---
title: "Course Introduction"
subtitle: "Biostat 203B"
author: "Dr. Hua Zhou @ UCLA"
date: "`r format(Sys.time(), '%d %B, %Y')`"
format:
  html:
    theme: cosmo
    number-sections: true
    toc: true
    toc-depth: 4
    toc-location: left
    code-fold: false
bibliography: "../bib-HZ.bib"
csl: "../apa.csl"
---

## Statistics and data science

- Statistics, the science of *data analysis*, is the applied mathematics in the 21st century.

- Data is increasing in [volume, velocity, and variety](http://www.forbes.com/sites/oreillymedia/2012/01/19/volume-velocity-variety-what-you-need-to-know-about-big-data/).

- My favorite definition of a *data scientist*:

> A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.

``` {shortcodes=false}
{{< tweet user="josh_wills" id="198093512149958656" >}}
```

## Big data in 1990s

@Huber94HugeData; @Huber96MassiveData

| Data Size | Bytes          | Storage Mode               |
|-----------|----------------|----------------------------|
| tiny      | $10^2$         | piece of paper             |
| small     | $10^4$         | a few pieces of paper      |
| medium    | $10^6$ (MB)    | a floppy disk              |
| large     | $10^8$         | hard disk                  |
| huge      | $10^9$ (GB)    | hard disk(s)               |
| massive   | $10^{12}$ (TB) | hard disk(s); RAID storage |

## Big data in 21st centry

4V's of big data:

<p align="center">
<img src="./4-Vs-of-big-data.jpg" height="450" width="700"/>
</p>

Source: [IBM](http://www.ibmbigdatahub.com/infographic/four-vs-big-data).

## Who are hiring data scientists?

Following tables are based on a survey of 403 students who earned a master's degree in statistics, biostatistics, or a related field (actuarial science, data science, informatics, math with stats focus) during the 2019--2020 academic year.

<p align="center">
<img src="./ms-employment-sector.png" width="500"/>
</p>

Source: [AmStat News (2021 Nov)](https://magazine.amstat.org/wp-content/uploads/2021/11/AmstatNewsNov2021-updated.pdf).

> there were more than 109 unique---although similar---job titles. The most common were data scientist (20), biostatistician (18), data analyst (9), biostatistician I (7), and statistician (5).

## A typical data scientist on [LinkedIn](http://linkedin.com)

A position posted by Genetech.

<p align="center">
<img src="./genetech.png" height="450" width="700"/>
</p>

## Course description

- This course introduces some computing skills and software tools for handling potentially big public health data in a reproducible way.

- This is _not_ a machine learning course. 

- Read [syllabus](https://ucla-biostat-203b.github.io/2023winter/syllabus/syllabus.html) and [schedule](https://ucla-biostat-203b.github.io/2023winter/schedule/schedule.html) for a tentative list of topics and course logistics.

## Why R?

<p align="center">
<img src="./ms-employment-software.png" width="500"/>
</p>

If time permits, I'll add some Python code in the lectures.

## What I expect from you

- You are curious and are excited about "figuring stuff out".

- You are proficient in coding and debugging (or are ready to work to get there).

- You are willing to ask questions. 

## What you can expect from me

- I value your learning experience and process. 

- I'm flexible with respect to the topics we cover. 

- I'm happy to share my professional connections.

- I'll try my best to be responsive in class, in office hours, and on Slack.

## More (free) UCLA resources for learning data science

-   IDRE workshops: <https://idre.ucla.edu/calendar>

-   QCBio workshops: <https://qcb.ucla.edu/collaboratory/workshops/>

## References