Introduction

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# Introduction
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.subtitle[
## JSC 370: Data Science II
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.date[
### January 6, 2025
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# Instructors

- Meredith Franklin: meredith.franklin@utoronto.ca
- Evelyn Pan, Sajal Bhalla

I will do the lectures on Mondays and the TAs will run the labs on Wednesdays. 
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# My Background

- In late 2021 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%)
- I am the Data Science Concentration Lead for the MScAC 
- I am on the executive committee for the U of T Data Science Institute

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# 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
- At U of T I have also taught STA255, STA465/STA2016

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# 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

<img src="data:image/png;base64,#img/research_fig.png" width="80%" style="display: block; margin: auto;" />
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# Course Goals

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

- Programming in R (Python for ML), 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

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# Quercus + Git + Piazza

Course website - lecture slides, labs, data

https://jsc370.github.io/JSC370-2025/

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

https://q.utoronto.ca/courses/383329

Piazza - questions and discussion

https://piazza.com/class/m5lck963uic6at

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# What is data science?

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

--
<img src="data:image/png;base64,#img/data-science.png" width="90%" style="display: block; margin: auto;" />

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# 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>

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# 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>

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![](data:image/png;base64,#img/demand.png){height=20%}

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.center[
![](data:image/png;base64,#img/ds2023.png)
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.center[
![](data:image/png;base64,#img/ds-degrees.png)
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# Data Scientists in Demand

Also see [here](https://www.amstat.org/news-listing/2021/10/11/new-report-highlights-growing-demand-for-data-science-analytics-talent), 
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/data-scientists-still-the-talk-of-the-town)

Things look good for DS jobs [in Toronto](https://weclouddata.com/blog/torontos-data-science-odyssey-a-tale-of-triumphs-tribulations-and-a-dash-of-uncertainty/)

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/)

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# What is this course?

This course is a introduction to the world of data science following on from where JSC270 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 we are focused mainly on R, RStudio and some Python.

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# What is R?

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

Created by statisticians for statisticians.

Over 20,000 packages added to CRAN.

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.center[
![](data:image/png;base64,#img/datascience_code.png)
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# 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)

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# R Data Science Resources

1)	R Programming for Data Science, 2022. Roger Peng. https://bookdown.org/rdpeng/rprogdatascience/

Supplementary References

2)	R for Data Science, 2023 Garrett Grolemund and Hadley Wickham. http://r4ds.hadley.nz/

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

4)	Mastering Software Development in R, 2020 Roger Peng, Sean Kross, Brooke Anderson https://bookdown.org/rdpeng/RProgDA/

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# R in the terminal
<img src="data:image/png;base64,#R-terminal.png" width="50%" style="display: block; margin: auto;" />

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# What is RStudio?

> RStudio is an integrated development environment (IDE) for R.

https://posit.co/download/rstudio-desktop/

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![](data:image/png;base64,#img/moderndive-r-vs-rstudio.png)
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# R + RStudio

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## GitHub

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

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

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

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# First Week

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

https://github.com/JSC370/JSC370-2025/

Download the Rmd files

Submit individually completed lab at the end of day Wednesday

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# Next Week

Lecture 1-3 pm Monday Jan 13 (Version control)

Lab 1-3 pm Wednesday Jan 15 (Version control)