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SKU/Artículo: AMZ-B0DNB5SXKK
DATA SCIENCE AND ENGINEERING A LEARNING PATH - DATA ACQUISITION, CLEANING, ANALYSIS AND VISUALIZATION WITH APPLICATIONS IN PYTHON
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1.07 kg
1.07 kg
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Amazon
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Sobre este producto
- This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises.It covers methodological aspects, data acquisition, management and cleaning, analysis and visualization. First of all it describes the CRISP DM methodology, the working phases, the success criteria, the usable languages and environments, the application libraries.Then, since this text uses Python for application aspects, its installation and use are briefly described. In any case, this text should not be considered a Python manual. If by chance you made the wrong choice because you expected something different, you are free to return it but perhaps it is not the case to give a negative rating.On acquisition, the book describes the data sources, acceleration techniques, discretization methods, security standards, data types and representations, techniques for managing text corpora such as bag-of-words, word-count, TF-IDF, n-grams, lexical analysis, syntactic analysis, semantic analysis, stop word filtering, stemming, techniques for representing and processing images, sampling, filtering, web scraping techniques.Then, the dimensions of data quality, algorithms for entity identification, truth discovery, rule-based cleaning, handling of missing and repeated values, categorical value encoding, cleaning of outliers and errors, handling of inconsistencies, scaling, data integration from various sources and ranking of open sources, application scenarios and the use of databases, data warehouses, data lakes and mediators, data schema mapping and the role of RDF, OWL and SPARQL,data transformations are consedered.On visualization, historical notes are made, then characteristics of an effective visualization are described, together with types of messages that can be conveyed, the Grammar of Graphs, the use of a graph and of a dashboard, the software and libraries that can be used, the role and use of color. 55 types of graphs are then analyzed, reporting their meaning, use, examples and visual dimensions, also with a vocabulary of graphs and summary tables. Examples are given in Python with the Pandas, Matplotlib, Seaborn and Plotly libraries. Visualization-based inference is discussed, exploratory and confirmatory analysis is defined and the techniques are reported.The text is accompanied by supporting material and it is possible to download the examples and test data.
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