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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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Data developer thinking

Having spent plenty of years wearing the hat of a data developer, it makes sense to start out here with a few quick comments about data developers.

In some circles, a database developer is the equivalent of a data developer. But whether data or database, both would usually be labeled as an information technology (IT) professional. Both spend their time working on or with data and database technologies.

We may see a split between those databases (data) developers that focus more on support and routine maintenance (such as administrators) and those who focus more on improving, expanding, and otherwise developing access to data (such as developers).

Your typical data developer will primarily be involved with creating and maintaining access to data rather than consuming that data. He or she will have input in or may make decisions on, choosing programming languages for accessing or manipulating data. We will make sure that new data projects adhere to rules on how databases store and handle data, and we will create interfaces between data sources.

In addition, some data developers are involved with reviewing and tuning queries written by others and, therefore, must be proficient in the latest tuning techniques, various query languages such as Structured Query Language (SQL), as well as how the data being accessed is stored and structured.

In summary, at least strictly from a data developer's perspective, the focus is all about access to valuable data resources rather than the consumption of those valuable data resources.

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