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1. Advanced R statistical programming and data models : analysis, machine learning, and visualization [2019]
 Wiley, Matt, author.
 [California, CA] : Apress, [2019] New York : Distributed to the book trade worldwide by Springer Science+Business Media New York, [2019]
 Description
 Book — 1 online resource (1 volume) : illustrations
 Czado, Claudia, author.
 Cham, Switzerland : Springer, [2019]
 Description
 Book — xxix, 242 pages : illustrations (some color) ; 24 cm.
 Summary

 Multivariate Distributions and Copulas
 Univariate Distributions
 Multivariate Distributions
 Features of Multivariate Data
 The Concept of a Copula and Sklarʼs Theorem
 Elliptical Copulas
 Empirical Copula Approximation
 Invariance Properties of Copulas
 Meta Distributions
 Bivariate Conditional Distributions Expressed in Terms of Their Copulas
 Exercises
 Dependence Measures
 Pearson ProductMoment Correlation
 Kendallʼs T and Spearmanʼs Ps
 Tail Dependence
 Partial and Conditional Correlations
 Exercises
 Bivariate Copula Classes, Their Visualization, and Estimation
 Construction of Bivariate Copula Classes
 Bivariate Elliptical Copulas
 Archimedean Copulas
 Bivariate ExtremeValue Copulas
 Relationship Between Copula Parameters and Kendallʼs T
 Rotated and Reflected Copulas
 Relationship Between Copula Parameters and Tail Dependence Coefficients
 Exploratory Visualization
 Simulation of Bivariate Copula Data
 Parameter Estimation in Bivariate Copula Models
 Conditional Bivariate Copulas
 Average Conditional and Partial Bivariate Copulas
 Exercises
 Pair Copula Decompositions and Constructions
 Illustration in Three Dimensions
 PairCopula Constructions of Drawable Dvine and Canonical Cvine Distributions
 Conditional Distribution Functions Associated with Multivariate Distributions
 Exercises
 Regular Vines
 Necessary Graph Theoretic Background
 Regular Vine Tree Sequences
 Regular Vine Distributions and Copulas
 Simplified Regular Vine Classes
 Representing Regular Vines Using Regular Vine Matrices
 Exercises
 Simulating Regular Vine Copulas and Distributions
 Simulating Observations from Multivariate Distributions
 Simulating from Pair Copula Constructions
 Simulating from Cvine Copulas
 Simulating from Dvine Copulas
 Simulating from Regular Vine Copulas
 Exercises
 Parameter Estimation in Simplified Regular Vine Copulas
 Likelihood of Simplified Regular Vine Copulas
 Sequential and Maximum Likelihood Estimation in Simplified Regular Vine Copulas
 Asymptotic Theory of Parametric Regular Vine Copula Estimators
 Exercises
 Selection of Regular Vine Copula Models
 Selection of a Parametric Copula Family for Each Pair Copula Term and Estimation of the Corresponding Parameters for a Given Vine Tree Structure
 Selection and Estimation of all Three Model Components of a Vine Copula
 The Dissmann Algorithm for Sequential TopDown Selection of Vine Copulas
 Exercises
 Comparing Regular Vine Copula Models
 Akaike and Bayesian Information Criteria for Regular Vine Copulas
 KullbackLeibler Criterion
 Vuong Test for Comparing Different Regular Vine Copula Models
 Correction Factors in the Vuong Test for Adjusting for Model Complexity
 Exercises
 Case Study : Dependence Among German DAX Stocks
 Data Description and Sector Groupings
 Marginal Models
 Finding Representatives of Sectors
 Dependence Structure Among Representatives
 Model Comparison
 Some Interpretive Remarks
 Recent Developments in Vine Copula Based Modeling
 Advances in Estimation
 Advances in Model Selection of Vine Copula Based Models
 Advances for Special Data Structures
 Applications of Vine Copulas in Financial Econometrics
 Applications of Vine Copulas in the Life Sciences
 Application of Vine Copulas in Insurance
 Application of Vine Copulas in the Earth Sciences
 Application of Vine Copulas in Engineering
 Software for Vine Copula Modeling
 References
 Index.
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA273.6 .C93 2019  Unavailable On order Request 
3. Analyzing dependent data with vine copulas [electronic resource] : a practical guide with R [2019]
 Czado, Claudia.
 Cham : Springer, c2019.
 Description
 Book — 1 online resource.
 Summary

