原标题：诚征译者 | Statistics and Data Visualization with Python
清华大学出版社近期拟引进 Taylor & Francis Group今年出版的一本 利用Python实现统计和数据可视化类英文著作，该著作旨在为有兴趣在数据科学与分析以及一般统计分析领域的学生和从业人员提供统计学方面的桥梁。 我社计划翻译成中文出版，以应广大读者的需求，现诚征译者。请阅读下面有关的著作信息和对译者的要求， 若有兴趣且符合条件的，可扫描文末二维码，填报“译者申请”，包括个人信息和试译内容。
注明：已获得博士学位、中青年高校教师及工程科技人员优先，不可委托他人代翻译。
著作信息
【著作名称】
暂译名：统计和数据可视化——使用Python语言实现
原书名：Statistics and Data Visualization with Python
【本书信息】
Publication date: 31 January 2023
Publisher: Taylor & Francis Group
Pages: 554
原文语种：英语
【本书作者】
[英] 赫苏斯·罗格尔-萨拉查（Jesús Rogel-Salazar）
Dr Jesús Rogel-Salazaris a lead data scientist working for companies such as Tympa Health Technologies, AKQA, IBM Data Science Studio, Dow Jones, Barclays, to name a few. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter.
He has held a position as Associate Professor in mathematics, as well as a consultant and data scientist in a variety of industries including science, finance, marketing, people analytics and health, among others. He is the author of Data Science and Analytics with Python and Advanced Data Science and Analytics with Python, as well as Essential MATLAB® and Octave, published by CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism, finance and health tech.
【本书概要】
This book is intended to serve as a bridge in statistics for graduates and business ractitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python.
Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.
【本书目录】
1 Data, Stats and Stories – An Introduction 1
1.1 From Small to Big Data
1.2 Numbers, Facts and Stats
1.3 A Sampled History of Statistics
1.4 Statistics Today
1.5 Asking Questions and Getting Answers
1.6 Presenting Answers Visually
2 Python Programming Primer 33
2.1 Talking to Python
2.1.1 ing and Interacting
2.1.2 Jupyter Notebook
2.2 Starting Up with Python
2.2.1 Types in Python
2.2.2 Numbers: Integers and Floats
2.2.3 Strings
2.2.4 Complex Numbers
2.3 Collections in Python
2.3.1 Lists
2.3.2 List Comprehension
2.3.3 Tuples
2.3.4 Dictionaries
2.3.5 Sets
2.4 The Beginning of Wisdom: Logic & Control Flow
2.4.1 Booleans and Logical Operators
2.4.2 Conditional Statements
2.4.3 While Loop
2.4.4 For Loop
2.5 Functions
2.6 s and Modules
3 Snakes, Bears & Other Numerical Beasts: NumPy, SciPy & pandas 99
3.1 Numerical Python– NumPy
3.1.1 Matrices and Vectors
3.1.2 N-Dimensional Arrays
3.1.3 N-Dimensional Matrices
3.1.4 Indexing and Slicing
3.1.5 Deive Statistics
3.2 Scientific Python– SciPy
3.2.1 Matrix Algebra
3.2.2 Numerical Integration
3.2.3 Numerical Optimisation
3.2.4 Statistics
3.3 Panel Data = pandas
3.3.1 Series and Dataframes
3.3.2 Data Exploration with pandas
3.3.3 Pandas Data Types
3.3.4 Data Manipulation with pandas
3.3.5 Loading Data to pandas
3.3.6 Data Grouping
4 The Measure of All Things– Statistics
4.1 Deive Statistics
4.2 Measures of Central Tendency and Dispersion
4.3 Central Tendency
4.3.1 Mode
4.3.2 Median
4.3.3 Arithmetic Mean
4.3.4 Geometric Mean
4.3.5 Harmonic Mean
4.4 Dispersion
4.4.1 Setting the Boundaries: Range
4.4.2 Splitting One’ s Sides: Quantiles, Quartiles, Percentiles and More
4.4.3 Mean Deviation
4.4.4 Variance and Standard Deviation
4.5 Data Deion – Deive Statistics Revisited
5 Definitely Maybe: Probability and Distributions 179
5.1 Probability
5.2 Random Variables and Probability Distributions
5.2.1 Random Variables
