size 1 ([1]) that contains the number2, etc.
R for Python users
Lists are more flexible than vectors (they are also slower to process)
Vectors are always flat:
# Me trying to do something complicated
my_silly_vector <- c(1, c(2, c(3, 4)), 5)
my_silly_vectoryields a simple vector:
[1] 1 2 3 4 5Lists preserve the structure:
# Let's try converting some vectors into a list
my_silly_list <- list(1, list(2, c(3, 4)), 5)
my_silly_listThis produces a list of length 3 (not 5) with a more complex, nested structure:
[[1]]
[1] 1
[[2]]
[[2]][[1]]
[1] 2
[[2]][[2]]
[1] 3 4
[[3]]
[1] 5R for Python users
Obs: Python does not have vectors, only lists
- If you run:
elements = [2, "3", 4]- You will get a list of length 3, with elements of different data types:
type(elements)listlen(elements)3elements[2, '3', 4](preserved structure)
R for Python users
Loops are not that different
for (i in 1:10) {
print(i)
}while (i < 10) {
print(i)
i <- i + 1
}(R needs the curly brackets)
for i in range(1, 11):
print(i)
while i < 10:
print(i)
i += 1
(Python needs the indentation)
R for Python users
Custom functions definition, compared
my_function <- function(x) {
return(x + 1)
}my_function(2)In R, the return keyword exists, but it is optional. Whatever is at the last line of the function will be returned.
my_function <- function(x) {
x + 1
}
def my_function(x):
return x + 1
my_function(2)Base R vs tidyverse
R has a base set of functions that come with the installation of the language
The base functions are OK - they are just not awesome.
The
tidyverseis not part of the base R installation, but it is a very popular packageIt is actually a collection of several packages that make it easier to manipulate data (+ databases + plotting + modelling + etc.)
This is what we will use in this course. (We suffered tremendously teaching base R last year)
Note to Python users
Think of the tidyverse as what pandas is to Python
Base R vs tidyverse
Example: reading a csv file
# Base R
my_data <- read.csv("my_file.csv")
# tidyverse
my_data <- read_csv("my_file.csv")Base R vs tidyverse
Example: selecting columns
# Base R
my_data <- my_data[, c("col1", "col2")]
# tidyverse
my_data <- select(my_data, col1, col2)Base R vs tidyverse
Example: filtering rows
# Base R
my_data <- my_data[my_data$col1 == 1, ]
# tidyverse
my_data <- my_data %>% filter(col1 == 1)Example: combing columns together
# Base R
my_data$col3 <- my_data$col1 + my_data$col2
# tidyverse
my_data <- my_data %>% mutate(col3 = col1 + col2)Base R vs tidyverse
Example: grouping and summarizing
Say we have a random dataset:
# Generate a random my_data
my_data <- data.frame(col1 = sample(1:3, 100, replace = TRUE), col2 = rnorm(100))If we want to calculate the mean of col2 for each value of col1:
# The Base R way
my_data <- aggregate(my_data, by = list(my_data$col1), FUN = mean)
# Overtime, you will see that the tidyverse way becomes more intuitive
my_data <- my_data %>% group_by(col1) %>% summarize(mean(col2))