{"product_id":"154554","title":"Everything Data Analysts Need to Know ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLMroiyv5gHRNsry9opDgIy4kS.png?v=1765080649\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Everything Data Analysts Need to Know \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/132423891\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLet's delve deeper into each step of data analysis and machine learning!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book covers everything from statistics, which can be considered the foundation of data analysis, to machine learning techniques from a practical perspective.\u003cbr\u003e From defining business problems to exploratory data analysis (EDA), data preprocessing and derivative variable creation, machine learning modeling and performance evaluation, and even storytelling, this book covers everything a data analyst needs to know.\u003cbr\u003e We've minimized unnecessary formulas and theories so that practitioners can immediately apply them in their companies, and organized the core concepts in an easy-to-understand manner. \u003cbr\u003eYou'll learn how to properly understand data and gain meaningful business insights through hands-on mini-project-based training in data analysis and machine learning.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e[Part 1] Building Data Fundamentals\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 1: Understanding Statistics\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Why should we know statistics?\u003cbr\u003e 1.2 Differences between Machine Learning and Traditional Statistics\u003cbr\u003e 1.3 Definition and Origin of Statistics\u003cbr\u003e 1.4 Descriptive Statistics Inferential Statistics\u003cbr\u003e ___1.4.1 Technical Statistics\u003cbr\u003e ___1.4.2 Inferential Statistics\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 2: Population and Sampling\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Population and sample, census and sample survey\u003cbr\u003e 2.2 Why do we conduct sampling surveys and how do we apply data science?\u003cbr\u003e 2.3 Types of bias in sampling\u003cbr\u003e 2.4 Types of cognitive biases\u003cbr\u003e ___2.4.1 Confirmation bias\u003cbr\u003e ___2.4.2 Anchoring bias\u003cbr\u003e ___2.4.3 Choice-supportive bias \u003cbr\u003e___2.4.4 Denominator bias\u003cbr\u003e ___2.4.5 Survivorship bias\u003cbr\u003e 2.5 Bias and Variance in Machine Learning Models\u003cbr\u003e 2.6 Sampling methods to minimize sampling bias\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 3: Variables and Scales\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Types of variables\u003cbr\u003e 3.2 Types of variable relationships\u003cbr\u003e 3.3 Types of scales\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 4: Descriptive Statistical Measurements of Data\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Measurement of central tendency\u003cbr\u003e 4.2 Variance and standard deviation\u003cbr\u003e 4.3 Scatterplot and range, quartiles, and coefficient of variation\u003cbr\u003e 4.4 Skewness and Kurtosis\u003cbr\u003e ___4.4.1 Skewness\u003cbr\u003e ___4.4.2 Kurtosis\u003cbr\u003e 4.5 Rule of thumb for standard deviation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 5: Probability and Random Variables\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Basic Concepts of Probability\u003cbr\u003e 5.2 Types of Probability\u003cbr\u003e 5.3 Segmentation and Bayesian Theory\u003cbr\u003e ___5.3.1 Split\u003cbr\u003e ___5.3.2 Bayesian theory\u003cbr\u003e 5.4 Concept and types of random variables\u003cbr\u003e 5.5 Simpson's Paradox\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 6: Probability Distributions\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Definition and types of probability distributions\u003cbr\u003e 6.2 Discrete probability distributions\u003cbr\u003e ___6.2.1 Uniform distribution\u003cbr\u003e ___6.2.2 Binomial distribution\u003cbr\u003e ___6.2.3 Hypergeometric distribution\u003cbr\u003e ___6.2.4 Poisson distribution\u003cbr\u003e 6.3 Continuous probability distribution\u003cbr\u003e ___6.3.1 Normal distribution\u003cbr\u003e ___6.3.2 Exponential distribution\u003cbr\u003e 6.4 Central Limit Theorem\u003cbr\u003e \u003cbr\u003e\u003cb\u003e▣ Chapter 7: Hypothesis Testing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Null and alternative hypotheses\u003cbr\u003e 7.2 Hypothesis testing procedures\u003cbr\u003e 7.3 Significance level and p-value of hypothesis testing\u003cbr\u003e 7.4 Type 1 and Type 2 errors\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 2] Preparing for Data Analysis\u003cbr\u003e\u003cbr\u003e ▣ Chapter 8: Analysis Project Preparation and Planning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Overall data analysis process\u003cbr\u003e ___8.1.1 Three Steps of Data Analysis\u003cbr\u003e ___8.1.2 CRISP-DM methodology\u003cbr\u003e ___8.1.3 SAS SEMMA Methodology\u003cbr\u003e 8.2 Defining the business problem and deriving the analysis objectives\u003cbr\u003e 8.3 Change in analysis purpose\u003cbr\u003e 8.4 Domain Knowledge\u003cbr\u003e 8.5 External Data Collection and Crawling\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 9: Setting Up the Analysis Environment\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 Which data analysis language should I use?