STAT3622 Data Visualization (Lecture 1)

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# STAT3622 Data Visualization (Lecture 1)
## Introduction to Data Science
### <br>Dr. Aijun Zhang<br> The University of Hong Kong
### 21 January 2020

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# What's covered in this lecture?

- STAT3622 Course Outline

- Course Objectives
  - Tentative Contents  
  - Assessments
  - Refereces 
  
- RStudio and R Markdown

- Introduction to Data Science

- DS Job Market  
  - DS Venn Diagram  
  - DS Workflow

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# 1. STAT3622 Course Outline

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# Course Admin Information

<img src="LogoSaaSHKU.png" width="40%" style="display: block; margin: auto;" />
 
- Instructor: Dr. Aijun Zhang

- Office: RR224  
  - Email: ajzhang@hku.hk

- Lecture Hours:

- Tuesday 1:30pm - 4:20pm (T5)

- Tutor: Mr. Yifeng Guo

- Office: RR114  
  - Email: gyf9712@hku.hk

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# Course Websites

- http://stat3622.saas.hku.hk/

* Weekly updates with various course materials

- http://moodle.hku.hk/

* Sync with lecture notes/assignments/etc

* Annoucements, reminders, and surveys

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# Course Objectives

- This course (as part of data science) will focus on statistical graphics and interactive data visualization, as well as their applications in real case studies.

- **Programming:** R (primary), Python, D3.js

- **You will learn:**

- Choose the best chart that fits the data

- Communicate effectively using statistical graphics

- Create compelling visualization via programming tools

- **Prerequisites:** STAT2602 (Probability & Statistics II) or STAT3902 (Statistical Models)

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# Tentative Contentes

.pull-left[
- Introduction to Data science

- Exploratory data analysis

- Data manipulation

- Hans Rosling's Bubbles

- Interactive data visualization

- Shiny

- Web scrapping

- Dynamic documentation

- Big data visualization

]

.pull-right[
1. Statisticla Graphics  
  * R base plot
  * R:lattice
  * R:ggplot2

1. Interactive Data Visualization  
  * R:magick/animation
  * R:plotly (based on d3.js)
  * Rstudio:shiny

1. Selected Topics
  * Spatiotemporal DataViz
  * Map visualization
  * Web scrapping, etc 
]

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# Assessments

- No Final Exam!

- 40% Homeworks (2 sets) and in-class quizzes (2 sets);

- 60% Final project, consisting of

- DataViz app: 30%   
   
   - Oral presentation: 15%     
   
   - Written report: 15%

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# References

- *R for Data Science* (2017 O'Reilly) by Grolemund and Wickham. http://r4ds.had.co.nz/

- Wickham, H. (2016). *ggplot2: Elegant Graphics for Data Analysis* (2nd). 
Springer. http://ggplot2.org/book/

- Rossant, C. (2015). *Learning IPython for Interactive Computing and Data Visualization* (2nd). Packt. http://ipython-books.github.io/minibook/

- Meeks, E. (2017) *D3.js in Action* (2nd Edition, 2017 Manning)   https://www.manning.com/books/d3js-in-action-second-edition

- Yau, N. (2011). *Visualize This: The FlowingData Guide to Design, Visualization, and Statistics*. Wiley. http://book.flowingdata.com/

- *RStudio Cheat Cheets*. https://www.rstudio.com/resources/cheatsheets/

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# RStudio and R Markdown

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# RStudio IDE

- RStudio is a popular IDE (Integrated Development Environment) for R programming

- It is a powerful editor for R coding and debugging.

- It is a powerful generator for HTML, PDF, dynamic documents and slide shows.

- RStudio can be run on both Desktop and Cloud.

- Check out more nice features of RStudio at its [official website](https://www.rstudio.com/products/rstudio/features/)

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# RStudio IDE

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# R Markdown (Demonstrated)

```r
knitr::kable(head(iris), format = 'html')
```

<table>
 <thead>
  <tr>
   <th style="text-align:right;"> Sepal.Length </th>
   <th style="text-align:right;"> Sepal.Width </th>
   <th style="text-align:right;"> Petal.Length </th>
   <th style="text-align:right;"> Petal.Width </th>
   <th style="text-align:left;"> Species </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 5.1 </td>
   <td style="text-align:right;"> 3.5 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.9 </td>
   <td style="text-align:right;"> 3.0 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.7 </td>
   <td style="text-align:right;"> 3.2 </td>
   <td style="text-align:right;"> 1.3 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.6 </td>
   <td style="text-align:right;"> 3.1 </td>
   <td style="text-align:right;"> 1.5 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 5.0 </td>
   <td style="text-align:right;"> 3.6 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 5.4 </td>
   <td style="text-align:right;"> 3.9 </td>
   <td style="text-align:right;"> 1.7 </td>
   <td style="text-align:right;"> 0.4 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
</tbody>
</table>

- Dynamic documentation:  report, table, graphics ...

- R packages by Yihui Xie: knitr, bookdown, xaringan, etc

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# R Markdown (Demonstrated)

```r
plot(iris, col=iris$Species)  
```

- Data-generated graphics that are reproducible

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# R Markdown

- Click [here](https://rmarkdown.rstudio.com/lesson-1.html) to view a fantastic micro-video tutorial

- Browse [here](http://rmarkdown.rstudio.com/gallery.html) for a gallery of creative Rmarkdown works

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# Introduction to Data Science
 
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#  Data Scientist The Sexy Job

- See also an old article by NYT (2009): [For Today’s Graduate, Just One Word: Statistics](https://www.nytimes.com/2009/08/06/technology/06stats.html)

- And another famous McKinseay 2011 Report: 
[Big data: The next frontier for innovation, competition, and productivity](https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation)

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#  What is a data scientist?

- Nate Silver ([FiveThirtyEight](https://fivethirtyeight.com/contributors/nate-silver/), author of The Signal and the Noise): "Data scientist is just a sexed up word for a statistician.

- "A data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician.” (from [Joshua Blumenstock](http://www.jblumenstock.com/teaching/course=infx573))

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#  What is a data scientist?

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#  From Big Data to Applied AI

<div class="figure" style="text-align: center">
<img src="AppliedAI.png" alt="HKU New BASc Programme in Applied AI (2019 onwards)" width="70%" />
<p class="caption">HKU New BASc Programme in Applied AI (2019 onwards)</p>
</div>

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#  Data Science Venn Diagram

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#  Data Science vs. Statitics

.pull-left[
<img src="TagCloudStatistics.png" width="90%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="TagCloudDataScience.png" width="90%" style="display: block; margin: auto;" />
]

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#  Data Science Workflow

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#  Data Science Workflow

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#  Roles of Data Visualization

- Role 1: Exploratory data analysis (pre stage);

- Role 2: Visual presentation of results (after stage).

- John W. Tukey (1977; Exploratory Data Analysis):  "The greatest value of a picture is when it forces us to notice what
we never expected to see.”

.pull-left[
<img src="JohnTukey.png" width="47%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="JohnTukeyEDA.jpg" width="40%" style="display: block; margin: auto;" />
]

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## The best stats you've ever seen | Hans Rosling

Click [here](https://www.youtube.com/embed/hVimVzgtD6w) to view it on YouTube.

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# Thank you!

Q&A or Email ajzhang@umich.edu。