 Preface. Multivariate Distributions and Copulas. Dependence Measures. Bivariate Copula Classes, Their Visualization and Estimation. Pair Copula Decompositions and Constructions. Regular Vines. Simulating Regular Vine Copulas and Distributions. Parameter Estimation in Regular Vine Copulas. Selection of Regular Vine Copula Models. Comparing Regular Vine Copula Models. Case Study: Dependence Among German DAX Stocks. Recent Developments in Vine Copula Based Modeling. Indices.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Dalla Villa, Chris, onscreen presenter.
 [Place of publication not identified] : Packt Publishing, 2019.
 Description
 Video — 1 online resource (1 streaming video file (2 hr., 17 min., 17 sec.)) : digital, sound, color
 Summary

"Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you'll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques. You'll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you've grasped the basics, you'll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You'll also get to grips with plotting trends, correlations, and statistical summaries. By the end of this course, you'll have created data visualizations that will impress your clients."Resource description page.
5. Applied machine learning with R [2019]
 [Place of publication not identified] : Packt Publishing, 2019.
 Description
 Video — 1 online resource (1 streaming video file (5 hr., 1 min., 48 sec.)) : digital, sound, color
 Summary

"Machine learning is here and it is changing the way businesses work! From the Netflix recommendation engine to Google's selfdriving car, it's all machine learning. Machine learning explores the development and use of algorithms that can gain from data. ML Algorithms provide the ability to learn at an accelerated pace as more and more datasets are available for training. It is very similar to how the human mind learns. In this course, you will also learn about machine learning and deep learning and will see how R can be used as a tool (to show output) and also in your ML projects. The course also covers packages that implement machine learning with TensorFlow and H2O. TensorFlow is a Python package that is implemented in R as well. The course also covers artificial neural networks. Here you get to learn how to create our own neural networks and implement them in R. Last but not least, the sixth module is Decision Tree and Text mining, a well know pattern involved in data science, again a new concept in machine learning. All the modules throw light on how machine learning implementation is easy and simple using R. So what are you waiting for? Begin your epic journey to being an awesome ML programmer with this applied R course."Resource description page.
6. Applied unsupervised learning with R [2019]
 Malik, Alok, author.
 Birmingham, UK : Packt Publishing, 2019.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Table of Contents Introduction to Clustering Methods Advanced Clustering Methods Probability Distributions Dimension Reduction Data Comparison Methods Anomaly Detection.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
7. Data analytics with R [2019]
 Khan, Hassan, speaker.
 [Place of publication not identified] : Technics Publications, 2019.
 Description
 Video — 1 online resource (1 streaming video file (4 hr., 39 min., 22 sec.)) : digital, sound, color
 Summary

"Master data analytics using R with this video series containing ten clips: Setting up the R Environment; Importing data into R; Exporting data from R; Creating labels in R; Reshaping data in R; Appending Data in R; Creating Loops in R – Part 1; Creating Loops in R – Part 2; Creating Loops in R – Part 3; Merging Data in R."Resource description page.
8. Data analytics with R Shiny [2019]
 Khan, Hassan, speaker.
 [Place of publication not identified] : [Technics Publications], [2019]
 Description
 Video — 1 online resource (1 streaming video file (3 hr., 2 min., 24 sec.)) : digital, sound, color
 Summary

"Master how to perform data analytics and build dashboards using R Shiny in this sixpart video series: Introducing R Shiny; Creating an R Shiny Dashboard; Entering and Displaying Output in R Shiny; Drop Down Menus and Radio Buttons in R Shiny; Sliders in R Shiny; Animated Charts in R Shiny."Resource description page.
9. Data science using Python and R [2019]
 Larose, Chantal D., author.
 Hoboken, NJ : John Wiley & Sons, Inc, 2019.
 Description
 Book — 1 online resource (xvii, 238 pages).
 Summary

