Introduction to Data Science

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.title[
# Introduction to Data Science
]
.subtitle[
## Session 1: What is data science?
]
.author[
### Simon Munzert
]
.institute[
### Hertie School
]

---

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name: welcome

# Welcome!
<html><div style='float:left'></div><hr color='#EB811B' size=1px style="width:1000px; margin:auto;"/></html>

---

# Introductions

### Course

Much of this course lives on GitHub. You will find lecture materials, code, assignments, and other people's presentations there. We also have Moodle, which is is for everything else.

### Me

### You

What's your name? And would you share a fun fact about yourself?

---

# More about you

---

# More about you

.pull-left[
<div align="center">
<br><b>MPP/MIA/PhD</b>
<br>
<img src="pics/wordcloud-mpp.png" height=400>
</div>
]

.pull-right[
<div align="center">
<br><b>MDS</b>
<br>
<img src="pics/wordcloud-mds.png" height=400>
</div>
]

---

# More about you

.pull-left[
<div align="center">
<br><b>MPP/MIA/PhD</b>
<br>
<img src="pics/terms_freq_mpp.png" height=400>
</div>
]

.pull-right[
<div align="center">
<br><b>MDS</b>
<br>
<img src="pics/terms_freq_mds.png" height=400>
</div>
]

---

# The labs

.pull-left-wide[

## Who & how

- This course is accompanied by labs administered by **Hiba Ahmad** and **Steve Kerr**.
- The labs are mandatory (MDS) / optional (the rest). Please attend them in any case.
- As with the regular classes, please stick to the lab you are assigned to.

## What for

- What these sessions are meant for:
  - Applying tools in practice
  - Discussion of issues related to the assignments
  - Boosting your R skills
- What these sessions are **not** meant for: 
  - Solving the assignments for you
  - Taking care of developing your coding skills
]

.pull-right-small-center[
<div align="center">
<br>
<img src="pics/ahmad-circle.png" height=200>
<br>
<br>
<img src="pics/kerr-circle.png" height=200>
</div>
]

---

# Class etiquette

.pull-left-wide[
<br>

- Learning how to code can be challenging and might lead you out of your comfort zone. If you have problems with the pace of the course, let me and the TAs know. I expect your commitment to the class, but **I do not want anyone to fail.**
- You are all genuinely interested in data science. But there is also considerable variation in your backgrounds. This is how we like it! Some sessions will be more informative for you than others. If you feel bored, **look out for and help others**, or explore other corners of R you don't know yet.
- The pandemic is still around, and other crises have emerged. We are affected by them in different ways. **Let's support each other.** 
- **Be respectful** to each other, all the time. This includes the TAs and me.
- **Ask questions** whenever you feel the need to do so!
]

.pull-right-small-center[
<div align="center">
<br><br>
<img src="pics/stupid-questions.jpg" height=400>
</div>
]

---

# Table of contents

</br></br>

1. [Welcome!](#welcome)

2. [What is data science?](#whatisdatascience)

3. [Sneak preview](#preview)

4. [Class logistics](#logistics)

---
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name: whatisdatascience

# What is data science?
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# A classic definition of data science

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# A classic definition of data science

---

# A classic definition of data science

---

# A classic definition of data science

---

# A classic definition of data science

---

# A classic definition of data science

---
background-image: url("pics/vintage-pipeline.jpeg")
background-size: contain
background-color: #000000

# The data science pipeline

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
- **Design** conceptualize, build data collection device
- **Data collection** recruit, collect, monitor

]

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
- **Design** conceptualize, build data collection device
- **Data collection** recruit, collect, monitor

**Data operation**

]

.pull-right-center[
<br><br><br>
<div align="center">
<img src="pics/data-science-flow.png" height=200>
</div>
]

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
  - **Design** conceptualize, build data collection device
  - **Data collection** recruit, collect, monitor

**Data operation**

- **Wrangle**: import, tidy, manipulate
   
]

.pull-right-center[
<br><br><br>
<div align="center">
<img src="pics/data-science-wrangle.png" height=200>
</div>
]

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
  - **Design** conceptualize, build data collection device
  - **Data collection** recruit, collect, monitor

**Data operation**

- **Wrangle**: import, tidy, manipulate
   - **Explore**: visualize, describe, discover

]

.pull-right-center[
<br><br><br>
<div align="center">
<img src="pics/data-science-explore.png" height=200>
</div>
]

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
  - **Design** conceptualize, build data collection device
  - **Data collection** recruit, collect, monitor

**Data operation**

- **Wrangle**: import, tidy, manipulate
   - **Explore**: visualize, describe, discover
   - **Model**: build, test, infer, predict

]

.pull-right-center[
<br><br><br>
<div align="center">
<img src="pics/data-science-model.png" height=200>
</div>
]

