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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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Summary

In this chapter, we defined neural networks and, from a data developer's knowledge of databases and data models, grew to understand the purpose and use of neural networks and why neural networks are so important to data science. We also looked at an R-based ANN and listed some popular use case examples.

In the next chapter, we will introduce the idea of using statistical boosting to better understand data in a database.

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