 Preface xi
 About the Authors xv
 Acknowledgements xvii
 Chapter 1 Introduction to Data Science
 1
 1.1 Why Data Science?
 1
 1.2 What is Data Science?
 1
 1.3 The Data Science Methodology
 2
 1.4 Data Science Tasks
 5
 1.4.1 Description
 6
 1.4.2 Estimation
 6
 1.4.3 Classification
 6
 1.4.4 Clustering
 7
 1.4.5 Prediction
 7
 1.4.6 Association
 7
 Exercises
 8
 Chapter 2 The Basics of Python and R
 9
 2.1 Downloading Python
 9
 2.2 Basics of Coding in Python
 9
 2.2.1 Using Comments in Python
 9
 2.2.2 Executing Commands in Python
 10
 2.2.3 Importing Packages in Python
 11
 2.2.4 Getting Data into Python
 12
 2.2.5 Saving Output in Python
 13
 2.2.6 Accessing Records and Variables in Python
 14
 2.2.7 Setting Up Graphics in Python
 15
 2.3 Downloading R and RStudio
 17
 2.4 Basics of Coding in R
 19
 2.4.1 Using Comments in R
 19
 2.4.2 Executing Commands in R
 20
 2.4.3 Importing Packages in R
 20
 2.4.4 Getting Data into R
 21
 2.4.5 Saving Output in R
 23
 2.4.6 Accessing Records and Variables in R
 24
 References
 26
 Exercises
 26
 Chapter 3 Data Preparation
 29
 3.1 The Bank Marketing Data Set
 29
 3.2 The Problem Understanding Phase
 29
 3.2.1 Clearly Enunciate the Project Objectives
 29
 3.2.2 Translate These Objectives into a Data Science Problem
 30
 3.3 Data Preparation Phase
 31
 3.4 Adding an Index Field
 31
 3.4.1 How to Add an Index Field Using Python
 31
 3.4.2 How to Add an Index Field Using R
 32
 3.5 Changing Misleading Field Values
 33
 3.5.1 How to Change Misleading Field Values Using Python
 34
 3.5.2 How to Change Misleading Field Values Using R
 34
 3.6 Reexpression of Categorical Data as Numeric
 36
 3.6.1 How to Reexpress Categorical Field Values Using Python
 36
 3.6.2 How to Reexpress Categorical Field Values Using R
 38
 3.7 Standardizing the Numeric Fields
 39
 3.7.1 How to Standardize Numeric Fields Using Python
 40
 3.7.2 How to Standardize Numeric Fields Using R
 40
 3.8 Identifying Outliers
 40
 3.8.1 How to Identify Outliers Using Python
 41
 3.8.2 How to Identify Outliers Using R
 42
 References
 43
 Exercises
 44
 Chapter 4 Exploratory Data Analysis
 47
 4.1 EDA Versus HT
 47
 4.2 Bar Graphs with Response Overlay
 47
 4.2.1 How to Construct a Bar Graph with Overlay Using Python
 49
 4.2.2 How to Construct a Bar Graph with Overlay Using R
 50
 4.3 Contingency Tables
 51
 4.3.1 How to Construct Contingency Tables Using Python
 52
 4.3.2 How to Construct Contingency Tables Using R
 53
 4.4 Histograms with Response Overlay
 53
 4.4.1 How to Construct Histograms with Overlay Using Python
 55
 4.4.2 How to Construct Histograms with Overlay Using R
 58
 4.5 Binning Based on Predictive Value
 58
 4.5.1 How to Perform Binning Based on Predictive Value Using Python
 59
 4.5.2 How to Perform Binning Based on Predictive Value Using R
 62
 References
 63
 Exercises
 63
 Chapter 5 Preparing to Model the Data
 69
 5.1 The Story So Far
 69
 5.2 Partitioning the Data
 69
 5.2.1 How to Partition the Data in Python
 70
 5.2.2 How to Partition the Data in R
 71
 5.3 Validating your Partition
 72
 5.4 Balancing the Training Data Set
 73
 5.4.1 How to Balance the Training Data Set in Python
 74
 5.4.2 How to Balance the Training Data Set in R
 75
 5.5 Establishing Baseline Model Performance
 77
 References
 78
 Exercises
 78
 Chapter 6 Decision Trees
 81
 6.1 Introduction to Decision Trees
 81
 6.2 Classification and Regression Trees
 83
 6.2.1 How to Build CART Decision Trees Using Python
 84
 6.2.2 How to Build CART Decision Trees Using R
 86
 6.3 The C5.0 Algorithm for Building Decision Trees
 88
 6.3.1 How to Build C5.0 Decision Trees Using Python
 89
 6.3.2 How to Build C5.0 Decision Trees Using R
 90
 6.4 Random Forests
 91
 6.4.1 How to Build Random Forests in Python
 92
 6.4.2 How to Build Random Forests in R
 92
 References
 93
 Exercises
 93
 Chapter 7 Model Evaluation
 97
 7.1 Introduction to Model Evaluation
 97
 7.2 Classification Evaluation Measures
 97
 7.3 Sensitivity and Specificity
 99
 7.4 Precision, Recall, and Fss Scores
 99
 7.5 Method for Model Evaluation
 100
 7.6 An Application of Model Evaluation
 100
 7.6.1 How to Perform Model Evaluation Using R
 103
 7.7 Accounting for Unequal Error Costs
 104
 7.7.1 Accounting for Unequal Error Costs Using R
 105
 7.8 Comparing Models with and without Unequal Error Costs
 106
 7.9 Data?Driven Error Costs
