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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>MAT381E-Week 1: Introduction to Data Science</title>
<meta charset="utf-8" />
<meta name="author" content="Gül İnan" />
<meta name="date" content="2021-10-09" />
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class: left, middle, my-title, title-slide
# MAT381E-Week 1: Introduction to Data Science
### Gül İnan
### Department of Mathematics<br/>Istanbul Technical University
### October 9, 2021
---
class: left
# Outline
* What is Data Science?
* Why R/RStudio?
* Introduction to R/RStudio basics.
* Introduction to RMarkdown.
---
# What is Data Science?
* According to [Wikipedia](https://en.wikipedia.org/wiki/Data_science):
* Data science is an **interdisciplinary** field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
* Data science is a **concept to unify statistics, data analysis, machine learning, and their related methods** to **understand and analyze actual phenomena** with data.
* It employs techniques and theories drawn from many fields within the context of **mathematics**, **statistics**, **information science**, and **computer science**.
---
class: middle, center
<div class="figure" style="text-align: center">
<img src="images/introdata.jpeg" alt="Introduction to Statistics" width="60%" height="100%" />
<p class="caption">Introduction to Statistics</p>
</div>
---
- **Definition of data** has been changed since then...
--
<img src="images/unstructured2.png" width="55%" height="100%" style="display: block; margin: auto;" />
---
- One reason why Data Science is so **popular now** is the **big volumes** of **structured/unstructured** data produced by the following tech companies:
--
.pull-left[
<img src="images/tech.png" width="100%" height="100%" />
]
--
.pull-right[
<div class="figure" style="text-align: center">
<img src="images/2020InternetMinute.png" alt="A minute on the internet in 2020" width="100%" height="100%" />
<p class="caption">A minute on the internet in 2020</p>
</div>
]
---
class: middle, center
<div class="figure" style="text-align: center">
<img src="images/unstructured.png" alt="Structured vs Unstructured data" width="70%" height="100%" />
<p class="caption">Structured vs Unstructured data</p>
</div>
---
- According to [Lawtomated](https://lawtomated.com/structured-data-vs-unstructured-data-what-are-they-and-why-care/), unstructured data comes from:
- **Social Media:** YouTube, Instagram, Twitter.
- **Mobile data:** text messages, locations.
- **Media:** MP3, digital photos, audio recordings and video files.
- **Satellite imagery:** atmospheric images, geographic forms, military movements.
- **Scientific data:** oil and gas exploration, space exploration, and seismic imagery.
---
- According to [DataRobot](https://www.datarobot.com/wiki/data-science/) (rephrased version of [Wikipedia](https://en.wikipedia.org/wiki/Data_science) definition):
- Data science is the field of study that combines **domain expertise**, **programming skills**, and knowledge of **mathematics** and **statistics** to extract meaningful insights from data.
- Data science practitioners apply machine learning algorithms to **numbers, text, images, video, audio, and more** to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence.
- In turn, these systems generate insights which analysts and business users can translate into tangible **business value**.
---
class: middle, center
<div class="figure" style="text-align: center">
<img src="images/datascience2.png" alt="Data Science" width="90%" height="100%" />
<p class="caption">Data Science</p>
</div>
---
class: middle, center
<div class="figure" style="text-align: center">
<img src="images/datascientist.png" alt="Data Scientist" width="70%" height="100%" />
<p class="caption">Data Scientist</p>
</div>
---
class: middle, center
<img src="images/datah.png" width="70%" height="100%" style="display: block; margin: auto;" />
---
<div class="figure" style="text-align: center">
<img src="images/roadmap.png" alt="Mind Map of Data Science Courses" width="80%" height="100%" />
<p class="caption">Mind Map of Data Science Courses</p>
</div>
---
- "Peck plays the guitar, harmonica, kazoo, maracas, and drums (with ropes attached to his shoes, wrist and the guitar head) simultaneously AND sings."
<a href="https://www.flickr.com/photos/randychiu/4602851011/" target="_blank"><img src="images/one_man_band.jpeg" width="30%" height="100%" style="display: block; margin: auto;" /></a>
---
class: middle, center
<a href="https://yapayzeka.itu.edu.tr/" target="_blank"><img src="images/itu.png" width="100%" height="100%" style="display: block; margin: auto;" /></a>
---
class: middle, center
<img src="images/itu2.png" width="100%" height="100%" style="display: block; margin: auto;" />
[İTÜ YZV Ders Planı](https://www.sis.itu.edu.tr/TR/ogrenci/lisans/ders-planlari/plan/YZVE/000000.html)
---
<a href="https://www.ucl.ac.uk/news/2021/feb/ucl-partners-facebook-ai-research-deliver-phd-programme" target="_blank"><img src="images/ucl.png" width="75%" height="100%" style="display: block; margin: auto;" /></a>
- "In the coming year four UCL PhD students will join the new research AI programme; each of the UCL students will be assigned FAIR mentors based at the FAIR London site, well known for its work in **3D computer vision**, knowledge intensive and multilingual **Natural Language Programming **(NLP), and **reinforcement learning** (RL)."
