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9月1日上的课现在才记得复习与做笔记，罪过啊罪过。 Data Minihg 的课我们CV组也上，算是稍微弥补了对没有选择DM的遗憾。

推荐一本书 《Introduce to machine learning》. 3rd,作者是Ethen Alpaydin。

What is Data analytics?

Data Analytics is the analysis of large databases to find novel, commercially valuable and exploitable patterns.

Aim: dicover meanningful insights and knowledge from data.

Data mining = process of building models

Models

• Captures the essence of the discoverrd knowledge
• Can assistant in understanding the world
• Can be used to make predictions

Problem & motibation for DA

We have always collected, checked and organized data.

举了Wal-Mart的例子

How does DA fit into business?

Understand the business context, and stronger, framing a business question

Translating the business question into a data analytics queston

Collecting, understanding and processing data from across the business and possibly externally

Build models and evaluate them

Deploying the result in the business to deliver benefits

iterative process.

Home work 1

Each group, get onto the Internet and find at least one success story for data mining and data analytics. Then one member of the group gives a short presentation next week.

Answer

• Baseball analytics (e.g. money ball movie)
• Weather Prediction (We were doing this for a long time)
• Nate Silver - After Silver successfully called the outcomes in 49 of the 50 states in the 2008 U.S. Presidential election, in 2009 Silver was named one of The World’s 100 Most Influential People by Time.
• Siri

Home work 2

Assume a telecommunications company like Telstra or Optus. Data is collected by the business.

1. In your group, discuss and make a list of the kind of data you think could likely be collected.
2. Write down an ER diagram for the data and a set of database tables.
3. How could this data be used to better understand the business processes?
4. How could this data be used to improve the business processes?
5. (Harder) Can you label these as supervised or unsupervised problems?

Answer

1. Personal information,such as name, phone number, gender. location, Frequent contact person, context about buy something, where you go