 107
 Exercises
 109
 Chapter 8 Naive Bayes Classification
 113
 8.1 Introduction to Naive Bayes
 113
 8.2 Bayes Theorem
 113
 8.3 Maximum a Posteriori Hypothesis
 114
 8.4 Class Conditional Independence
 114
 8.5 Application of Naive Bayes Classification
 115
 8.5.1 Naive Bayes in Python
 121
 8.5.2 Naive Bayes in R
 123
 References
 125
 Exercises
 126
 Chapter 9 Neural Networks
 129
 9.1 Introduction to Neural Networks
 129
 9.2 The Neural Network Structure
 129
 9.3 Connection Weights and the Combination Function
 131
 9.4 The Sigmoid Activation Function
 133
 9.5 Backpropagation
 134
 9.6 An Application of a Neural Network Model
 134
 9.7 Interpreting the Weights in a Neural Network Model
 136
 9.8 How to Use Neural Networks in R
 137
 References
 138
 Exercises
 138
 Chapter 10 Clustering
 141
 10.1 What is Clustering?
 141
 10.2 Introduction to the K?Means Clustering Algorithm
 142
 10.3 An Application of K?Means Clustering
 143
 10.4 Cluster Validation
 144
 10.5 How to Perform K?Means Clustering Using Python
 145
 10.6 How to Perform K?Means Clustering Using R
 147
 Exercises
 149
 Chapter 11 Regression Modeling
 151
 11.1 The Estimation Task
 151
 11.2 Descriptive Regression Modeling
 151
 11.3 An Application of Multiple Regression Modeling
 152
 11.4 How to Perform Multiple Regression Modeling Using Python
 154
 11.5 How to Perform Multiple Regression Modeling Using R
 156
 11.6 Model Evaluation for Estimation
 157
 11.6.1 How to Perform Estimation Model Evaluation Using Python
 159
 11.6.2 How to Perform Estimation Model Evaluation Using R
 160
 11.7 Stepwise Regression
 161
 11.7.1 How to Perform Stepwise Regression Using R
 162
 11.8 Baseline Models for Regression
 162
 References
 163
 Exercises
 164
 Chapter 12 Dimension Reduction
 167
 12.1 The Need for Dimension Reduction
 167
 12.2 Multicollinearity
 168
 12.3 Identifying Multicollinearity Using Variance Inflation Factors
 171
 12.3.1 How to Identify Multicollinearity Using Python
 172
 12.3.2 How to Identify Multicollinearity in R
 173
 12.4 Principal Components Analysis
 175
 12.5 An Application of Principal Components Analysis
 175
 12.6 How Many Components Should We Extract?
 176
 12.6.1 The Eigenvalue Criterion
 176
 12.6.2 The Proportion of Variance Explained Criterion
 177
 12.7 Performing Pca with K =
 4
 178
 12.8 Validation of the Principal Components
 178
 12.9 How to Perform Principal Components Analysis Using Python
 179
 12.10 How to Perform Principal Components Analysis Using R
 181
 12.11 When is Multicollinearity Not a Problem?
 183
 References
 184
 Exercises
 184
 Chapter 13 Generalized Linear Models
 187
 13.1 An Overview of General Linear Models
 187
 13.2 Linear Regression as a General Linear Model
 188
 13.3 Logistic Regression as a General Linear Model
 188
 13.4 An Application of Logistic Regression Modeling
 189
 13.4.1 How to Perform Logistic Regression Using Python
 190
 13.4.2 How to Perform Logistic Regression Using R
 191
 13.5 Poisson Regression
 192
 13.6 An Application of Poisson Regression Modeling
 192
 13.6.1 How to Perform Poisson Regression Using Python
 193
 13.6.2 How to Perform Poisson Regression Using R
 194
 Reference
 195
 Exercises
 195
 Chapter 14 Association Rules
 199
 14.1 Introduction to Association Rules
 199
 14.2 A Simple Example of Association Rule Mining
 200
 14.3 Support, Confidence, and Lift
 200
 14.4 Mining Association Rules
 202
 14.4.1 How to Mine Association Rules Using R
 203
 14.5 Confirming Our Metrics
 207
 14.6 The Confidence Difference Criterion
 208
 14.6.1 How to Apply the Confidence Difference Criterion Using R
 208
 14.7 The Confidence Quotient Criterion
 209
 14.7.1 How to Apply the Confidence Quotient Criterion Using R
 210
 References
 211
 Exercises
 211
 Appendix Data Summarization and Visualization
 215
 Part 1: Summarization
 1: Building Blocks of Data Analysis
 215
 Part 2: Visualization: Graphs and Tables for Summarizing and Organizing Data
 217
 Part 3: Summarization
 2: Measures of Center, Variability, and Position
 222
 Part 4: Summarization and Visualization of Bivariate Elationships
 225
 Index 231.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Healy, Kieran Joseph, 1973 author.
 Princeton ; Oxford : Princeton University Press, [2019]
 Description
 Book — xviii, 272 pages : illustrations (some color) ; 26 cm
 Summary