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
  - **Design** conceptualize, build data collection device
  - **Data collection** recruit, collect, monitor

**Data operation**

- **Wrangle**: import, tidy, manipulate
   - **Explore**: visualize, describe, discover
   - **Model**: build, test, infer, predict

**Dissemination**

- **Communicate**: to the public, media, policymakers
  - **Publish**: journals/proceedings, blogs, software
  - **Productize**: make usable, robust, scalable

]

.pull-right-center[
<br><br><br>
<div align="center">
<img src="pics/data-science-communicate.png" height=200>
</div>
]

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
  - **Design** conceptualize, build data collection device
  - **Data collection** recruit, collect, monitor

**Data operation**

- **Wrangle**: import, tidy, manipulate
   - **Explore**: visualize, describe, discover
   - **Model**: build, test, infer, predict

**Dissemination**

- **Communicate**: to the public, media, policymakers
  - **Publish**: journals/proceedings, blogs, software
  - **Productize**: make usable, robust, scalable

**Meta skill: programming**

]

.pull-right-center[
<br><br><br>
<div align="center">
<img src="pics/data-science-program.png" height=200>
</div>
]

---

# The data science pipeline

.pull-left[

**Preparatory work**

- **Problem definition** predict, infer, describe
  - **Design** conceptualize, build data collection device
  - **Data collection** recruit, collect, monitor

**Data operation**

- **Wrangle**: import, tidy, manipulate
   - **Explore**: visualize, describe, discover
   - **Model**: build, test, infer, predict

**Dissemination**

- **Communicate**: to the public, media, policymakers
  - **Publish**: journals/proceedings, blogs, software
  - **Productize**: make usable, robust, scalable

**Meta skill: programming with R**
]

.pull-right-center[
<br><br>
<div align="center">
<img src="pics/armyknife2.jpg" height=350>
</div>
]

---
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name: preview

# Sneak preview
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---

# Introduction to Data Science in a nutshell

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# Sneak preview <br> Learning to love a programming environment

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# The tidyverse

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# Sneak preview <br> Collecting web data at scale

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# Scraping the web for social research

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<div align="center">
<img src="pics/pap1.png" height=300>
<br><br>
<img src="pics/pap4.png" height=200>
</div>
]

.pull-right-center[
<div align="center">
<img src="pics/cld-paper.png" height=250>
<br>
<img src="pics/leakingnetworks.png" height=260>
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]

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# Sneak preview <br> Applying data science to tackle social problems

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# Tracking the usage of a contact tracing app

.pull-left-center[
<div align="center">
<img src="pics/nhb-screenshot.png" height=300>
<br><br>
<img src="pics/corona-warn-app-android-ios.jpeg" height=200>
</div>
]

.pull-right-center[
<div align="center">
<img src="pics/cwa-experiment.png" height=250>
<br>
<img src="pics/cwa-timeline.png" height=260>
</div>
]

---
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background-color: #fff

# Reducing hate speech on social media

.pull-left-center[
<div align="center">
<img src="pics/munger-1.png" height=500>
</div>
]

.pull-right-center[
<div align="center">
<img src="pics/munger-2.png" height=250>
<br>
<img src="pics/munger-3.png" height=260>
</div>
]

---
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# Monitoring the effects of climate change on health

.pull-left-center[
<div align="center">
<img src="pics/lancet-2020.png" height=300>
<br><br>
<img src="pics/lancet-2020-fig1.png" height=200>
</div>
]

.pull-right-center[
<div align="center">
<img src="pics/clickstream_network_graph_health_climate_labels_2020.png" height=250>
<br>
<img src="pics/clickstream-views-2018-2020.png" height=260>
</div>
]

---
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# Sneak preview <br> Getting to know the limits of data

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# Calling bullsh*t when you see it

.pull-left-vsmall[

<br>

**Learn not to be fooled** by 
- big data
- garbage data
- garbage models
- weird samples
- claims of generality
- statistical significance
- implausibly large effect sizes
- highly precise forecasts
- overfitted models

And much more...
]

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<img src="pics/sc01.png" height=130> 
<img src="pics/gigo.png" height=130>
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<img src="pics/criminal-faces-2.png" height=130>
<img src="pics/calling-bullshit-2.jpg" height=130>
<img src="pics/survivorship-bias.png" height=130>
</br>
<img src="pics/cor-examples7.png" height=130>
<img src="pics/overfitting.png" height=130>
<img src="pics/nyt-opera.png" height=130>
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]

---
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# Sneak preview <br> Reflecting everyday ethics in data science

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# How do I pay clickworkers fairly?

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# How do I respect intellectual property?

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# How do I protect the privacy of my research subjects?

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# How do I protect the safety of my research subjects?

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# How do I ensure statistical, measurement validity, etc.?

