Statistics for Data Science: Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

James D. Miller

商品描述

Get your statistics basics right before diving into the world of data science

About This Book

  • 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

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.

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

In Detail

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.

Style and approach

Step by step comprehensive guide with real world examples

商品描述(中文翻譯)

在潛入資料科學世界之前,先確立你的統計基礎

關於本書
- 無需取得統計學位,閱讀本書,為資料科學和實際應用建立堅實的統計基礎
- 在資料清理、挖掘和分析等資料科學任務中實施統計學
- 透過 R 程式幫助,學習概率、統計、數值計算等知識

本書適合對進入資料科學領域有興趣的開發人員,他們希望透過深入的程式和簡單的解釋獲得統計學的簡明資訊。具備基本的 R 操作經驗將會有所幫助。

你將學到什麼
- 分析從資料開發人員到資料科學家思維的轉變
- 熟悉 R 程式和統計計算的邏輯
- 理解變異、標準差、概率、矩陣計算等數學概念
- 學習在資料清理、挖掘和分析等資料科學任務中實施統計學
- 學習執行線性回歸、正規化、模型評估、提升、支持向量機和神經網絡等任務所需的統計技術
- 熟悉以程式方式進行資料科學的各種統計計算

詳細內容
資料科學是一個不斷發展的領域,以指數級增長的速度受到歡迎。資料科學包括從統計學、計算機科學,尤其是機器學習、數據庫、數據可視化等領域提取的技術和理論。

本書將帶領你從一無所知到在資料科學任務中使用各種統計方法時感到自在的整個統計學之旅。它從簡單的統計學開始,然後轉向在資料科學算法中使用的統計方法。清晰解釋了用於統計計算的 R 程式和邏輯。你將接觸到各種數學概念,如變異、標準差、概率、矩陣計算等。你將學習實施統計學在資料清理、挖掘和分析等資料科學任務中所需的技術。你將學習執行線性回歸、正規化、模型評估、提升、支持向量機和神經網絡等任務所需的統計技術。

通過閱讀本書,你將能夠熟練地以程式方式進行各種資料科學的統計計算。

風格和方法
- 逐步全面的指南,附有實際案例