 Preface
 Look at data
 Get started
 Make a plot
 Show the right numbers
 Graph tables, add labels, make notes
 Work with models
 Draw maps
 Refine your plots.
(source: Nielsen Book Data)
 Online
11. Easy statistics for food science with R [2019]
 Alkarkhi, Abbas F. M., author.
 London, United Kingdom : Academic Press, an imprint of Elsevier, [2019].
 Description
 Book — 1 online resource.
12. Handson data analytics with R [2019]
 Tiwari, Rahul, speaker.
 [Place of publication not identified] : Packt, [2019]
 Description
 Video — 1 online resource (1 streaming video file (2 hr., 15 min., 50 sec.)) : digital, sound, color
 Summary

"This course will expand your understanding of statistics so you can create analytic models in R. Highlevel data science techniques will be presented to you in a practical manner, to help you bridge the gap between the questions you wish to answer, the data used for analysis, and how to create some of the classic models used in data analytics. You will start off by understanding dimensionality reduction and data mining in R and learning how to simplify complex datasets and unearth patterns from data. Moving on, you will understand hypothesis testing and pvalues. You will also demonstrate onesample and twosample tests and the benefits they provide as very sophisticated analytical techniques. You will understand how data can give you predictive insights into the future and will conclude by presenting data in a way that will allow you to answer questions with datadriven confidence."Resource description page.
13. Handson data exploration with R [2019]
 Tiwari, Rahul, speaker.
 [Place of publication not identified] : Packt, [2019]
 Description
 Video — 1 online resource (1 streaming video file (2 hr., 9 min., 4 sec.)) : digital, sound, color
 Summary