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# How do I ensure an open science workflow?

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# How do I communicate results honestly?

---
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name: logistics

# Class logistics
<html><div style='float:left'></div><hr color='#EB811B' size=1px style="width:1000px; margin:auto;"/></html>

---
# The plan

.pull-left[

### Goals of the course

- This course equips you with conceptual knowledge about the data science pipeline and coding workflow, data structures, and data wrangling.
- It enables you to apply this knowledge with statistical software.
- It prepares you for our other core courses and electives as well as the master’s thesis.

]

.pull-right[
### What we will cover

- Version control and project management
- R and the tidyverse
- Programming workflow: debugging, automation, packaging
- Relational databases and SQL
- Web data and technologies
- Model fitting and evaluation
- Viusalization
- Monitoring and communication
- Data science ethics
- (The command line)

]

---
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# You at the beginning of the course

---
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# You at the end of the course

---

# Why R and RStudio?

### Data science positivism

- Alongside Python, R has become the *de facto* language for data science.
  - See: [*The Impressive Growth of R*](https://stackoverflow.blog/2017/10/10/impressive-growth-r/), [*The Popularity of Data Science Software*](http://r4stats.com/articles/popularity/)
- Open-source (free!) with a global user-base spanning academia and industry.
  - "Do you want to be a profit source or a cost center?"

### Bridge to multiple other programming environments, with statistics at heart

- Already has all of the statistics support, and is amazingly adaptable as a “glue” language to other
programming languages and APIs.
- The RStudio IDE and ecosystem allow for further, seemless integration.

### Path dependency

- It's also the language that I know best.
- (Learning multiple languages is a good idea, though.)

---
# Why R and RStudio? (cont.)

---
# Why R and RStudio? (cont.)

<div align="center">
<img src="pics/facebook-programming-languages.png" width=600>
</div>
`Credit` [Left_Ad8361/Reddit](https://www.reddit.com/r/dataisbeautiful/comments/qw1bew/oc_which_programming_language_is_required_to_land/)

---
# Core (and optional) readings

---
# Attendance

## General rules

- You cannot miss more than two sessions. If you have to miss a session for medical reasons or personal emergencies, please **inform Examination Office** and they will inform me about your absence. There is no need to notify me in advance or ex post.
- We will check attendance on-site.
- The current **Hertie hygiene rules** apply!

---
# Office hours and advice

-  If you want to discuss content from class, please first do so in the lab sessions. 
- If you still need more feedback on course topics, use the Moodle forum.
- If you want to discuss any other matters with me, drop Alex Karras, my assistant, a message (<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 112c-8.8 0-16 7.2-16 16v22.1L220.5 291.7c20.7 17 50.4 17 71.1 0L464 150.1V128c0-8.8-7.2-16-16-16H64zM48 212.2V384c0 8.8 7.2 16 16 16H448c8.8 0 16-7.2 16-16V212.2L322 328.8c-38.4 31.5-93.7 31.5-132 0L48 212.2zM0 128C0 92.7 28.7 64 64 64H448c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128z"/></svg> [karras@hertie-school.org](mailto:karras@hertie-school.org)) and he will arrange a meeting.
- For general technical advice, the [Research Consulting Team at the Data Science Lab](https://hertie-data-science-lab.github.io/research-consulting/) is there for you.

---
# Assignments and grading

| Component                           | Weight |
|:-|-:|
| 4(5) × homework assignments (10% each) | 40% |
| 4(5) × online quizzes (5% each) | 20% |
| 1 × workshop presentation/attendance 	| 10% |
| 1 × hackathon project       | 30% |

### Homework assignments 
- The assignments are distributed via our own [GitHub Classroom](https://classroom.github.com/classrooms/113288586-introduction-to-data-science-fall-2023).
- Each assignment is a mix of practical problems that are to be solved with R.
- You are encouraged to collaborate, but everyone will hand in a separate solution.
- There will be 5 assignments (one every ~2 weeks; see [overview on GitHub](https://github.com/intro-to-data-science-23/assignments)) and the 4 best will contribute to the final grade.
- You'll have one week to work on each assignment (deadline: Tuesdays at 9:30am).
- You submit your solutions via GitHub.

---
# Assignments and grading

### Homework assignments 
- Grades will be based on (1) the accuracy of your solutions and (2) the adherence of a clean and efficient coding style.
- Feedback will be verbal:
  - Excellent (95+)
  - Very good (90-94)
  - Good (85-89)
  - OK (80-84)
  - Acceptable (75-79)
  - Definitely needs improvement (below 75)

---
# Assignments and grading

### Online quizzes
- The short online quizzes will test your knowledge about the topics covered in class.
- There will be 5 quizzes and the 4 best will contribute to the final grade.
- You'll have one week to work on each assignment (deadline: Tuesdays at 9:30am).

