Book Details


Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets / Maria Cristina Mariani, Osei Kofi Tweneboah, Maria Pia Beccar-Varela

Publication year: 2021

ISBN: 1119674689

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Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets

Subject: Data science, Big Data, Data Mining, Knowledge Discovery, Models of Computation, Big Data Analysis, Data Streams, Learning Analysis, Data Analysis, Matrix Algebra, Random Vectors, Multivariate Analysis, Time Series Forecasting, Python, Algorithms, Data Preprocessing, Data Validations, Data Visualizations, Binomial and Trinomial Trees, Component Analysis, Discriminant and Cluster Analysis, Multidimensional Scaling, Support Vector Machines, Neural Networks, Fourier Analysis, Wavelets Analysis, Stochastic Analysis, Fractal Analysis, Stochastic Differential Equations