"R can help you work with data you already have. You can do this by learning some common R data commands, exploring your data, aggregating the data into summary information, and visualizing the results to share with others. But before that, data cleaning is a very important aspect. Here we will talk about using tidyr to create tidy data. This course will teach you how to put R to practical use in a world where decisions are datadriven. We start off by understanding how to prepare your data for analysis. You will learn how to organize data in a way that is easily workable. We will then explore data and understand how easy it is to gain insights from it by summarizing, aggregating, and visualizing data in R. By the end of this course, you will be equipped with the skills you need to explore a Retail, Telecom, or any other dataset handed to you, break down its key feature into easily digestible information, summarize this information, and produce visually appealing plots to demonstrate these insights."Resource description page.
 Hwang, Yoon Hyup, author.
 Birmingham, UK : Packt Publishing, 2019.
 Description
 Book — 1 online resource : illustrations
 Summary

 Table of Contents Data Science and Marketing Key Performance Indicators and Visualizations Drivers behind Marketing Engagement From Engagement to Conversion Product Analytics Recommending the Right Products Exploratory Analysis for Customer Behavior Predicting the Likelihood of Marketing Engagement Customer Lifetime Value DataDriven Customer Segmentation Retaining Customers A/B Testing for Better Marketing Strategy What's Next?
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Wang, Jane, speaker.
 [Place of publication not identified] : Packt Publishing, 2019.
 Description
 Video — 1 online resource (1 streaming video file (2 hr., 11 min., 3 sec.)) : digital, sound, color
 Summary

"This course introduces you to the full workflow, ranging from acquiring data, data wrangling, and analysis to outputting and publishing visualization products. We touch on a variety of datasets (including remotesensing data and techniques) and incorporate machine learning in QGIS analytical steps. We further investigate geospatial analysis using the most uptodate R packages, such as ggplot2, raster, sf, Leaflet, and Shiny. By the end of the course, you will be able to produce interactive maps and professional cartographic products, deploy them as a Shiny application, and critique a variety of endresults."Resource description page.
 Bellini, Tiziano, author.
 London, United Kingdom : Academic Press, [2019]
 Description
 Book — 1 online resource.
 Summary

IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.
17. Introduction to R [2019]
 [Place of publication not identified] : Stone River eLearning, 2019.
 Description
 Video — 1 online resource (1 streaming video file (5 hr., 14 min., 39 sec.)) : digital, sound, color
 Summary

"The Introduction to R is a primary course for aspiring data scientists who are currently working with Microsoft Excel, Matlab, Mathematica or SAS for numerical analysis of large sets of data. The course enables the candidates in using more powerful OpenSource environments especially the R programming language. R is a functional programming environment employed by many data analysts and data scientists, easily accessible to nonprogrammers and naturally extending a skill set that is common to data analysts and data scientists. It's the perfect tool for when the one has a statistical, numerical, or probabilitiesbased problem based on real data and they've pushed those tools past their limits. This fundamental course covers all the necessary topics required to kick start the candidates in working with R programming language. This introductory course provides a complete coverage of the fundamentals of R programming language, including its uses, benefits and applications. The students who enroll in this course will move one step closer to become accomplished data scientists. The course covers the umbrella of technologies that are on the leading edge of data science development focused on R and related tools."Resource description page.
 Brunsdon, Chris, author.
 Second edition.  London ; Thousand Oaks, California : SAGE Publications, 2019.
 Description
 Book — ix, 325 pages : illustrations (some color), maps (color) ; 25 cm.
 Summary

 Chapter 1 Introduction
 Chapter 2 Data and Plots
 Chapter 3 Handling Spatial Data
 Chapter 4 Programming in R
 Chapter 5 Using R as a GIS
 Chapter 6 Point Pattern Analysis
 Chapter 7 Spatial Attribute Analysis
 Chapter 8 Localised Spatial Analysis
 Chapter 9 R and Internet Data
 Chapter 10 Epilogue.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276.45 .R3 B78 2019  Unknown 
 HUI, ERIC GOH MING HUI.
 [S.l.] : APRESS, 2019.
 Description
 Book — 1 online resource
 Summary

