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دانلود کتاب Statistical Application Development with R and Python

دانلود کتاب توسعه برنامه های آماری با R و Python

مشخصات کتاب

Statistical Application Development with R and Python

ویرایش: 2nd 
نویسندگان: Prabhanjan Narayanachar Tattar  
سری:  
ISBN (شابک) : 9781788621199 
ناشر: Packt 
سال نشر: 2017 
تعداد صفحات: 414 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 7 مگابایت

قیمت کتاب (تومان) : 46,000

میانگین امتیاز به این کتاب :
تعداد امتیاز دهندگان : 12

در صورت تبدیل فایل کتاب Statistical Application Development with R and Python به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.

توجه داشته باشید کتاب توسعه برنامه های آماری با R و Python نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.

توضیحاتی در مورد کتاب توسعه برنامه های آماری با R و Python

پیاده‌سازی نرم‌افزار نشان‌داده‌شده با R و Python درباره این کتاب* ماهیت داده‌ها را از طریق نرم‌افزاری که مفاهیم اولیه را بلافاصله با استفاده از R و Python می‌گیرد بیاموزید.* مدل‌سازی و تجسم داده‌ها را برای انجام تجزیه و تحلیل آماری کارآمد با این راهنما درک کنید.* با تکنیک‌ها به خوبی آشنا شوید. مانند رگرسیون، خوشه‌بندی، طبقه‌بندی، ماشین‌های بردار پشتیبان و موارد دیگر برای یادگیری اصول آمار مدرن. پس این کتاب همان چیزی است که شما نیاز دارید. هیچ دانش قبلی لازم نیست. دانشمند مشتاق داده، کاربران R در تلاش برای یادگیری پایتون و بالعکس چه خواهید آموخت* ماهیت داده ها را از طریق نرم افزار با مفاهیم اولیه بلافاصله در R بیاموزید* داده ها را از منابع مختلف بخوانید و خروجی R را به نرم افزارهای دیگر صادر کنید* انجام تجسم موثر داده ها با ماهیت متغیرها و گزینه‌های جایگزین غنی * تجزیه و تحلیل داده‌های اکتشافی را برای درک اولیه مفید و ایجاد نگرش صحیح نسبت به استنتاج مؤثر انجام دهید * استنتاج آماری را از طریق شبیه‌سازی با ترکیب استنتاج کلاسیک و قدرت محاسباتی مدرن بیاموزید. در مدل‌های رگرسیونی مانند خطی و لجستیک برای رگرسیون های پیوسته و گسسته برای تشکیل مبانی آمار مدرن* خود را با CART معرفی کنید - ابزار یادگیری ماشینی که زمانی بسیار مفید است که داده ها دارای غیرخطی بودن ذاتی باشند. تحلیل آماری جزئی شامل جمع آوری و بررسی داده ها برای توصیف ماهیت داده ها نیاز به تحلیل دارد این کتاب به شما کمک می‌کند تا رابطه داده‌ها را کشف کنید و مدل‌هایی بسازید تا تصمیم‌های بهتری بگیرید. این کتاب مفاهیم آماری را همراه با R و Python بررسی می‌کند، که به خوبی از کلمه go یکپارچه شده‌اند. تقریباً هر مفهومی دارای یک کد R است که قدرت R و برنامه‌های کاربردی را نشان می‌دهد. کد R و برنامه ها با برنامه های مشابه پایتون تقویت شده اند. بنابراین، ابتدا ویژگی های داده ها، آمار توصیفی و نگرش اکتشافی را درک خواهید کرد که به شما پایه محکمی برای تجزیه و تحلیل داده ها می دهد. استنتاج آماری پایه فنی روش های آماری را تکمیل می کند. رگرسیون، مدل‌سازی خطی، لجستیک و CART، جعبه ابزار ضروری را می‌سازد. این به شما کمک می کند تا مشکلات پیچیده را در دنیای واقعی کامل کنید. شما با درک مختصری از ماهیت داده ها شروع می کنید و با مدل های آماری مدرن و پیشرفته مانند CART پایان می دهید. هر مرحله با DATA و کد R برداشته می‌شود و توسط Python بهبود می‌یابد. سفر تجزیه و تحلیل داده‌ها با تجزیه و تحلیل اکتشافی آغاز می‌شود که بیش از خلاصه داده‌های ساده و توصیفی است. سپس مدل‌سازی رگرسیون خطی را اعمال می‌کنید و با رگرسیون لجستیک، CART و آمار فضایی پایان می‌دهید. در پایان این کتاب می‌توانید یادگیری آماری خود را در حوزه‌های اصلی در محل کار یا پروژه‌های خود به کار ببرید. سبک و رویکرد توسعه بهتر و بهتر روش های هوشمندتر برای تجزیه و تحلیل داده ها تصمیم گیری بهتر/پیش بینی های آینده نحوه کاوش، تجسم و انجام تجزیه و تحلیل آماری را بیاموزید. روش های آماری و محاسباتی بهتر و کارآمدتر. برای تسلط بر یادگیری خود مثال های عملی را اجرا کنید

