<commit_before>BI TECH CP303 - Data Mining
===========================

* **Instructor:** <NAME>
* **Email:** <<EMAIL>>  
* **Course Location:** Puget Sound Plaza in room 407 ([map](http://www.pce.uw.edu/maps.aspx?id=88&terms))
* **Course Time:** Mondays 6:00 - 9:00 PM
* **Dates:** April 6, 2015 through June 15, 2015

> Welcome to data mining! This is an applied course meant to teach you practical tools for data mining and knowledge discovery. The course is composed of four units: regression, classification, unsupervised learning and time series analysis. The goal is to provide experience in a breadth of applications and to prepare you for the job of an analyst, data scientist, or any role that calls for data mining. If you already have experience with R or data mining, there are additional readings and challenges in the projects to challenge you and elevate your skills.

## Course Grade

Grading is based on classroom participation, completion of homeworks and projects, and attendance. Students are required to attend 80% of the lectures to receive a passing grade.

## Assignments

#### Projects

There are a total of four projects, one for each topic area. For each project, you will receive a business problem and a corresponding data set. You're free to use any methods you like, so long as you support your choices. You will write a brief report of your analyses and provide/receive feedback from your classmates.

#### Critiques

When you turn in your project reports, you will receive the reports of three of your classmates. During the following week, read their reports and provide thoughts and feedback. Please write at least a paragraph discussing parts of the analyses you liked and disliked.  While you're reading, try to put yourself into the mind of the business stakeholder and ask if your requests were adequately met. Are you confident in the conclusions drawn? Were the figures and supporting evidence compelling? Remember to maintain a tone of mutual respect and read the section on policies and values for more information. 

#### Due Dates

| Assignment |   Date   |
|-----------|----------|
| Project 1 | April 27 | 
| Project 1 Critiques | May 4 | 
| Project 2 | May 11   | 
| Project 2 Critiques | May 18 | 
| Project 3 | June 1   | 
| Project 3 Critiques | June June 8 | 
| Project 4 | June 15  |

## Textbook

There is no required textbook for this course. Everything you need to succeed is available in the course repository.

## Policies and values

A large component of this course is providing critical feedback on the analyses of your peers. It is imperative that all students are thoughtful when providing written feedback and participating in class. This means using respectful language in discussions and writings, but also being respectful of class time by arriving prepared and engaged.

Everyone is required to do original work for all projects. You're free to openly discuss the projects and your approaches, just like you would in a professional setting, but reports should be your own.

## Topics 

Week | Date | Topic | Dataset
-------------- | ------------ | ------------- | -------------
1 | April 6 | Introduction to Data mining and programming with R. | Capital Bikeshare
2 | April 13 | Linear regression | Capital BikeShare 
3 | April 20 | Linear regression extensions | Capital BikeShare <commit_msg>add some project links to syllabus<commit_after>BI TECH CP303 - Data Mining
===========================

* **Instructor:** <NAME>
* **Email:** <<EMAIL>>  
* **Course Location:** Puget Sound Plaza in room 407 ([map](http://www.pce.uw.edu/maps.aspx?id=88&terms))
* **Course Time:** Mondays 6:00 - 9:00 PM
* **Dates:** April 6, 2015 through June 15, 2015

> Welcome to data mining! This is an applied course meant to teach you practical tools for data mining and knowledge discovery. The course is composed of four units: regression, classification, unsupervised learning and time series analysis. The goal is to provide experience in a breadth of applications and to prepare you for the job of an analyst, data scientist, or any role that calls for data mining. If you already have experience with R or data mining, there are additional readings and challenges in the projects to challenge you and elevate your skills.

## Course Grade

Grading is based on classroom participation, completion of homeworks and projects, and attendance. Students are required to attend 80% of the lectures to receive a passing grade.

## Assignments

#### [Projects](https://github.com/erinshellman/BI-TECH-CP303/tree/master/projects)

There are a total of four projects, one for each topic area. For each project, you will receive a business problem and a corresponding data set. You're free to use any methods you like, so long as you support your choices. You will write a brief report of your analyses and provide/receive feedback from your classmates.

#### Critiques

When you turn in your project reports, you will receive the reports of three of your classmates. During the following week, read their reports and provide thoughts and feedback. Please write at least a paragraph discussing parts of the analyses you liked and disliked.  While you're reading, try to put yourself into the mind of the business stakeholder and ask if your requests were adequately met. Are you confident in the conclusions drawn? Were the figures and supporting evidence compelling? Remember to maintain a tone of mutual respect and read the section on policies and values for more information. 

#### Due Dates

| Assignment |   Date   |
|-----------|----------|
| [Project 1](https://github.com/erinshellman/BI-TECH-CP303/blob/master/projects/project%201/problem_statement_project_1.md) | April 27 | 
| Project 1 Critiques | May 4 | 
| Project 2 | May 11   | 
| Project 2 Critiques | May 18 | 
| Project 3 | June 1   | 
| Project 3 Critiques | June June 8 | 
| Project 4 | June 15  |

## Textbook

There is no required textbook for this course. Everything you need to succeed is available in the course repository.

## Policies and values

A large component of this course is providing critical feedback on the analyses of your peers. It is imperative that all students are thoughtful when providing written feedback and participating in class. This means using respectful language in discussions and writings, but also being respectful of class time by arriving prepared and engaged.

Everyone is required to do original work for all projects. You're free to openly discuss the projects and your approaches, just like you would in a professional setting, but reports should be your own.

## Topics 

Week | Date | Topic | Dataset
-------------- | ------------ | ------------- | -------------
1 | April 6 | Introduction to Data mining and programming with R. | Capital Bikeshare
2 | April 13 | Linear regression | Capital BikeShare 
3 | April 20 | Linear regression extensions | Capital BikeShare 
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