---
# Assignments and grading

### Workshop presentation (MDS students)
- On October  30, 14-20h, we will flip roles and you will become instructor of a data science workshop session.
- You, in groups of 2 students, will present a data science workflow tool (randomly [allocated](https://github.com/intro-to-data-science-23/workshop-presentations)).
- Your contribution will include:
  1. A lightning talk (recorded) where you briefly introduce and motivate the tool
  2. A hands-on session where you showcase the tool and provide practice material
- Both the recorded talk and the materials will be graded.
- Check out the materials from previous workshops online [`>2021<`](https://intro-to-data-science-21-workshop.github.io/) [`>2022<`](https://intro-to-data-science-22-workshop.github.io/)!
- **MPP/MIA students**: You will not give a talk, but have to actively participate in the workshop.

---
# Assignments and grading

### Hackathon project
- On December 4, 17-20h, there will be a hackathon hosted at Hertie.
- At the hackathon itself, we introduce the data and provide an environment that should facilitate you getting started with the project and form groups of 3-4 students.
- Two weeks later, on December 18, the project instructions will be made available. You will then have 48 hours to submit your solutions.
- The task is similar to the homework assignments but puts more emphasis on creative problem-solving using the tools and techniques you have learned in class.

---
# AI use in and for the course

.pull-left-wide[

### Can AI tools (LLM interfaces, AI pair programming) be used for assignments?

- Yes, but use them with care. You will not become an efficient programmer if you heavily rely on those tools without learning the basics.
- The Hertie School has installed [teaching guidelines on the use of AI Tools](https://www.hertie-school.org/en/news/allcontent/detail/content/hertie-school-defines-guidelines-for-use-of-artificial-intelligence-software-at-university) in Spring 2023. We will stick to those guidelines.
- Some key elements from the guidelines:
  - "Familiarity with AI tools is helpful for the learning experience and the professional development of students afterwards, ..."
  - "... but needs to be done with clear guidelines on ethical use, biases, and limits of the tools that are currently available."
  - "[T]he use of AI tools for the preparation of assignments (...) needs to be clearly referenced in the text."
  
]

.pull-right-small-center[
<div align="center">
<br>
<img src="pics/chatgpt-logo.png" height=200>
<br>
<br>
<img src="pics/github-copilot-logo.jpeg" height=200>
</div>
]

---
# Further reading

---
# Further listening

---
# Further watching

---
# Getting started for the course

### Software

1. Download [R](https://cran.rstudio.com/).

2. Download [RStudio](https://www.rstudio.com/products/rstudio/download/preview/).

3. Download [Git](https://git-scm.com/downloads).

4. Create an account on [GitHub](https://github.com/) and register for a student/educator [discount](https://education.github.com/discount_requests/new). You will soon receive an invitation to the course organization on GitHub, as well as [GitHub classroom](https://classroom.github.com), which is how we'll disseminate and submit assignments, receive feedback and grading, etc. 
  
--

### OS extras

- **Windows:** Install [Rtools](https://cran.r-project.org/bin/windows/Rtools/). You might also want to install [Chocolately](https://chocolatey.org/).
- **Mac:** Install [Homebrew](https://brew.sh/).
- **Linux:** None (you should be good to go).

---
# Checklist

☑ Do you have the most recent version of R?
  
  ```r
  R> version$version.string
  ```
  
  ```
  ## [1] "R version 4.3.1 (2023-06-16)"
  ```

☑ Do you have the most recent version of RStudio? (The [preview version](https://www.rstudio.com/products/rstudio/download/preview/) is fine.)
  
  ```r
  R> RStudio.Version()$version
  R> ## Requires an interactive session but should return something like "[1] ‘1.4.1100’"
  ```

☑ Have you updated all of your R packages? 
  
  ```r
  R> update.packages(ask = FALSE, checkBuilt = TRUE)
  ```
  
---
# Checklist (cont.)

Open up the [shell](http://happygitwithr.com/shell.html#shell).
- Windows users, make sure that you installed a Bash-compatible version of the shell. If you installed [Git for Windows](https://gitforwindows.org), then you should be good to go.

☑ Which version of Git have you installed?
  
  ```bash
  $ git --version
  ```
  
  ```
  ## git version 2.37.1 (Apple Git-137.1)
  ```

☑ Did you introduce yourself to Git? (Substitute in your details.)
  
  ```bash
  $ git config --global user.name 'Simon Munzert'
  $ git config --global user.email 'munzert@hertie-school.org'
  $ git config --global --list
  ```

☑ Did you register an account in GitHub?

---
# This semester

---
# Coming up

### The first lab session

Get to know Hiba, Steve, R, and RStudio, four of your best friends for the next months!

### Next lecture

Version control and project management