Introduction

class: center, middle, title-slide

.title[
# Introduction
]
.subtitle[
## JSC 370: Data Science II
]

---

a, a > code {
  color: #4393C3;
  text-decoration: none;
}

strong {
  color: #000000;
}

em {
  color: #08519C;
  font-style: normal;
  font-weight: bold;
}

del {
  color: #E5E5E5;
  text-decoration: none;
  font-weight: bold;
}

/* General slides */
.remark-code {
  font-family: 'Hack', 'Lucida Console', Monaco, monospace;
  font-size: 65%;
}

.remark-inline-code {
  background: #F5F5F5;
  border-radius: 3px;
  padding: 4px;
}

.remark-slide-content {
  color: #474747;
  font-weight: 300;
  font-size: 180%;
  padding: 1em 2em 1em 2em;
}

/* Slide headers */
.remark-slide-content h1 {
  color: #444444;
  font-family: Montserrat;
}

.remark-slide-content h2 {
  color: #444444;
  font-family: Montserrat;
}

.remark-slide-content h3 {
  color: #444444;
  font-family: Montserrat;
}

.remark-slide-content h4 {
  color: #444444;
  font-family: Montserrat;
}

.remark-slide-content h5 {
  color: #444444;
  font-family: Montserrat;
}

/* Images */
img, video, iframe {
  max-width: 100%;
}

/* Lists */
li {
  padding: 10px;
  display: block;
}

li::before {
  content: "- ";
  font-weight: bold;
}

/* Title slide */
.title-slide {
  background-color: #FFFFFF;
}

.title-slide .remark-slide-number {
  display: none;
}

.title-slide h1 {
  color: #101645;
  font-size: 72px;
  text-shadow: none;
  font-family: Montserrat;
  text-align: center;
  vertical-align: bottom;
}

.title-slide h2 {
  color: #101645;
  text-shadow: none;
  font-family: Montserrat;
  font-size: 48px;
  text-align: center;
  font-weight: bold;
}

.title-slide h3 {
  color: #333333;
  text-shadow: none;
  font-family: Montserrat;
  font-size: 36px;
  text-align: center;
  margin-bottom: 10px;
}

.title-slide h4 {
  color: #333333;
  text-shadow: none;
  font-family: Montserrat;
  font-size: 24px;
  text-align: center;
  margin-bottom: 10px;
}

.hidden {
  visibility: hidden;
}
</style>

# Instructors

- Meredith Franklin: meredith.franklin@utoronto.ca
- Michael Moon: michael.moon@mail.utoronto.ca

---

# My Background

- Last year moved from Los Angeles where I was an Assistant/Associate Professor of Biostatistics at University of Southern California
- From Canada originally, McGill math for BSc, Ottawa/Carleton Institute of Math for MSc, Harvard for PhD, UChicago for postdoc
- At U of T I'm an Associate Professor with tenure in the Department of Statistical Science (51%) and the School of the Environment (49%)

---

# My Teaching

- Founded a Master's of Health Data Science program at USC that launched in 2020
- Co-taught the introduction data science course
- Taught graduate-level spatial statistics, inference, linear models
- Taught undergraduate intro to stat (once!)
- This semester I am also teaching STA255

---

# My Research

- Spatial statistical methods for environmental data
- Data science techniques for remote sensing data/imagery
- Focus on pollution (air, noise) and climate (ghg, land cover change)
- Machine learning becoming a big part of environmental research

.center[
![](img/research_fig.png)
]
---

# Course Goals

Through this course, you will hone the techniques used in Data Science. You will learn:

- Programming in R, and tools Markdown, Git
- Exploratory data analysis – generating hypotheses and building intuition
- Data visualization – showing data through interpretable summaries
- Data collection – data scraping, wrangling, cleaning
- Statistical (machine learning) algorithms
- Building a github.io website

---

# Quercus + Git + Piazza

Course website - lecture slides, labs, data

https://jsc370.github.io/jsc370-2023/

Quercus - announcements, homework solutions, lab solutions, guest speaker reflections, grading

https://q.utoronto.ca/courses/298698

Quercus - questions and discussion

---

# What is data science?

- Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge.

