{"product_id":"data-science-using-python-and-r-isbn-9781119526810","title":"Data Science Using Python and R","description":"\u003cp\u003e\u003cb\u003eLearn data science by doing data science!\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003ci\u003eData Science Using Python and R \u003c\/i\u003e\u003c\/b\u003e\u003cb\u003ewill get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData science is hot. \u003ci\u003eBloomberg \u003c\/i\u003ecalled data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In \u003ci\u003eData Science Using Python and R\u003c\/i\u003e, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. \u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Science Using Python and R\u003c\/i\u003e is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R.\u003c\/p\u003e \u003cp\u003eThose with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.\u003c\/p\u003e \u003cp\u003eFurther, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Science Using Python and R\u003c\/i\u003e provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAbout the Authors xv\u003c\/p\u003e \u003cp\u003eAcknowledgements xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 \u003c\/b\u003e\u003cb\u003eIntroduction to Data Science 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Why Data Science? 1\u003c\/p\u003e \u003cp\u003e1.2 What is Data Science? 1\u003c\/p\u003e \u003cp\u003e1.3 The Data Science Methodology 2\u003c\/p\u003e \u003cp\u003e1.4 Data Science Tasks 5\u003c\/p\u003e \u003cp\u003e1.4.1 Description 6\u003c\/p\u003e \u003cp\u003e1.4.2 Estimation 6\u003c\/p\u003e \u003cp\u003e1.4.3 Classification 6\u003c\/p\u003e \u003cp\u003e1.4.4 Clustering 7\u003c\/p\u003e \u003cp\u003e1.4.5 Prediction 7\u003c\/p\u003e \u003cp\u003e1.4.6 Association 7\u003c\/p\u003e \u003cp\u003eExercises 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 \u003c\/b\u003e\u003cb\u003eThe Basics of Python and R 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Downloading Python 9\u003c\/p\u003e \u003cp\u003e2.2 Basics of Coding in Python 9\u003c\/p\u003e \u003cp\u003e2.2.1 Using Comments in Python 9\u003c\/p\u003e \u003cp\u003e2.2.2 Executing Commands in Python 10\u003c\/p\u003e \u003cp\u003e2.2.3 Importing Packages in Python 11\u003c\/p\u003e \u003cp\u003e2.2.4 Getting Data into Python 12\u003c\/p\u003e \u003cp\u003e2.2.5 Saving Output in Python 13\u003c\/p\u003e \u003cp\u003e2.2.6 Accessing Records and Variables in Python 14\u003c\/p\u003e \u003cp\u003e2.2.7 Setting Up Graphics in Python 15\u003c\/p\u003e \u003cp\u003e2.3 Downloading R and RStudio 17\u003c\/p\u003e \u003cp\u003e2.4 Basics of Coding in R 19\u003c\/p\u003e \u003cp\u003e2.4.1 Using Comments in R 19\u003c\/p\u003e \u003cp\u003e2.4.2 Executing Commands in R 20\u003c\/p\u003e \u003cp\u003e2.4.3 Importing Packages in R 20\u003c\/p\u003e \u003cp\u003e2.4.4 Getting Data into R 21\u003c\/p\u003e \u003cp\u003e2.4.5 Saving Output in R 23\u003c\/p\u003e \u003cp\u003e2.4.6 Accessing Records and Variables in R 24\u003c\/p\u003e \u003cp\u003eReferences 26\u003c\/p\u003e \u003cp\u003eExercises 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 \u003c\/b\u003e\u003cb\u003eData Preparation 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 The Bank Marketing Data Set 29\u003c\/p\u003e \u003cp\u003e3.2 The Problem Understanding Phase 29\u003c\/p\u003e \u003cp\u003e3.2.1 Clearly Enunciate the Project Objectives 29\u003c\/p\u003e \u003cp\u003e3.2.2 Translate These Objectives into a Data Science Problem 30\u003c\/p\u003e \u003cp\u003e3.3 Data Preparation Phase 31\u003c\/p\u003e \u003cp\u003e3.4 Adding an Index Field 31\u003c\/p\u003e \u003cp\u003e3.4.1 