\u003cbr\u003e 9.2 Understanding the Data Processing Process\u003cbr\u003e 9.3 Distributed Data Processing\u003cbr\u003e ___9.3.1 HDFS\u003cbr\u003e ___9.3.2 Apache Spark\u003cbr\u003e 9.4 Table Joins, Definitions, and ERDs\u003cbr\u003e ___9.4.1 Table Joins\u003cbr\u003e ___9.4.2 Data Dictionary\u003cbr\u003e ___9.4.3 Table Definition\u003cbr\u003e ___9.4.4 ERD\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 10: Data Exploration and Visualization\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 10.1 Exploratory Data Analysis\u003cbr\u003e ___10.1.1 EDA using Excel \u003cbr\u003e___10.1.2 Exploratory Data Analysis Practice\u003cbr\u003e 10.2 Covariance and Correlation Analysis\u003cbr\u003e ___10.2.1 Covariance\u003cbr\u003e ___10.2.2 Correlation coefficient\u003cbr\u003e ___10.2.3 Covariance and Correlation Analysis Practice\u003cbr\u003e 10.3 Time Visualization\u003cbr\u003e ___10.3.1 Time Visualization Practice\u003cbr\u003e 10.4 Comparative Visualization\u003cbr\u003e ___10.4.1 Comparative Visualization Practice\u003cbr\u003e 10.5 Distribution Visualization\u003cbr\u003e ___10.5.1 Distribution Visualization Practice\u003cbr\u003e 10.6 Relationship Visualization\u003cbr\u003e ___10.6.1 Relationship Visualization Practice\u003cbr\u003e 10.7 Spatial Visualization\u003cbr\u003e ___10.7.1 Spatial Visualization Practice\u003cbr\u003e 10.8 Box plot\u003cbr\u003e ___10.8.1 Box Plot Practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 11: Data Preprocessing and Derived Variable Creation\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 11.1 Handling missing values\u003cbr\u003e ___11.1.1 Practice Handling Missing Values\u003cbr\u003e 11.2 Outlier Handling\u003cbr\u003e ___11.2.1 Outlier Handling Practice\u003cbr\u003e 11.3 Variable Binning\u003cbr\u003e ___11.3.1 Variable Binning Practice\u003cbr\u003e 11.4 Data Standardization and Normalization Scaling\u003cbr\u003e ___11.4.1 Data Standardization and Normalization Scaling Practice\u003cbr\u003e 11.5 Creating Derived Variables to Improve Model Performance\u003cbr\u003e ___11.5.1 Practice creating derived variables\u003cbr\u003e 11.6 Sliding Window Data Processing\u003cbr\u003e ___11.6.1 Sliding Window Practice \u003cbr\u003e11.7 Handling dummy variables of categorical variables\u003cbr\u003e ___11.7.1 Practice handling dummy variables of categorical variables\u003cbr\u003e 11.8 Undersampling and Oversampling to Address Class Imbalance Problems\u003cbr\u003e ___11.8.1 Undersampling and Oversampling Practice\u003cbr\u003e 11.9 How to measure data distance\u003cbr\u003e ___11.9.1 Representative distance measurement methods\u003cbr\u003e ___11.9.2 Data Distance Measurement Practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 3] Analyzing Data\u003cbr\u003e\u003cbr\u003e ▣ Chapter 12: Statistical Analysis Methodology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 12.1 Analysis Model Overview\u003cbr\u003e 12.2 Principal Component Analysis (PCA)\u003cbr\u003e ___12.2.1 Principal Component Analysis Practice\u003cbr\u003e 12.3 Common Factor Analysis (CFA)\u003cbr\u003e ___12.3.1 Common Factor Analysis Practice\u003cbr\u003e 12.4 Addressing Multicollinearity and Shapley Value Analysis\u003cbr\u003e 12.5 Data Massage and Blind Analysis\u003cbr\u003e ___12.5.1 Data Massage\u003cbr\u003e ___12.5.2 Blind Analysis\u003cbr\u003e 12.6 Z-test and T-test\u003cbr\u003e ___12.6.1 Z-test and T-test Practice\u003cbr\u003e 12.7 Analysis of Variance (ANOVA)\u003cbr\u003e ___12.7.1 ANOVA Practice\u003cbr\u003e 12.8 Chi-square test (cross-tabulation)\u003cbr\u003e ___12.8.1 Chi-square test practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 13: Machine Learning Analysis Methodology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 13.1 Linear Regression Analysis and Elastic Net (Predictive Model) \u003cbr\u003e___13.1.1 Origin and Principles of Regression Analysis\u003cbr\u003e ___13.1.2 Polynomial regression\u003cbr\u003e ___13.1.3 Ridge, Lasso, and Elastic Net\u003cbr\u003e ___13.1.4 Linear Regression Analysis and Elastic Net Practice\u003cbr\u003e 13.2 Logistic Regression Analysis (Classification Model)\u003cbr\u003e ___13.2.1 Logistic Regression Analysis Practice\u003cbr\u003e 13.3 Decision Trees and Random Forests (Prediction\/Classification Models)\u003cbr\u003e ___13.3.1 Classification trees and regression trees\u003cbr\u003e ___13.3.2 Advantages and Disadvantages of Decision Tree Models\u003cbr\u003e ___13.3.3 Methods for preventing overfitting of decision tree models\u003cbr\u003e ___13.3.4 Random Forest\u003cbr\u003e ___13.3.5 Decision Tree and Random Forest Practice\u003cbr\u003e 13.4 Linear Discriminant Analysis and Quadratic Discriminant Analysis (Classification Models)\u003cbr\u003e ___13.4.1 Linear Discriminant Analysis\u003cbr\u003e ___13.4.2 Second-order discriminant analysis\u003cbr\u003e ___13.4.3 Practice of Linear Discriminant Analysis and Quadratic Discriminant Analysis\u003cbr\u003e 13.5 Support Vector Machine (Classification Model)\u003cbr\u003e ___13.5.1 Support Vector Machine Practice\u003cbr\u003e 13.6 KNN (Classification, Prediction Model)\u003cbr\u003e ___13.6.1 KNN Practice\u003cbr\u003e 13.7 Time Series Analysis (Forecasting Model)\u003cbr\u003e ___13.7.1 Regression-based time series analysis\u003cbr\u003e ___13.7.2 ARIMA Model \u003cbr\u003e___13.7.3 Time Series Analysis Practice\u003cbr\u003e 13.8 k-means clustering (clustering model)\u003cbr\u003e ___13.8.1 k-means clustering practice\u003cbr\u003e 13.9 Association Rules and Collaborative Filtering (Recommendation Models)\u003cbr\u003e ___13.9.1 Association Rules\u003cbr\u003e ___13.9.2 Content-Based Filtering and Collaborative Filtering\u003cbr\u003e ___13.9.3 Association Rules and Collaborative Filtering Practice\u003cbr\u003e 13.10 Artificial Neural Networks (CNN, RNN, LSTM)\u003cbr\u003e ___13.10.1 CNN\u003cbr\u003e ___13.10.2 RNN and LSTM\u003cbr\u003e ___13.10.3 Artificial Neural Network Practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 14: Model Evaluation\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 14.1 Training set, validation set, test set, and overfitting\u003cbr\u003e 14.2 Major cross-validation methods\u003cbr\u003e ___14.2.1 k-Fold Cross Validation\u003cbr\u003e ___14.2.2 LOOCV(Leave-one-out Cross-validation)\u003cbr\u003e ___14.2.3 Stratified K-fold Cross Validation\u003cbr\u003e ___14.2.4 Nested Cross Validation\u003cbr\u003e ___14.2.5 Grid Search Cross Validation\u003cbr\u003e ___14.2.6 Practicing the Major Cross-Validation Methods\u003cbr\u003e 14.3 Regression Performance Evaluation Index\u003cbr\u003e ___14.3.1 R-Square and Adjusted R-Square\u003cbr\u003e ___14.3.2 RMSE (Root Mean Square Error)\u003cbr\u003e ___14.3.3 MAE(Mean Absolute Error)\u003cbr\u003e ___14.3.4 MAPE(Mean Absolute Percentage Error)\u003cbr\u003e ___14.3.5 RMSLE(Root Mean Square Logarithmic Error) \u003cbr\u003e___14.3.6 AIC and BIC\u003cbr\u003e ___14.3.7 Regression Performance Evaluation Index Practice\u003cbr\u003e 14.4 Classification and Recommendation Performance Evaluation Indicators\u003cbr\u003e ___14.4.1 Confusion Matrix\u003cbr\u003e ___14.4.2 Accuracy, misclassification rate, precision, sensitivity, specificity, and f-score\u003cbr\u003e ___14.4.3 Improvement Table, Improvement Chart, and Improvement Curve\u003cbr\u003e ___14.4.4 ROC Curve and AUC\u003cbr\u003e ___14.4.5 Profit Curve\u003cbr\u003e ___14.4.6 Precision at k, Recall at K, and MAP\u003cbr\u003e ___14.4.7 Classification, Recommendation Performance Evaluation Indicator Practice\u003cbr\u003e 14.5 A\/B Testing and MAB\u003cbr\u003e ___14.5.1 A\/B Testing\u003cbr\u003e ___14.5.2 MAB\u003cbr\u003e 14.6 Pitfalls of Significance Probability\u003cbr\u003e 14.7 Analyst's Subjective Judgment and Storytelling\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4729\/MidCate007\/472860363.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ Basic statistical concepts essential for data analysis\u003cbr\u003e ◎ Data bias and cognitive bias\u003cbr\u003e ◎ How to define business problems and derive the purpose of data analysis\u003cbr\u003e ◎ Configuring the data analysis environment\u003cbr\u003e ◎ Data exploration and visualization\u003cbr\u003e ◎ Data preprocessing and derived variable creation \u003cbr\u003e◎ Major machine learning algorithms and model performance evaluation techniques\u003cbr\u003e ◎ A\/B testing and MAB \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e August 30, 2024\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 640 pages | 188*240*29mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158395483 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893435695146,"sku":"154554","price":46.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/2c77b1cc22ff3d713d2a12cc4a39bce3.jpg?v=1765401888","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154554","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}