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[NulledPremium.com] Statistics For Data Science
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Book details File Size: 3.80 MB Format: epub Print Length: 288 pages Page Numbers Source ISBN: 1788290674 Publisher: Packt Publishing; 1 edition (November 17, 2017) Publication Date: November 17, 2017 Sold by: Amazon Digital Services LLC Language: English ASIN: B06Y2XX2LH
Get your statistics basics right before diving into the world of data science
Key Features
No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Book Description 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.
What you will learn
Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically Style and approach Step by step comprehensive guide with real world examples
Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.
Table of Contents
Transitioning from Data Developer to Data Scientist Declaring the Objectives A Developer’s Approach to Data Cleaning Data Mining and the Database Developer Statistical Analysis for the Database Developer Database Progression to Database Regression Regularization for Database Improvement Database Development and Assessment Databases and Neural Networks Boosting your Database Database Classification using Support Vector Machines Database Structures and Machine Learning
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Statistics for Data Science by James D. Miller.epub
3.8 MB
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