How to Add an Index Field Using Python 31\u003c\/p\u003e \u003cp\u003e3.4.2 How to Add an Index Field Using R 32\u003c\/p\u003e \u003cp\u003e3.5 Changing Misleading Field Values 33\u003c\/p\u003e \u003cp\u003e3.5.1 How to Change Misleading Field Values Using Python 34\u003c\/p\u003e \u003cp\u003e3.5.2 How to Change Misleading Field Values Using R 34\u003c\/p\u003e \u003cp\u003e3.6 Reexpression of Categorical Data as Numeric 36\u003c\/p\u003e \u003cp\u003e3.6.1 How to Reexpress Categorical Field Values Using Python 36\u003c\/p\u003e \u003cp\u003e3.6.2 How to Reexpress Categorical Field Values Using R 38\u003c\/p\u003e \u003cp\u003e3.7 Standardizing the Numeric Fields 39\u003c\/p\u003e \u003cp\u003e3.7.1 How to Standardize Numeric Fields Using Python 40\u003c\/p\u003e \u003cp\u003e3.7.2 How to Standardize Numeric Fields Using R 40\u003c\/p\u003e \u003cp\u003e3.8 Identifying Outliers 40\u003c\/p\u003e \u003cp\u003e3.8.1 How to Identify Outliers Using Python 41\u003c\/p\u003e \u003cp\u003e3.8.2 How to Identify Outliers Using R 42\u003c\/p\u003e \u003cp\u003eReferences 43\u003c\/p\u003e \u003cp\u003eExercises 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 \u003c\/b\u003e\u003cb\u003eExploratory Data Analysis 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 EDA Versus HT 47\u003c\/p\u003e \u003cp\u003e4.2 Bar Graphs with Response Overlay 47\u003c\/p\u003e \u003cp\u003e4.2.1 How to Construct a Bar Graph with Overlay Using Python 49\u003c\/p\u003e \u003cp\u003e4.2.2 How to Construct a Bar Graph with Overlay Using R 50\u003c\/p\u003e \u003cp\u003e4.3 Contingency Tables 51\u003c\/p\u003e \u003cp\u003e4.3.1 How to Construct Contingency Tables Using Python 52\u003c\/p\u003e \u003cp\u003e4.3.2 How to Construct Contingency Tables Using R 53\u003c\/p\u003e \u003cp\u003e4.4 Histograms with Response Overlay 53\u003c\/p\u003e \u003cp\u003e4.4.1 How to Construct Histograms with Overlay Using Python 55\u003c\/p\u003e \u003cp\u003e4.4.2 How to Construct Histograms with Overlay Using R 58\u003c\/p\u003e \u003cp\u003e4.5 Binning Based on Predictive Value 58\u003c\/p\u003e \u003cp\u003e4.5.1 How to Perform Binning Based on Predictive Value Using Python 59\u003c\/p\u003e \u003cp\u003e4.5.2 How to Perform Binning Based on Predictive Value Using R 62\u003c\/p\u003e \u003cp\u003eReferences 63\u003c\/p\u003e \u003cp\u003eExercises 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 \u003c\/b\u003e\u003cb\u003ePreparing to Model the Data 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The Story So Far 69\u003c\/p\u003e \u003cp\u003e5.2 Partitioning the Data 69\u003c\/p\u003e \u003cp\u003e5.2.1 How to Partition the Data in Python 70\u003c\/p\u003e \u003cp\u003e5.2.2 How to Partition the Data in R 71\u003c\/p\u003e \u003cp\u003e5.3 Validating your Partition 72\u003c\/p\u003e \u003cp\u003e5.4 Balancing the Training Data Set 73\u003c\/p\u003e \u003cp\u003e5.4.1 How to Balance the Training Data Set in Python 74\u003c\/p\u003e \u003cp\u003e5.4.2 How to Balance the Training Data Set in R 75\u003c\/p\u003e \u003cp\u003e5.5 Establishing Baseline Model Performance 77\u003c\/p\u003e \u003cp\u003eReferences 78\u003c\/p\u003e \u003cp\u003eExercises 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 \u003c\/b\u003e\u003cb\u003eDecision Trees 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction to Decision Trees 81\u003c\/p\u003e \u003cp\u003e6.2 Classification and Regression Trees 83\u003c\/p\u003e \u003cp\u003e6.2.1 How to Build CART Decision Trees Using Python 84\u003c\/p\u003e \u003cp\u003e6.2.2 How to Build CART Decision Trees Using R 86\u003c\/p\u003e \u003cp\u003e6.3 The C5.0 Algorithm for Building