---
title: "Course Introduction"
author: "Dr. Hua Zhou @ UCLA"
date: "Jan 7, 2020"
output:
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    toc_depth: 4  
subtitle: Biostat 203B
bibliography: ../bib-HZ.bib
csl: ../apa.csl
---
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## 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.

## 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

Four V'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).

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

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

## Course desciption

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

- Read [syllabus](https://ucla-biostat203b-2020winter.github.io/syllabus.html) and [schedule](https://ucla-biostat203b-2020winter.github.io/schedule.html) for a tentative list of topics and course logistics.

## References