.center[
![](img/data-science.png)
]

---

.center[
![](img/data-science-drew-conway.jpg)
]

---

# Data science can be really cool

<figure align="center">
<img src="https://imgs.xkcd.com/comics/regular_expressions.png" style="width:450px">
<figcaption>Source: https://xkcd.com/208/</figcaption>
</figure>

---

# With great power comes great responsibility

<figure align="center">
<img src="https://imgs.xkcd.com/comics/extrapolating.png" style="width:500px">
<figcaption>Fuente: https://xkcd.com/605/</figcaption>
</figure>

---

![](img/demand.png){height=20%}

---

# Data Scientists in Demand

Also see [here](https://www.amstat.org/asa/News/New-Report-Highlights-Growing-Demand-for-Data-Science-Analytics-Talent.aspx), 
and [here](https://www.ibm.com/downloads/cas/3RL3VXGA),
and [here](https://www.forbes.com/sites/gilpress/2021/06/27/salaries-and-job-opportunities-for-data-scientists-continue-to-rise/),
and [here](https://www.glassdoor.com/research/job-market-report/)

A good [data science subreddit](https://www.reddit.com/r/datascience/) to follow - it provides insights on jobs, academic programs, and there are AMAs from industry leaders.

Another good resource is [Towards Data Science](https://towardsdatascience.com/)

---

# What is this course?

This course is a introduction to the world of data science following on from where JSC 270 left off.

The course will teach language agnostic skills that are easily transferable, with examples done in R.

You can use any language/tool you prefer. But I can only guarantee help if you are using R and RStudio.

---

# What is R?

> R is a language and environment for statistical computing and graphics.
-- https://r-project.org

Created by statisticians for statisticians.

Over 16,000 packages added to CRAN

---

.center[
![](img/datascience_code.png)
]

---

# History of R

Originates from S, which was developed by Bell Labs in the 1970s

First versions of R were developed by Robert Gentleman and Ross Ihaka of U Aukland in mid-1990s

R is intended for statisticians but used by many (>2M users!)

R is open source, has nice graphics and visualizations

A lot of help is available online (Stack Overflow, R package vignettes, Journal of Statistical Software)

---

# R Data Science Resources

1)	R Programming for Data Science, 2019. Roger Peng. https://bookdown.org/rdpeng/rprogdatascience/

Supplementary References

2)	R for Data Science, 2017 Garrett Grolemund and Hadley Wickham. http://r4ds.had.co.nz/

3)	Exploratory Data Analysis with R, 2020 Roger Peng https://bookdown.org/rdpeng/exdata/

4)	Mastering Software Development in R, 2017 Roger Peng, Sean Kross, Brooke Anderson https://bookdown.org/rdpeng/RProgDA/

---

# R in the terminal
<figure align="center">
<img src="R_terminal.png" height="500px">
</figure>

---

# What is RStudio?

> RStudio is an integrated development environment (IDE) for R.
-- https://rstudio.org/products/rstudio

---

.center[
![](img/moderndive-r-vs-rstudio.png)
]

---

# R + RStudio

---

## GitHub

--
- Version control is necessary in the trade of data science and is used in industry and academia

--
- Building up a solid GitHub profile will put you in a good position for job hunting

--
- You will build a github.io website as part of this course

---

# First Week

The lab exercises can be found on the course website in the schedule

https://jsc370.github.io/jsc370-2023/

Download the Rmd files

Submit individually completed lab at the end of day Wednesday

---
# Next Week

Guest speaker 1-2pm Monday January 16

Upload 1/2 page summary of guest speaker's seminar by Wednesday end of day to Quercus

Lecture 2-3pm Monday Jan 16 (Version control)

Lab 1-3pm Wednesday Jan 18 (Version control)