Decision Trees 88\u003c\/p\u003e \u003cp\u003e6.3.1 How to Build C5.0 Decision Trees Using Python 89\u003c\/p\u003e \u003cp\u003e6.3.2 How to Build C5.0 Decision Trees Using R 90\u003c\/p\u003e \u003cp\u003e6.4 Random Forests 91\u003c\/p\u003e \u003cp\u003e6.4.1 How to Build Random Forests in Python 92\u003c\/p\u003e \u003cp\u003e6.4.2 How to Build Random Forests in R 92\u003c\/p\u003e \u003cp\u003eReferences 93\u003c\/p\u003e \u003cp\u003eExercises 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 \u003c\/b\u003e\u003cb\u003eModel Evaluation 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Model Evaluation 97\u003c\/p\u003e \u003cp\u003e7.2 Classification Evaluation Measures 97\u003c\/p\u003e \u003cp\u003e7.3 Sensitivity and Specificity 99\u003c\/p\u003e \u003cp\u003e7.4 Precision, Recall, and \u003ci\u003eF\u003csub\u003eβ \u003c\/sub\u003e\u003c\/i\u003eScores 99\u003c\/p\u003e \u003cp\u003e7.5 Method for Model Evaluation 100\u003c\/p\u003e \u003cp\u003e7.6 An Application of Model Evaluation 100\u003c\/p\u003e \u003cp\u003e7.6.1 How to Perform Model Evaluation Using R 103\u003c\/p\u003e \u003cp\u003e7.7 Accounting for Unequal Error Costs 104\u003c\/p\u003e \u003cp\u003e7.7.1 Accounting for Unequal Error Costs Using R 105\u003c\/p\u003e \u003cp\u003e7.8 Comparing Models with and without Unequal Error Costs 106\u003c\/p\u003e \u003cp\u003e7.9 Data‐Driven Error Costs 107\u003c\/p\u003e \u003cp\u003eExercises 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 \u003c\/b\u003e\u003cb\u003eNaïve Bayes Classification 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction to Naive Bayes 113\u003c\/p\u003e \u003cp\u003e8.2 Bayes Theorem 113\u003c\/p\u003e \u003cp\u003e8.3 Maximum a Posteriori Hypothesis 114\u003c\/p\u003e \u003cp\u003e8.4 Class Conditional Independence 114\u003c\/p\u003e \u003cp\u003e8.5 Application of Naive Bayes Classification 115\u003c\/p\u003e \u003cp\u003e8.5.1 Naive Bayes in Python 121\u003c\/p\u003e \u003cp\u003e8.5.2 Naive Bayes in R 123\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003eExercises 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 \u003c\/b\u003e\u003cb\u003eNeural Networks 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction to Neural Networks 129\u003c\/p\u003e \u003cp\u003e9.2 The Neural Network Structure 129\u003c\/p\u003e \u003cp\u003e9.3 Connection Weights and the Combination Function 131\u003c\/p\u003e \u003cp\u003e9.4 The Sigmoid Activation Function 133\u003c\/p\u003e \u003cp\u003e9.5 Backpropagation 134\u003c\/p\u003e \u003cp\u003e9.6 An Application of a Neural Network Model 134\u003c\/p\u003e \u003cp\u003e9.7 Interpreting the Weights in a Neural Network Model 136\u003c\/p\u003e \u003cp\u003e9.8 How to Use Neural Networks in R 137\u003c\/p\u003e \u003cp\u003eReferences 138\u003c\/p\u003e \u003cp\u003eExercises 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 \u003c\/b\u003e\u003cb\u003eClustering 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 What is Clustering? 141\u003c\/p\u003e \u003cp\u003e10.2 Introduction to the \u003ci\u003eK\u003c\/i\u003e‐Means Clustering Algorithm 142\u003c\/p\u003e \u003cp\u003e10.3 An Application of \u003ci\u003eK\u003c\/i\u003e‐Means Clustering 143\u003c\/p\u003e \u003cp\u003e10.4 Cluster Validation 144\u003c\/p\u003e \u003cp\u003e10.5 How to Perform \u003ci\u003eK\u003c\/i\u003e‐Means Clustering Using Python 145\u003c\/p\u003e \u003cp\u003e10.6 How to Perform \u003ci\u003eK\u003c\/i\u003e‐Means Clustering Using R 147\u003c\/p\u003e \u003cp\u003eExercises 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 \u003c\/b\u003e\u003cb\u003eRegression Modeling 