The Analytics Edge [edX] Summer 2015 (319 files)

2 Linear Regression/2_10.mp4 | 124.20MB |

6 Clustering/6_1.mp4 | 4.89MB |

6 Clustering/6_22.mp4 | 8.47MB |

6 Clustering/6_11.mp4 | 14.41MB |

6 Clustering/6_19.mp4 | 12.76MB |

6 Clustering/6_6.mp4 | 7.51MB |

6 Clustering/6_3.mp4 | 11.42MB |

6 Clustering/6_13.mp4 | 9.14MB |

6 Clustering/6_14.mp4 | 3.30MB |

6 Clustering/6_20.mp4 | 17.35MB |

6 Clustering/6_17.mp4 | 27.29MB |

6 Clustering/6_15.mp4 | 3.28MB |

6 Clustering/6_7.mp4 | 38.15MB |

6 Clustering/6_16.mp4 | 8.45MB |

6 Clustering/6_12.mp4 | 12.56MB |

6 Clustering/6_5.mp4 | 13.22MB |

6 Clustering/6_10.mp4 | 6.22MB |

6 Clustering/6_21.mp4 | 32.58MB |

6 Clustering/6_8.mp4 | 30.15MB |

6 Clustering/6_18.mp4 | 29.66MB |

6 Clustering/6_4.mp4 | 7.94MB |

6 Clustering/6_9.mp4 | 8.22MB |

3 Logistic Regression/3_5.mp4 | 43.44MB |

3 Logistic Regression/3_8.mp4 | 19.54MB |

3 Logistic Regression/3_13.mp4 | 6.47MB |

3 Logistic Regression/3_16.mp4 | 2.89MB |

3 Logistic Regression/3_10.mp4 | 10.98MB |

3 Logistic Regression/3_4.mp4 | 9.03MB |

3 Logistic Regression/3_15.mp4 | 10.89MB |

3 Logistic Regression/3_11.mp4 | 9.68MB |

3 Logistic Regression/3_2.mp4 | 8.83MB |

3 Logistic Regression/3_18.mp4 | 23.56MB |

3 Logistic Regression/3_1.mp4 | 4.69MB |

3 Logistic Regression/3_9.mp4 | 6.55MB |

3 Logistic Regression/3_12.mp4 | 33.19MB |

3 Logistic Regression/3_17.mp4 | 11.32MB |

3 Logistic Regression/3_7.mp4 | 21.93MB |

3 Logistic Regression/3_3.mp4 | 8.44MB |

3 Logistic Regression/3_19.mp4 | 19.00MB |

3 Logistic Regression/3_6.mp4 | 21.00MB |

3 Logistic Regression/3_14.mp4 | 4.37MB |

3 Logistic Regression/3_21.mp4 | 12.54MB |

3 Logistic Regression/3_20.mp4 | 27.93MB |

8 Linear Optimization/8_22.mp4 | 43.29MB |

8 Linear Optimization/8_20.mp4 | 11.17MB |

8 Linear Optimization/8_18.mp4 | 11.81MB |

8 Linear Optimization/8_12.mp4 | 34.85MB |

8 Linear Optimization/8_4.mp4 | 8.45MB |

8 Linear Optimization/8_1.mp4 | 4.25MB |

**Type:**Course

**Tags:**machine learning, analytics, MIT, R

**Bibtex:**

@article{, title= {The Analytics Edge [edX] Summer 2015}, journal= {}, author= {}, year= {}, url= {https://www.edx.org/course/the-analytics-edge-0}, abstract= {The Analytics Edge Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. About this course Skip Course Description 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 real-world 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). At the end of the class there will be a final exam, which will be similar to the homework assignments. What you'll learn An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization How to implement all of these methods in R An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software Prerequisites Basic mathematical knowledge (at a high school level). You should be familiar with concepts like mean, standard deviation, and scatterplots. Mathematical maturity and prior experience with programming will decrease the estimated effort required for the class, but are not necessary to succeed. https://www.edx.org/course/the-analytics-edge-0}, keywords= {Machine Learning, Analytics, MIT, R}, terms= {}, license= {}, superseded= {} }

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