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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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Understanding basic data cleaning


The importance of having clean (and therefore reliable) data in any statistical project cannot be overstated. Dirty data, even with sound statistical practice, can be unreliable and can lead to producing results that suggest courses of action that are incorrect or that may even cause harm or financial loss. It has been stated that a data scientist spends nearly 90 percent of his or her time in the process of cleaning data and only 10 percent on the actual modeling of the data and deriving results from it.

So, just what is data cleaning?

Data cleaning is also referred to as data cleansing or data scrubbing and involves both the processes of detecting as well as addressing errors, omissions, and inconsistencies within a population of data.

This may be done interactively with data wrangling tools, or in batch mode through scripting. We will use R in this book as it is well-fitted for data science since it works with even very complex datasets, allows handling...

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