توضیحاتی درمورد کتاب به خارجی

Software Implementation Illustrated with R and PythonAbout This Book* Learn the nature of data through software which takes the preliminary concepts right away using R and Python.* Understand data modeling and visualization to perform efficient statistical analysis with this guide.* Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.Who This Book Is ForIf you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versaWhat You Will Learn* Learn the nature of data through software with preliminary concepts right away in R* Read data from various sources and export the R output to other software* Perform effective data visualization with the nature of variables and rich alternative options* Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference* Learn statistical inference through simulation combining the classical inference and modern computational power* Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics* Introduce yourself to CART - a machine learning tool which is very useful when the data has an intrinsic nonlinearityIn DetailStatistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.Style and approachDeveloping better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

فهرست مطالب

Cover
Copyright
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Table of Contents
Preface
Chapter 1: Data Characteristics
	Questionnaire and its components
		Understanding the data characteristics in an R environment
	Experiments with uncertainty in computer science
	Installing and setting up R
	Using R packages
		RSADBE – the books R package
	Python installation and setup
		Using pip for packages
	IDEs for R and Python
	The companion code bundle
	Discrete distributions
		Discrete uniform distribution
		Binomial distribution
		Hypergeometric distribution
		Negative binomial distribution
		Poisson distribution
	Continuous distributions
		Uniform distribution
		Exponential distribution
		Normal distribution
	Summary
Chapter 2: Import/Export Data
	Packages and settings – R and Python
	Understanding data.frame and other formats
		Constants, vectors, and matrices
			Time for action – understanding constants, vectors, and basic arithmetic
			What just happened?
			Doing it in Python
			Time for action – matrix computations
			What just happened?
			Doing it in Python
		The list object
			Time for action – creating a list object
			What just happened?
		The data.frame object
			Time for action – creating a data.frame object
			What just happened?
			Have a go hero
		The table object
			Time for action – creating the Titanic dataset as a table object
			What just happened?
			Have a go hero
	Using utils and the foreign packages
		Time for action – importing data from external files
			What just happened?
			Doing it in Python
		Importing data from MySQL
			Doing it in Python
	Exporting data/graphs
		Exporting R objects
		Exporting graphs
			Time for action – exporting a graph
			What just happened?
		Managing R sessions
			Time for action – session management
			What just happened?
			Doing it in Python
	Pop quiz
	Summary
Chapter 3: Data Visualization
	Packages and settings – R and Python
	Visualization techniques for categorical data
		Bar chart
			Going through the built-in examples of R
			Time for action – bar charts in R
			What just happened?
			Doing it in Python
			Have a go hero
		Dot chart
			Time for action – dot charts in R
			What just happened?
			Doing it in Python
		Spine and mosaic plots
			Time for action – spine plot for the shift and operator data
			What just happened?
			Time for action – mosaic plot for the Titanic dataset
			What just happened?
		Pie chart and the fourfold plot
	Visualization techniques for continuous variable data
		Boxplot
			Time for action – using the boxplot
			What just happened?
			Doing it in Python
		Histogram
			Time for action – understanding the effectiveness of histograms
			What just happened?
			Doing it in Python
			Have a go hero
		Scatter plot
			Time for action – plot and pairs R functions
			What just happened?
			Doing it in Python
			Have a go hero
		Pareto chart
		A brief peek at ggplot2
			Time for action – qplot
			What just happened?
			Time for action – ggplot
			What just happened?
			Pop quiz
	Summary
Chapter 4: Exploratory Analysis
	Packages and settings – R and Python
	Essential summary statistics
		Percentiles, quantiles, and median
		Hinges
		Interquartile range
			Time for action – the essential summary statistics for The Wall dataset
			What just happened?
	Techniques for exploratory analysis
		The stem-and-leaf plot
			Time for action – the stem function in play
			What just happened?
		Letter values
		Data re-expression
			Have a go hero
		Bagplot – a bivariate boxplot
			Time for action – the bagplot display for multivariate datasets
			What just happened?
		Resistant line
			Time for action – resistant line as a first regression model
			What just happened?
		Smoothing data
			Time for action – smoothening the cow temperature data
			What just happened?
		Median polish
			Time for action – the median polish algorithm
			What just happened?
			Have a go hero
	Summary
Chapter 5: Statistical Inference
	Packages and settings – R and Python
	Maximum likelihood estimator
		Visualizing the likelihood function
			Time for action – visualizing the likelihood function
			What just happened?
			Doing it in Python
		Finding the maximum likelihood estimator
		Using the fitdistr function
			Time for action – finding the MLE using mle and fitdistr functions
			What just happened?
	Confidence intervals
		Time for action – confidence intervals
			What just happened?
			Doing it in Python
	Hypothesis testing
		Binomial test
			Time for action – testing probability of success
			What just happened?
		Tests of proportions and the chi-square test
			Time for action – testing proportions
			What just happened?