 Chapter 1: Introduction Chapter Goal: To understand what is R, why use R, statistics in data mining and data scienceNo of pages 15Sub Topics1. What is R?2. High Level and Low Level Language3. What is Statistics?4. What is Data Science?5. What is Data Mining?6. What is Text Mining?7. Three Types of Analytics8. Big Data9. Why R?10. Conclusion
 Chapter 2: Getting StartedChapter Goal: To set up the computer for R ProgrammingNo of pages: 15Sub  Topics
 1. What is R and RStudio?2. Installation of R and RStudio3. Integrated Development Environment4. RStudio  The IDE for R.
 5. Conclusion
 Chapter 3: Basic SyntaxChapter Goal: To learn R programming basicsNo of pages : 30Sub  Topics:
 1. Writing in R Console2. Using Code Editor3. Variables and Data Types4. Vectors5. Lists6. Data Frame7. Logical Statements8. Loops9. Functions10. Conclusion
 Chapter 4: Descriptive StatisticsChapter Goal: To learn Descriptive Statistics in RNo of pages: 20Sub  Topics:
 1. Reading Data Files2. Mean, Median, Min, Max, ...3. Percentile, Standard Deviations4. The Summary() and Str() functions5. Distributions6. Conclusion
 Chapter 5: Data VisualizationsChapter Goal: To learn Data Visualizations in R No of pages: 20Sub  Topics:
 1. What is Data Visualizations?2. Bar Chart, Histogram3. Line Chart, Pie Chart4. Scatterplot and Box Plot5. Scatterplot Matrix6. Decision Trees7. Conclusion
 Chapter 6: Inferential Statistics and RegressionsChapter Goal: To learn inferential statistics and regressions in RNo of pages: 20Sub  Topics:
 1. Correlations2. T Test, Chi Square, ANOVA3. Non Parametric Test4. Linear Regressions5. Multiple Linear Regressions.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Ramasubramanian, Karthik.
 Second edition.  New York : Apress, 2019.
 Description
 Book — 1 online resource.
 Summary

