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Statistics for Data Science

Statistics for Data Science

By : James D. Miller
3.6 (5)
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Statistics for Data Science

Statistics for Data Science

3.6 (5)
By: James D. Miller

Overview of this book

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
Table of Contents (13 chapters)
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R and statistical assessment


So, let's get started with some statistical assessment work!

As we discussed in the previous section, instead of using all the data (the entire population of observations) to train a statistical model (and then test using some of that data), cross-validation divides the data into training and testing datasets.

The first step that a data scientist needs to take when he or she is interested in using cross-validation to assess the performance of a statistical model is to organize (or split) the data into two separate subsets.

There are actually several approaches of cross-validation:

  • Leave-one-out cross-validation (LOOCV)
  • Holdout
  • k-fold and repeated k-fold
  • Re-substitution (most agree that this method is the simplest method)

This cross-validation approaches all focus on how to split the data for the training, testing, and validation. Each has its own merit (pros and cons).

There are (as always) many approaches to programming a problem. The following is one such simple method...

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