		Tests based on normal distribution – one sample
			Time for action – testing one-sample hypotheses
			What just happened?
			Have a go hero
		Tests based on normal distribution – two sample
			Time for action – testing two-sample hypotheses
			What just happened?
			Have a go hero
			Doing it in Python
	Summary
Chapter 6: Linear Regression Analysis
	Packages and settings - R and Python
	The essence of regression
	The simple linear regression model
		What happens to the arbitrary choice of parameters?
			Time for action - the arbitrary choice of parameters
			What just happened?
		Building a simple linear regression model
			Time for action - building a simple linear regression model
			What just happened?
			Have a go hero
		ANOVA and the confidence intervals
			Time for action - ANOVA and the confidence intervals
			What just happened?
		Model validation
			Time for action - residual plots for model validation
			What just happened?
			Doing it in Python
			Have a go hero
	Multiple linear regression model
		Averaging k simple linear regression models or a multiple linear regression model
			Time for action - averaging k simple linear regression models
			What just happened?
		Building a multiple linear regression model
			Time for action - building a multiple linear regression model
			What just happened?
		The ANOVA and confidence intervals for the multiple linear regression model
			Time for action - the ANOVA and confidence intervals for the multiple linear regression model
			What just happened?
			Have a go hero
		Useful residual plots
			Time for action - residual plots for the multiple linear regression model
			What just happened?
	Regression diagnostics
		Leverage points
		Influential points
		DFFITS and DFBETAS
		The multicollinearity problem
			Time for action - addressing the multicollinearity problem for the gasoline data
			What just happened?
			Doing it in Python
	Model selection
		Stepwise procedures
			The backward elimination
			The forward selection
			The stepwise regression
		Criterion-based procedures
			Time for action - model selection using the backward, forward, and AIC criteria
			What just happened?
			Have a go hero
	Summary
Chapter 7: Logistic Regression Model
	Packages and settings – R and Python
		The binary regression problem
			Time for action – limitation of linear regression model
			What just happened?
		Probit regression model
			Time for action – understanding the constants
			What just happened?
			Doing it in Python
		Logistic regression model
			Time for action – fitting the logistic regression model
			What just happened?
			Doing it in Python
		Hosmer-Lemeshow goodness-of-fit test statistic
			Time for action – Hosmer-Lemeshow goodness-of-fit statistic
			What just happened?
	Model validation and diagnostics
		Residual plots for the GLM
			Time for action – residual plots for logistic regression model
			What just happened?
			Doing it in Python
			Have a go hero
		Influence and leverage for the GLM
			Time for action – diagnostics for the logistic regression
			What just happened?
			Have a go hero
		Receiving operator curves
			Time for action – ROC construction
			What just happened?
			Doing it in Python
		Logistic regression for the German credit screening dataset
			Time for action – logistic regression for the German credit dataset
			What just happened?
			Doing it in Python
			Have a go hero
	Summary
Chapter 8: Regression Models with Regularization
	Packages and settings – R and Python
		The overfitting problem
			Time for action – understanding overfitting
			What just happened?
			Doing it in Python
			Have a go hero
	Regression spline
		Basis functions
		Piecewise linear regression model
			Time for action – fitting piecewise linear regression models
			What just happened?
		Natural cubic splines and the general B-splines
			Time for action – fitting the spline regression models
			What just happened?
	Ridge regression for linear models
		Protecting against overfitting
			Time for action – ridge regression for the linear regression model
			What just happened?
			Doing it in Python
		Ridge regression for logistic regression models
			Time for action – ridge regression for the logistic regression model
			What just happened?
		Another look at model assessment
			Time for action – selecting  iteratively and other topics
			What just happened?
			Pop quiz
	Summary
Chapter 9: Classification and Regression Trees
	Packages and settings – R and Python
		Understanding recursive partitions
			Time for action – partitioning the display plot
			What just happened?
	Splitting the data
		The first tree
			Time for action – building our first tree
			What just happened?
		Constructing a regression tree
			Time for action – the construction of a regression tree
			What just happened?
		Constructing a classification tree
			Time for action – the construction of a classification tree
			What just happened?
			Doing it in Python
		Classification tree for the German credit data
			Time for action – the construction of a classification tree
			What just happened?
			Doing it in Python
			Have a go hero
		Pruning and other finer aspects of a tree
			Time for action – pruning a classification tree
			What just happened?
			Pop quiz
	Summary
Chapter 10: CART and Beyond
	Packages and settings – R and Python
		Improving the CART
			Time for action – cross-validation predictions
			What just happened?
	Understanding bagging
		The bootstrap
			Time for action – understanding the bootstrap technique
			What just happened?
		How the bagging algorithm works
			Time for action – the bagging algorithm
			What just happened?
			Doing it in Python
		Random forests
			Time for action – random forests for the German credit data
			What just happened?
			Doing it in Python
		The consolidation
			Time for action – random forests for the low birth weight data
			What just happened?
	Summary
Index