5.2.2 Discrete and Continuous Distributions
5.2.3 Expected Value and Variance
5.3 Discrete Probability Distributions
5.3.1 Uniform Distribution
5.3.2 Bernoulli Distribution
5.3.3 Binomial Distribution
5.3.4 Hypergeometric Distribution
5.3.5 Poisson Distribution
5.4 Continuous Probability Distributions
5.4.1 Normal or Gaussian Distribution
5.4.2 Standard Normal Distribution Z
5.4.3 Shape and Moments of a Distribution
5.4.4 The Central Limit Theorem
5.5 Hypothesis and Confidence Intervals
5.5.1 Student’ s t Distribution
5.5.2 Chi-squared Distribution
6 Alluring Arguments and Ugly Facts – Statistical Modelling and Hypothesis Testing 267
6.1 Hypothesis Testing
6.1.1 Tales and Tails: One- and Two-Tailed Tests
6.2 Normality Testing
6.2.1 Q-Q Plot
6.2.2 Shapiro-Wilk Test
6.2.3 D’ Agostino K-squared Test
6.2.4 Kolmogorov-Smirnov Test
6.3 Chi-square Test
6.3.1 Goodness of Fit
6.3.2 Independence
6.4 Linear Correlation and Regression
6.4.1 Pearson Correlation
6.4.2 Linear Regression
6.4.3 Spearman Correlation
6.5 Hypothesis Testing with One Sample
6.5.1 One-Sample t-test for the Population Mean
6.5.2 One-Sample z-test for Proportions
6.5.3 Wilcoxon Signed Rank with One-Sample
6.6 Hypothesis Testing with Two Samples
6.6.1 Two-Sample t-test – Comparing Means, Same Variances
6.6.2 Levene’ s Test – Testing Homoscedasticity
6.6.3 Welch’ s t-test – Comparing Means, Different Variances
6.6.4 Mann-Whitney Test – Testing Non-normal Samples
6.6.5 Paired Sample t-test
6.6.6 Wilcoxon Matched Pairs
6.7 Analysis of Variance
6.7.1 One-factor or One-way ANOVA
6.7.2 Tukey’ s Range Test
6.7.3 Repeated Measures ANOVA
6.7.4 Kruskal-Wallis – Non-parametric One-way ANOVA
6.7.5 Two-factor or Two-way ANOVA
6.8 Tests as Linear Models
6.8.1 Pearson and Spearman Correlations
6.8.2 One-sample t- and Wilcoxon Signed Rank Tests
6.8.3 Two-Sample t- and Mann-Whitney Tests
6.8.4 Paired Sample t- and Wilcoxon Matched Pairs Tests
6.8.5 One-way ANOVA and Kruskal-Wallis Test
7 Delightful Details – Data Visualisation 383
7.1 Presenting Statistical Quantities
7.1.1 Textual Presentation
7.1.2 Tabular Presentation
7.1.3 Graphical Presentation
7.2 Can You Draw Me a Picture?– Data Visualisation
7.3 Design and Visual Representation
7.4 Plotting and Visualising: Matplotlib
7.4.1 Keep It Simple: Plotting Functions
7.4.2 Line Styles and Colours
7.4.3 Titles and Labels
7.4.4 Grids
7.5 Multiple Plots
7.6 Subplots
7.7 Plotting Surfaces
7.8 Data Visualisation– Best Practices
8 Dazzling Data Designs– Creating Charts 417
8.1 What Is the Right Visualisaton for Me?
8.2 Data Visualisation and Python
8.2.1 Data Visualisation with Pandas
8.2.2 Seaborn
8.2.3 Bokeh
8.2.4 Plotly
8.3 Scatter Plot
8.4 Line Chart
8.5 Bar Chart
8.6 Pie Chart
8.7 Histogram
8.8 Box Plot
8.9 Area Chart
8.10 Heatmap
A Variance: Population v Sample
B Sum of First n Integers
C Sum of Squares of the First n Integers
D The Binomial Coefficient
E The Hypergeometric Distribution
F The Poisson Distribution
G The Normal Distribution
H Skewness and Kurtosis
I Kruskal-Wallis Test– No Ties
Bibliography 501
Index 511
【试译内容】
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译者要求
1. 具有统计/数据可视化/Python的相关学术背景和课程教学经验；
2. 翻译态度认真，追求品质，保障有6个月的翻译工作时间；
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特别说明
1.试译截止日期为2023年6月1日；
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