![]() Multivariate Analysis: Multivariate Frequencies, Multivariate Data Visualization, Multivariate Statistics, Infographics and Word Clouds.ĭata Quality and Preprocessing: Data Quality, Converting to a Different Scale Type, Converting to a Different Scale, Data Transformation, Dimensionality Reduction.Ĭlustering: Distance Measures, Clustering Validation, Clustering Techniques.įrequent Pattern Mining: Frequent Itemsets, Association Rules, Behind Support and Confidence, Other Types of Pattern.Ĭheat Sheet and Project on Descriptive Analytics: Cheat Sheet of Descriptive Analytics, Project on Descriptive Analytics. Introductory: Introduction to Data, Big Data and Data Science, Big Data Architectures, Small Data, What is Data? A Short Taxonomy of Data Analytics, Examples of Data Use, A Project on Data Analytics.ĭescriptive Statistics: Scale Types, Descriptive Univariate Analysis, Descriptive Bivariate Analysis. ![]() To explain the binary classification problem, performance measures for classification, methods based on probabilities and distance measures and more advanced and state-of-the-art methods of prediction of data. ![]() To explain cheat sheet and project on descriptive analytics and generalization, performance measures for regression and the bias–variance trade-off. To explain methods involving clustering, frequent pattern mining, which aims to capture the most frequent patterns. phase of the CRISP-DM methodology, concerning data quality issues, converting data to different scales or scale types and reducing data dimensionality. To explain multivariate descriptive statistics methods of data analytics, methods used in the data preparation. ![]() ![]() To explain introductory concepts, a brief methodological description and some descriptive statistics of data. ![]()
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