Welcome! & Data Science Life-cycle

CVEN 5999 - Summer 2025

Lars Schöbitz

Welcome! 👋

Meet the lecturer

Lars Schöbitz (he/him)

• Environmental Engineer
  
• Open Science Specialist at ETH Zurich
• Independent Instructor for Data Science with R
  
• LinkedIn: Lars Schöbitz

Learning Goals (for the course)

1. Be able to use a common set of data science tools (R, RStudio IDE, Git, GitHub, tidyverse, Quarto) to illustrate and communicate the utility of solutions for water, sanitation, air quality, and global health.

2. Learn to use the Quarto file format and the RStudio IDE visual editing mode to produce scholarly documents with citations, footnotes, cross-references, figures, and tables.

Why are you here?

Pick an item

Take notes for 2 minutes:
What does the item you have picked have to do with the reason for you being here?

Why are you here?

In break-out rooms

Take 2 minutes each to share with your room partner:
What does the item you have picked have to do with the reason for you being here?

From which country are you joining us?

In the Zoom chat

Share with us from which country you are joining us.

Learning Objectives (for this week)

1. Learners can navigate the platforms (Posit Cloud, GitHub, Course Website) that are used to for the course.
2. Learners can render a Quarto file to an output file in HTML, PDF and DOCX format.
3. Learners can list the six elements of the data science lifecycle.
4. Learners can identify four components of a Quarto file (YAML, code chunk, R code, markdown).

Artwork from @juliesquid for @openscapes (illustrated by @allison_horst)

Classroom tools

Live Coding Exercises

• Instructor writes and narrates code out loud
• Intstructor explains elements and principles that are relevant
• Code is displayed on second screen / split screen
• Learners join by writing and executing the same code
• Learners “code-along” with the instructor

Pair Programming Exercises

• Two learners work together in a break out session
• One person (the driver) shares the screen and does the typing
• The other person (the navigator) offers comments and suggestions
• Roles get switched

Platforms and Tools

• R
• Posit Cloud
• RStudio IDE
• tidyverse R Packages
• Quarto publishing system

cven5999-ss25.github.io/website/ 🔖

Posit Cloud

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Screen setup

Who uses a setup with one screen?

“One screen” in the Zoom Chat

Screen setup

Who uses a setup with two screens?

“Two screens” in the Zoom Chat

Email from GitHub?

Please accept the invitation to the GitHub organisation for the course

Live Coding Exercise

git-configuration

Follow along on the screen

1. Open the GitHub organisation for the course: https://github.com/cven5999-ss25
2. You will find a repository titled: wk-02-USERNAME (with your GitHub Username)
3. You will “clone” this repository to Posit Cloud

Break

GitHub PAT from week 1

Do you have your GitHub Personal Access Token readily accessible?

10:00

Photo by Blake Wisz

Version Control

Version Control with Git and GitHub

A way to share files with others, so they can:

• download
• re-use
• contribute

You can view the history of files, and jump back in time to any point.

Why is it useful?

Git and GitHub

• Git is a software for version control
• Released in 2005
• Popular among programmers collaboratively developing code
• Tracks changes in a set of files (directory/folder/repository)

• GitHub is a hosting platform for version control using Git
• Launched in 2008, aquired by Microsoft in in 2018, Microsoft for US$ 7.5 billion
• 73 million Users (February, 2022)
• Facebook for Software Developers

Version Control - Terminology

Data Science Lifecycle

Deep End

via GIPHY

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Add notes

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1. First you must import your data into R. This typically means that you take data stored:

• in a file
• in a database,
• or web application, and load it into R.

2. If you can get your data into R, you can do data science on it!

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1. Once you’ve imported your data, it is a good idea to tidy it.

2. This means to get it into a structure that lets you focus on the questions about your data and not the format it needs to be in

3. This is the step where we spend by far the biggest amount of our time.

4. It has been estimated that we spend about 80% of our time with cleaning and tidying data, so that we can use it fo our analyses.

5. In my courses you will learn how to structure your data in a tidy format as you collect it, and before you import it. A highly underestimated skill.

