Скачать или смотреть Lecture 4-1: Data Types, Data Organization, and Data Modality - Data Modeling and Regression

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Lecture 4-1: Data Types, Data Organization, and Data Modality - Data Modeling and Regression Analysis in Business

The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical such as win-don’t win an auction. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior.


There has been a tremendous increase in the way data generation via sensors, digital platforms, user-generated content, etc. are being used in the industry. For example, sensors continuously record data and store it for analysis at a later point. In the way data gets captured, there can be a lot of redundancy. With more variables, comes more trouble! There may be very little (or no) incremental information gained from these sources. This is the problem of a high number of unwanted dimensions. To avoid this pitfall, data transformation and dimension reduction comes to the rescue by examining and extracting fewer dimensions while ensuring that it conveys the full information concisely.
Lecture 4-1: Data Types, Data Organization, and Data Modality - Data Modeling and Regression Analysis in Business
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