Exploratory Data Analysis with MATLAB, Third Edition by Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka

By Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka

Praise for the second one Edition:
"The authors current an intuitive and easy-to-read booklet. … observed by means of many examples, proposed routines, strong references, and accomplished appendices that start up the reader surprising with MATLAB."
―Adolfo Alvarez Pinto, International Statistical Review

"Practitioners of EDA who use MATLAB will desire a reproduction of this booklet. … The authors have performed an outstanding carrier via bringing jointly such a lot of EDA exercises, yet their major accomplishment during this dynamic textual content is delivering the knowledge and instruments to do EDA.

―David A Huckaby, MAA Reviews

Exploratory info research (EDA) is a crucial a part of the information research technique. The equipment awarded during this textual content are ones that are supposed to be within the toolkit of each facts scientist. As computational sophistication has elevated and information units have grown in dimension and complexity, EDA has turn into a good extra very important method for visualizing and summarizing info sooner than making assumptions to generate hypotheses and types.

Exploratory information research with MATLAB, 3rd Edition

offers EDA equipment from a computational standpoint and makes use of a variety of examples and purposes to teach how the tools are utilized in perform. The authors use MATLAB code, pseudo-code, and set of rules descriptions to demonstrate the thoughts. The MATLAB code for examples, facts units, and the EDA Toolbox can be found for obtain at the book’s website.

New to the 3rd Edition

  • Random projections and estimating neighborhood intrinsic dimensionality
  • Deep studying autoencoders and stochastic neighbor embedding
  • Minimum spanning tree and extra cluster validity indices
  • Kernel density estimation
  • Plots for visualizing information distributions, comparable to beanplots and violin plots
  • A bankruptcy on visualizing specific data

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Extra resources for Exploratory Data Analysis with MATLAB, Third Edition (Chapman & Hall/CRC Computer Science & Data Analysis)

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It is important to understand this so that you can verify the data is being properly interpreted. For example, in a table that maintains test scores, there is a big difference between NULL and 0 when you use the AVG or MIN aggregates. If the test is excused and should not be included in the calculation, the NULL value does not have a negative impact on the accuracy of the calculation, but if the test should be averaged in, the database integrity checks should make sure that 0 is entered rather than NULL.

For example, if the following code was run on January 19, 2009, the result for the first query would be -8, representing that 8 year boundaries had been crossed between the start and end dates provided. The result for the second query would be -18 representing that 18 day boundaries had been crossed between the start and end dates. By reversing the start and end dates in each query, positive values are returned in the result set: SELECT DATEDIFF(YEAR, GETDATE(), '1/1/2001') SELECT DATEDIFF(DD, GETDATE(), '1/1/2009') More Info Date and time functions For a complete list of data types and functions that are used to work with date fields, see “Date and Time Data Types and Functions (Transact-SQL),” in SQL Server Books Online.

2. In the existing query window, type and execute the following command to display the product ID and name for all active products that have not had any sales. Products with a list price of $0 are not included because they are not currently available for sale. ProductID ORDER BY ProductID; 3. ProductID ORDER BY ProductID; 4. Because of the data integrity checks in this database, the SELECT command in step 3 returns the same result set as a SELECT DISTINCT command run against the ­SalesOrderDetail table because every product that is sold is included in the Product table.

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