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Estimation Task 151\u003c\/p\u003e \u003cp\u003e11.2 Descriptive Regression Modeling 151\u003c\/p\u003e \u003cp\u003e11.3 An Application of Multiple Regression Modeling 152\u003c\/p\u003e \u003cp\u003e11.4 How to Perform Multiple Regression Modeling Using Python 154\u003c\/p\u003e \u003cp\u003e11.5 How to Perform Multiple Regression Modeling Using R 156\u003c\/p\u003e \u003cp\u003e11.6 Model Evaluation for Estimation 157\u003c\/p\u003e \u003cp\u003e11.6.1 How to Perform Estimation Model Evaluation Using Python 159\u003c\/p\u003e \u003cp\u003e11.6.2 How to Perform Estimation Model Evaluation Using R 160\u003c\/p\u003e \u003cp\u003e11.7 Stepwise Regression 161\u003c\/p\u003e \u003cp\u003e11.7.1 How to Perform Stepwise Regression Using R 162\u003c\/p\u003e \u003cp\u003e11.8 Baseline Models for Regression 162\u003c\/p\u003e \u003cp\u003eReferences 163\u003c\/p\u003e \u003cp\u003eExercises 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 \u003c\/b\u003e\u003cb\u003eDimension Reduction 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 The Need for Dimension Reduction 167\u003c\/p\u003e \u003cp\u003e12.2 Multicollinearity 168\u003c\/p\u003e \u003cp\u003e12.3 Identifying Multicollinearity Using Variance Inflation Factors 171\u003c\/p\u003e \u003cp\u003e12.3.1 How to Identify Multicollinearity Using Python 172\u003c\/p\u003e \u003cp\u003e12.3.2 How to Identify Multicollinearity in R 173\u003c\/p\u003e \u003cp\u003e12.4 Principal Components Analysis 175\u003c\/p\u003e \u003cp\u003e12.5 An Application of Principal Components Analysis 175\u003c\/p\u003e \u003cp\u003e12.6 How Many Components Should We Extract? 176\u003c\/p\u003e \u003cp\u003e12.6.1 The Eigenvalue Criterion 176\u003c\/p\u003e \u003cp\u003e12.6.2 The Proportion of Variance Explained Criterion 177\u003c\/p\u003e \u003cp\u003e12.7 Performing Pca with \u003ci\u003eK \u003c\/i\u003e= 4 178\u003c\/p\u003e \u003cp\u003e12.8 Validation of the Principal Components 178\u003c\/p\u003e \u003cp\u003e12.9 How to Perform Principal Components Analysis Using Python 179\u003c\/p\u003e \u003cp\u003e12.10 How to Perform Principal Components Analysis Using R 181\u003c\/p\u003e \u003cp\u003e12.11 When is Multicollinearity Not a Problem? 183\u003c\/p\u003e \u003cp\u003eReferences 184\u003c\/p\u003e \u003cp\u003eExercises 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 \u003c\/b\u003e\u003cb\u003eGeneralized Linear Models 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 An Overview of General Linear Models 187\u003c\/p\u003e \u003cp\u003e13.2 Linear Regression as a General Linear Model 188\u003c\/p\u003e \u003cp\u003e13.3 Logistic Regression as a General Linear Model 188\u003c\/p\u003e \u003cp\u003e13.4 An Application of Logistic Regression Modeling 189\u003c\/p\u003e \u003cp\u003e13.4.1 How to Perform Logistic Regression Using Python 190\u003c\/p\u003e \u003cp\u003e13.4.2 How to Perform Logistic Regression Using R 191\u003c\/p\u003e \u003cp\u003e13.5 Poisson Regression 192\u003c\/p\u003e \u003cp\u003e13.6 An Application of Poisson Regression Modeling 192\u003c\/p\u003e \u003cp\u003e13.6.1 How to Perform Poisson Regression Using Python 193\u003c\/p\u003e \u003cp\u003e13.6.2 How to Perform Poisson Regression Using R 194\u003c\/p\u003e \u003cp\u003eReference 195\u003c\/p\u003e \u003cp\u003eExercises 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 \u003c\/b\u003e\u003cb\u003eAssociation Rules 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction to Association Rules 199\u003c\/p\u003e \u003cp\u003e14.2 A Simple Example of Association Rule Mining 200\u003c\/p\u003e \u003cp\u003e14.3 