Robust data analysis in R and Matlab

强大的数据分析和异常值检测在统计，数据分析，数据挖掘，机器学习，模式识别，人工智能，分类，主成分，回归，大数据以及与数据相关的任何领域中都是至关重要的。研究人员，学生，数据分析师以及大多数正在处理真实数据的人都必须意识到异常值的问题，并且他们必须知道如何处理该问题。

本课程旨在研究问题的特征，后果以及学习如何通过现有方法识别问题。如果我们只有一个随机变量（单变量数据）或如果我们有多个变量（多变量数据），我们将深入研究检测异常值的方法的性能和特性。我们将看到这些方法的理论特性，并将其应用于示例。另外，我们将看到R和Matlab软件的实际性能，并且我们将学习两个软件中存在的不同软件包，以解决异常检测问题。实施和示例代码可在开放的Google云端硬盘存储库中找到。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + .srt | Duration: 8 sections 73 lectures 9h 12m total length | Size: 3.51 GB
Learn robust data analysis with R and Matlab, the key in Machine Learning, Data Science and Statistics.

What you’ll learn

Concepts related to Robust Statistics.
Performance of outlier detection methods.
Learn to differentiate one method from another.
Identify the most robust and efficient methods that you should use in practice.
Application of the methods with handmade examples.
Application of the methods with R and Matlab.

Requirements

Basic statistical knowledge.

Description

Robust data analysis and outlier detection are crucial in Statistics, Data Analysis, Data Mining, Machine Learning, Pattern Recognition, Artificial Intelligence, Classification, Principal Components, Regression, Big Data, and any field related with data. Researchers, students, data analyst, and mostly anyone who is dealing with real data have to be aware of the problem with outliers and they have to know how to deal with this issue.

This course is intended to study the characteristics of the problem, its consequences and learn how to recognise it through the existing approaches. We will deeply study the performance and the properties of the methods to detect outliers in case we have a single random variable (univariate data) or in case we have more than one (multivariate data) . We will see the theoretical properties of the methods and we will apply them to examples. In addition, we are going to see the practical performance with the software R and Matlab, and we will learn the different existing packages in both software for the problem of outlier detection. The implementation and example codes are available in the open Google Drive repository.

You will learn about both classical and recent algorithms for outliers detection:

Univariate space:

Method SD

Z score

Tukey Boxplot

MADe

Modified Z score

Adjusted boxplot

Multivariate space:

Classical Mahalanobis distance

Robust Mahalanobis distance

MCD

Adjusted MCD

Stahel-Donoho

Kurtosis

Linear regression:

Ordinary least squares (classic method)

Robust regression: LAD, LMS, LTS

In addition, we have two sections of basic concepts that will help you to remember some notions necessary to understand the methods for outlier detection.

Basics I

Sample, population, random variable

Distribution of a random variable

Normal distribution

Fisher chi-square, t-student and F distributions

Estimators

Basics II

Linear algebra

Multivariate variable

Joint and marginal distribution

Independence, covariance and correlation

Multivariate Normal

With this course you will master one of the most important issues today both academically, as in industry and in data analysis. The examples will help you to visualize this importance and as a guide to carry out these analyzes by yourself.

Who this course is for:

Data scientist.
Data analyst.
Students.
Researchers.