 Chapter 1: Introduction to Machine LearningChapter Goal: This chapter walks through the What, Why, Where and How kind of questions, generally asked by many beginners in Machine Learning. The answers will set the momentum and direction for the chapters to follow. No of pages: 25Sub Topics1. What does a Machine really learn?2. Why is Machine Learning so popular?3. Where do we use Machine Learning?4. How is Machine Learning changing our way of life?5. Machine Learning Tools and Software6. Machine Learning using R
 Chapter 2: Data Exploration and PreparationChapter Goal: The basis for building a good Machine Learning model is to have a clear understanding and well preparedness of data. This chapter will explain ways to explore the data for understanding and how to deal with the inconsistencies present in the data. No of pages: 50Sub  Topics1. Various Data Formats2. Summary Statistics3. Missing Values4. Data Imputation5. Transforming Unstructured Data
 Chapter 3: Sampling and Resampling TechniquesChapter Goal: In many realworld dataset, the biggest challenge is the sheer volume of the data. This volume makes the computational limitations more evident for building the Machine Learning Models. In order to reduce the need for computational power and at the same time not compromising the efficacy of the model, this chapter explains some sampling techniques for selecting a smaller dataset from the bigger dataset. We will also explore the idea of resampling which increases the accuracy of many Machine Learning Models.No of pages: 50Sub  Topics:
 1. Simple Random Sampling2. Systematic Sampling3. Stratified Sampling4. Cluster Sampling5. Bootstrap sampling
 Chapter 4: Visualization of DataChapter Goal: Visualization is a powerful tool to see through things in our data which might not be very evident when a manual exploration is carried out. This chapter will explain some of the commonly used plots and diagrams to see visually appealing insights coming out from our data. No of pages: 50Sub  Topics:
 1. Scatterplot, Histogram and Box Plot2. Heat maps and Waterfall Charts3. Dendrogram for Clustering4. Bubble Chart and Word Cloud5. Sankey Diagrams6. Time Series Graphs7. Cohort Diagram
 Chapter 5: Feature Engineering Chapter Goal: One more challenge in the real world dataset is the number of features it contains. There might be hundreds of feature in a dataset but not all of it is useful for building our model. So, in order to select the features which explain our dataset more than the other features, and hence give a more accurate result, we have certain well proven technique derived from statistics. The feature engineering has now become an unavoidable step in our Machine Learning Model building process.No of pages: 40Sub  Topics:1. Feature Ranking2. Variable Subset Selection
 3. Dimensionality Reduction
 Chapter 6: Machine Learning Models: Theory and PracticeChapter Goal: This
 chapter is the core of this book. After we had the fair understanding of our data and performed the feature engineering, it's now time to build some really powerful Machine Learning Models. This chapter lists all the ML algorithms under one header. A clear demarcation will be drawn for explaining how each of these ML algorithms are different from each other and which algorithm suits the given usecases.No of pages: 150Sub  Topics:
 1. Linear, Logistic and Polynomial Regression Models2. Decision Tree3. Clustering Algorithms4. Text Mining Approaches5. Neural Networks6. Support Vector Machine7. Association Rule Mining8. Deep Learning9. Online Machine Learning Algorithm
 Chapter 7: Machine Learning Model EvaluationChapter Goal: At all times, our job doesn't just end with building a Machine Learning Model but it further goes in evaluating the model's efficacy. A model is considered the best only when it crosses the benchmark accuracy and performs better than the existing models. The significance of evaluating the model increases, even more, when we want to set a common ground of comparing many different models coming out from a research and experimental project.No of pages: 45Sub  Topics:
 1. kfold Cross Validation2. Bootstrap sampling3. ROC Curve4. Accuracy, Precision and Recall5. Sensitivity and Specificity
 Chapter 8: Model Performance ImprovementChapter Goal: Once we have performed our evaluations, its time to think on how to further improve the model accuracy. And experiences show that, in many cases, we get a significant improvement over accuracy from our base models when we apply methods like Boosting and Ensemble models. This chapter will take a detailed discussion on these methods.No of pages: 60Sub  Topics:1. Parameter Tuning2. Ensemble based ML Model3. Bagging Technique4. Boosting Methods
 Chapter 9: Time Series ModellingChapter Goal: So far, we have explored the entire ML process flow in good depth along with studying numerous algorithms and approaches. However, in order place this book in a unique fusion of contemporary and legacy techniques from Statistics, Machine Learning and Computer Science, this chapter will touch upon a powerful statistical modeling technique called Time Series. It has its applications in demandsupply planning, stockmarket predictions, weather forecast and many other numerous places where one can establish the dependency of a variable with respect to time. Time series models identify the trend, seasonality and random component in the variable of your interest and thus capturing the pattern emerging out from the data from the past to take decision for the future.No of pages: 40Sub  Topics:1. White noise, autoregressive (AR) models, moving average (MA) models, ARMA models
 2. Stationarity, differencing, detrending, seasonality3. DickeyFuller test for stationarity4. Autocorrelation function (ACF) and partial autocorrelation function (PACF)5. BoxJenkins methodology for selecting an ARIMA model
 Chapter 10: Scalable Machine Learning and related technologyChapter Goal: In the concluding chapter, we will discuss some of the contemporary technologies and architectures used for building scalable Machine Learning models. This chapter will give an emphasis on how the Machine Learning algorithms are going through the changes required for accommodating the new Big Data age. And how the new domains likes Data Science is gaining the popularity with just using the classic ML algorithms.No of pages: 80Sub  Topics:1. Introduction to Map Reduce Architecture2. Understanding basics of Apache Hadoop, Hive and Pig3. Integrating Apache Hadoop and R4. Parallel Processing using R5. Machine Learning using Apache Spark and its tools
 Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlowChapter Goal: Certain problems which were thought to be highly complex and computationally infeasible to be solved by either by sophisticated heuristic or traditional Machine Learning algorithms, are now becoming possible to be solved using Deep Learning (DL) algorithms. Although DL as a subject derives its root from the Neural Network models of Machine Learning, its architecture is trying to mimic the way human brain works. Tasks that we humans do quite effortlessly, like driving a car, processing speech and differentiating apples from oranges requires enormous amount of cognitive ability which we never realize. DL algorithms are getting better in performing such tasks more efficiently than humans now.
 No of pages: 50Sub  Topics:1. Using Keras and TensorFlow with R2. Overview of RNN, CNN, LSTMs networks3. Question Answering using Memory Network4. Text and Image processing using Keras.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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