---
class: middle, center
<a href="https://www.microsoft.com/en-us/research/collaboration/bair/" target="_blank"><img src="images/berkley.png" width="100%" height="100%" style="display: block; margin: auto;" /></a>
---
class: middle, center
<a href="https://www.amazon.science/academic-engagements/usc-and-amazon-establish-center-for-secure-and-trusted-machine-learning" target="_blank"><img src="images/amazon.png" width="90%" height="100%" style="display: block; margin: auto;" /></a>
---
class: middle, center
# Some Real-World Data Science Examples
---
class: middle, center
.pull-left[
<img src="images/covid1.png" width="140%" height="100%" />
[Covid-19](https://pubmed.ncbi.nlm.nih.gov/33387306/)
]
--
.pull-right[
<img src="images/covid2.png" width="100%" height="100%" style="display: block; margin: auto;" />
]
---
class: middle, center
<a href="https://news.stanford.edu/2020/05/21/mapping-dry-wildfire-fuels-ai-new-satellite-data/" target="_blank"><img src="images/wildfire.png" width="80%" height="100%" /></a>
[Wildfire](https://news.stanford.edu/2020/05/21/mapping-dry-wildfire-fuels-ai-new-satellite-data/)
---
class: middle, center
<a href="https://journals.sagepub.com/doi/abs/10.1177/08944393211010398" target="_blank"><img src="images/court.png" width="80%" height="40%" /></a>
[Court Decision](https://journals.sagepub.com/doi/abs/10.1177/08944393211010398)
---
class: middle, center
<a href="https://assets.amazon.science/69/8d/2249945a4e10ba8fc758f7523b0c/getting-your-package-to-the-right-place-supervised-machine-learning-for-geolocation.pdf" target="_blank"><img src="images/package.png" width="80%" height="5%" /></a>
[Amazon package delivery](https://assets.amazon.science/69/8d/2249945a4e10ba8fc758f7523b0c/getting-your-package-to-the-right-place-supervised-machine-learning-for-geolocation.pdf)
---
class: middle, center
# When Data Science goes wrong
---
# Algorithmic bias
.pull-left[
<img src="images/bias1.png" width="80%" height="100%" />
]
.pull-left[
- "Algorithmic bias describes **systematic and repeatable errors** in a computer system
that create **unfair outcomes**, such as privileging one arbitrary groups of users over others." -[Wikipedia](https://en.wikipedia.org/wiki/Algorithmic_bias)
- "Machine learning bias generally stems from problems introduced by the individuals who design and/or train the machine learning systems. These individuals could either **create algorithms** that reflect **unintended cognitive biases** or **real-life prejudices**. Or the individuals could introduce biases because they use **incomplete, faulty or prejudicial data sets** to train and/or validate the machine learning systems." -[Margaret Rouse](https://searchenterpriseai.techtarget.com/definition/machine-learning-bias-algorithm-bias-or-AI-bias)
]
---
.pull-left[
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">‘Orwellian’ AI lie detector project challenged in EU court — this one claims to determine honesty of immigrants through facial expressions. Absolutely insane stuff. <a href="https://t.co/AmmPHuhMap">https://t.co/AmmPHuhMap</a></p>&mdash; Eryk Salvaggio (@e_salvaggio) <a href="https://twitter.com/e_salvaggio/status/1357934812175233025?ref_src=twsrc%5Etfw">February 6, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
]
--
.pull-right[
- EU-funded research project using artificial intelligence for facial “lie detection” with the aim of **speeding up immigration checks**.
- The research in question is controversial because the notion of an accurate lie detector machine remains science fiction, and with good reason: There’s no evidence of a **“universal psychological signal”** for deceit.
]
---
.pull-left[
<img src="images/bias2.png" width="80%" height="100%" />
<img src="images/alexa.png" width="80%" height="100%" />
[Voice reconigition](https://www.scientificamerican.com/article/how-speech-recognition-software-discriminates-against-minority-voices/)
]
--
.pull-right[
- The growth of this tech in the past decade—not just **Siri** but **Alexa** and **Cortana** and others—has unveiled a problem in it: **racial bias**.
- "Koenecke points to the most likely: the **data used for training**, which are predominantly from **white, native speakers of American English**. By using databases that are narrow both in the words that are used and how they are said, **training systems exclude accents and other ways of speaking that have unique linguistic features**."
- On average, the authors found, all five programs from leading technology companies, including Apple and Microsoft, showed significant **race disparities**; they were roughly **twice as likely to incorrectly transcribe audio from Black speakers** compared with white speakers.
- This effectively censors voices that are not part of the **standard languages** or accents used to create these technologies.
- For someone with a **disability** who is dependent on these technologies, **being misunderstood** could have serious consequences.
]
---
class: middle, center
# How can we avoid algorithmic bias?
---
<img src="images/fire.png" width="60%" height="100%" />
- Magaret Mitchell wrote a paper about AI safety and ethical concerns related to language models such as GPT3 and BERT.
---
- " ...human beings cannot overcome all forms of bias. But slowing down and learning
what those traps are -as well as how to recognize and challenge them- is critical." Yael Eisenstat (CIA analyst, diplomat and national security advisor at the White House).
- Several ways to **avoid bias**:
- Data management.
- Choice of algorithm.
- Transparency.
- Diverse data science teams.
- Speak out!..
---
class: middle, center
<img src="images/facebook.png" width="70%" height="100%" />
---
class: middle, center
<a href="https://www.youtube.com/watch?v=jZl55PsfZJQ" target="_blank"><img src="images/coded_bias.png" width="70%" height="100%" /></a>
---
# Attributions
- All images used in this slide are taken from the web.
- Some part of this lecture note is developed through following sources:
- [Data Science Labs](https://datasciencelabs.github.io/pages/lectures.html) and
- [Data Science for Beginners](https://bookdown.org/BaktiSiregar/data-science-for-beginners/).
</textarea>
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