College :
通識教育中心
Department :
一般通識組
Credits :
1.0
GE Field :
科學與邏輯思維
Platform :
edX
University :
Massachusetts Institute of Technology
Instructors :
Dimitris Bertsimas
Language :
英文
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Course Goals
In the last decade, the amount of data available to organizations has reached unprecedented levels.
Data is transforming business, social interactions, and the future of our society.
In this course, you will learn how to use data and analytics to give an edge to your career and your life.
We will examine real world examples of how analytics have been used to significantly improve a business or industry.
These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix.
Through these examples and many more, we will teach you the following analytics methods:
linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization.
We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge).
It is a challenging class, but it will enable you to apply analytics to realworld applications.
The class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each.
After each lecture piece, we will ask you a “quick question” to assess your understanding of the material.
There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets.
(R is a free statistical and computing software environment we’ll use in the course. See the Software FAQ below for more info).
In the middle of the class, we will run an analytics competition, and at the end of the class there will be a final exam, which will be similar to the homework assignments.
Course Syllebus
(1) An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization
(2) How to implement all of these methods in R
(3) An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software
Other Info.

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