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1. Once your data is tidy, a common first step is to transform it.

2. This includes

• narrowing in on what interests you (like all people without toilets in one district, or all data from last year)
• creating new variables from existing (like toilet density, as in number of people per toilet)
• calculating summary statistics (like counts or the mean)

Together, tidying and transforming are called wrangling, because getting your data in a form that’s natural to work with often feels like a fight!

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1. Once you have tidy data with the variables you need, there are two main engines of knowledge generation:

• visualisation
• and modelling.

2. These have complementary strengths and weaknesses so any real analysis will iterate between them many times.

3. Visualisation is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data.

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1. Models are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them.

2. In my courses, they play a very small part, but I am confident that you will be able to enhance your knowledge on modelling once you understand the basic principles of using the R lanuage.

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1. The last step of data science is communication, an absolutely critical part of any data analysis project.

2. It doesn’t matter how well your models and visualisation have led you to understand the data, unless you can also communicate your results to others.

3. And this is a part where R has become really strong.

Live Coding Exercise

live-data-science-lifecycle

1. Head over to posit.cloud
2. Open the workspace for the course cven5999-ss25
3. Open “Projects”
4. Open the “wk-02-USERNAME” project
5. Follow along with me

Break

05:00

Photo by Blake Wisz

R

Packages

base R

sqrt(49)
sum(1, 2)

• Functions come with R

R Packages

library(dplyr)

• Installed once in the Console: install.packages("dplyr")
• Loaded per script

Packages

• Main way that reusable code is shared in R
• Combination of code, data, and documentation
• R has a rich ecosystem of packages for data manipulation & analysis

Functions & Arguments

library(dplyr)

filter(.data = gapminder, 
       year == 2007)

• Function: filter()
• Argument: .data =
• Arguments following: year == 2007 What do do with the data

Objects

library(dplyr)

gapminder_yr_2007 <- filter(.data = gapminder, 
                            year == 2007)

• Function: filter()
• Argument: .data =
• Arguments following: year == 2007 What do do with the data
• Object: gapminder_yr_2007

Operators

library(dplyr)

gapminder_yr_2007 <- gapminder |> 
  filter(year == 2007) 

• Function: filter()
• Argument: .data =
• Arguments following: year == 2007 What do do with the data
• Object: gapminder_yr_2007
• Assignment operator: <-
• Pipe operator: |>

Rules

Rules of dplyr functions:

• First argument is always a data frame
• Subsequent arguments say what to do with that data frame
• Always return a data frame
  
• Don’t modify in place

Course information

Weekly Structure

Monday	Lecture
Tuesday
Wednesday
Thursday	Feedback (grading) on assignments from previous week
Friday	Homework assignment and learning reflection are due

Homework assignments

• Weekly programming assignments
• Graded as pass/fail (100 pts)
• Submitted as rendered Quarto documents on GitHub
• weighted at 40% of the total grade

Learning reflections

• Reflections on the different class elements (lecture, homework assignment, readings)
• Graded as pass/fail (100 pts)
• minimum 100 words
• Submitted as rendered Quarto documents on GitHub
• weighted at 20% of the total grade

Capstone Project

• Data analysis project report with a data set of your choice
• Graded as number of points out of 100 pts for pre-defined graded elements
• Submitted as rendered Quarto document on GitHub
• weighted at 40% of the total grade

Grading

Conversion from percent to grades.
grade	percent
A+	97
A	93
A-	90
B+	87
B	83
B-	80
C+	77
C	73
C-	70
D+	67
D	63
D-	60
F	0

Late work policy

• due dates are set and all work is due on the stated date
• work not submitted by the due date will receive 0 pts
• the lowest score for each of the assignments or learning reflections is dropped

Homework week 2

Homework due dates

• All material on course website
• Homework assignment & learning reflection due: 2025-06-13

Thanks! 🌻

Slides created via revealjs and Quarto: https://quarto.org/docs/presentations/revealjs/

Access slides as PDF on GitHub

All material is licensed under Creative Commons Attribution Share Alike 4.0 International.