Support, Confidence, and Lift 200\u003c\/p\u003e \u003cp\u003e14.4 Mining Association Rules 202\u003c\/p\u003e \u003cp\u003e14.4.1 How to Mine Association Rules Using R 203\u003c\/p\u003e \u003cp\u003e14.5 Confirming Our Metrics 207\u003c\/p\u003e \u003cp\u003e14.6 The Confidence Difference Criterion 208\u003c\/p\u003e \u003cp\u003e14.6.1 How to Apply the Confidence Difference Criterion Using R 208\u003c\/p\u003e \u003cp\u003e14.7 The Confidence Quotient Criterion 209\u003c\/p\u003e \u003cp\u003e14.7.1 How to Apply the Confidence Quotient Criterion Using R 210\u003c\/p\u003e \u003cp\u003eReferences 211\u003c\/p\u003e \u003cp\u003eExercises 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix \u003c\/b\u003e\u003cb\u003eData Summarization and Visualization 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart 1: Summarization 1: Building Blocks of Data Analysis 215\u003c\/p\u003e \u003cp\u003ePart 2: Visualization: Graphs and Tables for Summarizing and Organizing Data 217\u003c\/p\u003e \u003cp\u003ePart 3: Summarization 2: Measures of Center, Variability, and Position 222\u003c\/p\u003e \u003cp\u003ePart 4: Summarization and Visualization of Bivariate Elationships 225\u003c\/p\u003e \u003cp\u003eIndex 231 \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eCHANTAL D. LAROSE, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is an Assistant Professor of Statistics \u0026amp; Data Science at Eastern Connecticut State University (ECSU). She has co-authored three books on data science and predictive analytics and helped develop data science programs at ECSU and SUNY New Paltz. Her PhD dissertation, \u003ci\u003eModel-Based Clustering of Incomplete Data\u003c\/i\u003e, tackles the persistent problem of trying to do data science with incomplete data. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDANIEL T. LAROSE, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a Professor of Data Science and Statistics and Director of the Data Science programs at Central Connecticut State University. He has published many books on data science, data mining, predictive analytics, and statistics. His consulting clients include \u003ci\u003eThe Economist\u003c\/i\u003e magazine, \u003ci\u003eForbes\u003c\/i\u003e Magazine, the CIT Group, and Microsoft.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLearn data science by doing data science!\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eData Science Using Python and R\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e will get you plugged into the world's two most widespread open-source platforms for data science: Python and R.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eData science is hot. \u003ci\u003eBloomberg\u003c\/i\u003e called data scientist \"the hottest job in America.\" Python and R are the top two open-source data science tools in the world. In \u003ci\u003eData Science Using Python and R\u003c\/i\u003e, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eData Science Using Python and R\u003c\/i\u003e is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. \u003c\/p\u003e\u003cp\u003eThose with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. \u003c\/p\u003e\u003cp\u003eFurther, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eData Science Using Python and R\u003c\/i\u003e provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989025997029,"sku":"NP9781119526810","price":114.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119526810.jpg?v=1761782488","url":"https:\/\/k12savings.com\/es\/products\/data-science-using-python-and-r-isbn-9781119526810","provider":"K12savings","version":"1.0","type":"link"}