Contents -- Chapter 1. Introduction to data science and data analytics 1 -- 1.1 About Data Science -- 1.2 About the EDISON Project and Data Science Framework -- 1.2.1 The EDISON project -- 1.2.2 The EDISON Data Science Framework -- 1.3 About Data Analytics -- 1.3.1 Data Analytics Competences -- 1.3.2 Data Analytics Body of Knowledge -- 1.3.3 Data Analytics Model Curriculum Approach -- 1.3.4 Data Analytics Professional Profiles -- 1.4 About this Book -- Chapter 2. Data ...... 49 -- A. Theory -- 2.1 Introduction -- 2.2 Characteristic -- 2.2.1 Definition of characteristic -- 2.2.2 Types of characteristics -- 2.3 Data -- 2.3.1 Definition of Data -- 2.3.2 Types of data from their nature -- 2.3.3 Types of data from their storage -- 2.4 Available Data -- 2.4.1 Experiment -- 2.4.2 Data population -- 2.4.3 Data Sample -- 2.4.4 Data Quality -- 2.5 Frequency -- 2.5.1 Definition of frequency -- 2.5.2 Types of frequency -- 2.5.3 Frequency of grouped Data -- 2.5.4 Mode -- 2.6 Mean -- 2.6.1 Definition of Mean -- 2.6.2 Arithmetic Mean -- 2.6.3 Variance and Standard Deviation -- 2.7 Median -- 2.7.1 Range -- 2.7.2 Median -- 2.7.3 Quantiles -- 2.7.4 Quantiles range -- B. Computer Based Solving -- 2.8 Reproject -- 2.9 R graphical user interface -- 2.10 Data exercises solves with R -- C. Data Exercises solves -- 2.11 Handmade exercises -- 2.12 Exercises solves in R -- Annex. Data Extended Concepts -- 2.A.1 Frequency -- 2.A.2 Mean -- Chapter 3. Probability -- A. Theory -- 3.1 Introduction -- 3.2 Event -- 3.3 Sets theory actions and operations -- 3.4 La Place or classic probability -- 3.5 Bayesian Probability -- 3.6 Probability distribution of random variables -- 3.6.1 Random Variable -- 3.6.2 Probability distribution -- 3.6.3 Discrete probability distributions -- 3.6.3.1 Bernoulli Probability distribution -- 3.6.3.2 Binomial Probability distribution -- 3.6.3.3 Geometric Probability distribution -- 3.6.3.4 Poison Probability distribution -- 3.6.4 Continuous probability distribution -- 3.6.4.1 Normal Distribution -- 3.6.4.2 Pearson chi square distribution -- 3.6.4.3 T the student distribution -- 3.6.4.4 F the fisher distribution -- B. Computer Based Solving -- C. Probability exercises solved -- 3.7 Handmade exercises -- 3.8 Exercises solved in R -- Annex. Probability extended concepts -- Chapter 4. Anomaly Detection -- Juan. J Cuadrado-Gallego, Yuri Demchenko, Josefa Gómez, Adelhamid Tayebi -- A. Theory -- 4.1 Introduction -- 4.2 Anomaly detection basic on Statistics -- 4.2.1 Anomaly detection Basic on the mean and the standard deviation -- 4.2.2Anomaly detection based on the quartiles -- 4.2.3 Anomaly detection based errors of the residuals -- 4.3 Anomaly detection based on proximity. K nearest neighbor algorithm -- 4.4 Anomaly detection based on density simplified local outlier factor algorithm -- B. Computer based solving -- 4.5 R packages -- 4.6 Anomaly detection the exercise solves with R -- C. Anomaly detection exercises solves -- 4.7 Handmade exercises -- 4.8 Exercises solved in R -- -- Chapter 5. Unsupervised Classification -- Juan. J Cuadrado-Gallego, Yuri Demchenko, Adelhamid Tayebi -- A. Theory -- 5.1 Introduction -- 5.2 Unsupervised classification based on distances K Meand Algorithm -- 5.3 Agglomerative hierarchical clustering -- B. Computer Based Solved -- 5.4 R studio -- 5.5 Unsupervised classification exercises solves with R -- C. Unsupervised Classification Solved -- 5.6 Handmade exercises -- 5.7 Exercises solved in R -- -- Chapter 6. Supervised Classification -- Juan. J Cuadrado-Gallego, Yuri Demchenko, Josefa Gómez -- A. Theory -- 6.1 Introduction -- 6.2 Decision tree -- 6.2.1 Optimizing the construction of a decision tree: ID3 Algorithm -- 6.2.2 Optimizing the construction of a decision tree: CART Algorithm -- 6.2.3 Optimizing the construction of a decision tree: Error Algorithm -- 6.3 Neural Network -- 6.4 Naïve Bayes -- 6.5 Regression functions -- 6.5.1 Lineal regression of polynomial events -- 6.5.2 Lineal regression of polynomial for three events -- 6.5.3 Lineal regression of polynomial for K events -- 6.5.4 No Lineal regression of polynomial for two events -- 6.5.5 No Lineal regression of not polynomial for two events -- 6.5.6 Lineal regression validity analysis -- B. Computer based solving -- C. Supervised classification analysis exercises solved -- 6.6 Handmade Exercises -- 6.7. Exercises solves in R -- Chapter 7. Association -- A. Theory -- 7.1 Introduction -- 7.2 Analysis of association of events composed by a single elementary event -- 7.2.1 Support -- 7.2.2 Confidence -- 7.2.3 Contingency -- 7.2.4 Correlation -- 7.3 Analysis of association of events composed by more than one elementary event . Apriori algorithm -- B. Computer based solving -- C. Association analysis exercises solved -- 7.4 Handmade Exercises -- 7.5 Exercises solves in R.