Essential Statistics for Non-STEM Data Analysts: Get to grips with the statistics and math knowledge needed to enter the world of data science with Py
暫譯: 非STEM數據分析師必備統計學:掌握進入數據科學世界所需的統計與數學知識,使用Python
Rongpeng Li
- 出版商: Packt Publishing
- 出版日期: 2020-11-13
- 售價: $1,840
- 貴賓價: 9.5 折 $1,748
- 語言: 英文
- 頁數: 394
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1838984844
- ISBN-13: 9781838984847
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相關分類:
機率統計學 Probability-and-statistics、Data Science
海外代購書籍(需單獨結帳)
商品描述
Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming
Key Features
- Work your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisions
- Understand how various data science algorithms function
- Build a solid foundation in statistics for data science and machine learning using Python-based examples
Book Description
Statistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks.
The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You'll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you'll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you've uncovered the working mechanism of data science algorithms, you'll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you'll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning.
By the end of this Essential Statistics for Non-STEM Data Analysts book, you'll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals.
What you will learn
- Find out how to grab and load data into an analysis environment
- Perform descriptive analysis to extract meaningful summaries from data
- Discover probability, parameter estimation, hypothesis tests, and experiment design best practices
- Get to grips with resampling and bootstrapping in Python
- Delve into statistical tests with variance analysis, time series analysis, and A/B test examples
- Understand the statistics behind popular machine learning algorithms
- Answer questions on statistics for data scientist interviews
Who this book is for
This book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you're a developer or student with a non-mathematical background, you'll find this book useful. Working knowledge of the Python programming language is required.
商品描述(中文翻譯)
**強化您從統計角度理解資料科學和資料分析的能力,以便使用 Python 程式語言從資料中提取有意義的見解**
#### 主要特點
- 在考慮統計問題的情況下,逐步了解整個資料分析流程,以做出合理的決策
- 理解各種資料科學演算法的運作方式
- 使用基於 Python 的範例建立資料科學和機器學習的統計基礎
#### 書籍描述
統計學仍然是現代分析任務的基石,幫助您解釋資料科學流程產生的結果。本書是一本詳細的指南,涵蓋了進行資料科學任務所需的數學和各種統計方法。
本書首先展示如何從統計的角度預處理資料並檢查分佈和相關性。接著,您將掌握統計分析的基本原則,並將其概念應用於現實世界的資料集。隨著進展,您將發現統計概念如何從資料科學流程的不同階段中產生,理解資料集的統計摘要,並利用這些知識為強健的資料產品(如解釋模型和預測模型)建立堅實的基礎。一旦您揭示了資料科學演算法的運作機制,您將涵蓋高效資料收集、清理、挖掘、視覺化和分析的基本概念。最後,您將在關鍵的機器學習任務中實施統計方法,例如分類、回歸、基於樹的方法和集成學習。
在這本《非 STEM 資料分析師的基本統計學》書籍結束時,您將學會如何建立和呈現一個自足的、以統計為基礎的資料產品,以滿足您的商業目標。
#### 您將學到什麼
- 瞭解如何抓取並將資料載入分析環境
- 執行描述性分析以從資料中提取有意義的摘要
- 探索機率、參數估計、假設檢定和實驗設計的最佳實踐
- 熟悉 Python 中的重抽樣和自助法
- 深入了解變異數分析、時間序列分析和 A/B 測試範例的統計檢定
- 理解流行機器學習演算法背後的統計學
- 回答資料科學家面試中的統計問題
#### 本書適合誰
本書是一本針對資料科學愛好者、資料分析師以及任何剛入門資料科學領域並希望透過簡單的解釋和範例學習基本統計概念的入門指南。如果您是具有非數學背景的開發者或學生,您會發現這本書非常有用。需要具備 Python 程式語言的工作知識。
作者簡介
Rongpeng Li is a data science instructor and a senior data scientist at Galvanize, Inc. He has previously been a research programmer at Information Sciences Institute, working on knowledge graphs and artificial intelligence. He has also been the host and organizer of the Data Analysis Workshop Designed for Non-STEM Busy Professionals at LA.
作者簡介(中文翻譯)
李榮鵬是Galvanize, Inc.的數據科學講師及資深數據科學家。他曾在資訊科學研究所擔任研究程式設計師,專注於知識圖譜和人工智慧。他也曾是洛杉磯為非STEM忙碌專業人士設計的數據分析工作坊的主持人和組織者。
目錄大綱
Table of Contents
- Fundamentals of Data Collection, Cleaning and Preprocessing
- Essential Statistics for Data Assessment
- Visualization with Statistical Graphs
- Sampling and Inferential Statistics
- Common Probability Distributions
- Parametric Estimation
- Statistical Hypothesis Testing
- Statistics for Regression
- Statistics for Classification
- Statistics for Tree-based Methods
- Statistics for Ensemble Method
- A Collection of Best Practices
- Exercises and Projects
目錄大綱(中文翻譯)
Table of Contents
- Fundamentals of Data Collection, Cleaning and Preprocessing
- Essential Statistics for Data Assessment
- Visualization with Statistical Graphs
- Sampling and Inferential Statistics
- Common Probability Distributions
- Parametric Estimation
- Statistical Hypothesis Testing
- Statistics for Regression
- Statistics for Classification
- Statistics for Tree-based Methods
- Statistics for Ensemble Method
- A Collection of Best Practices
- Exercises and Projects