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. Second of a series of books, it covers methodological aspects, analysis and visualization. It describes the CRISP DM methodology, the working phases, the success criteria, the languages and the environments that can be used, the application libraries. Since this book uses Orange for the application aspects, its installation and widgets are described. In visualization, historical notes are made, and next the book describes the characteristics of an effective visualization, the types of messages that can be conveyed, the Grammar of Graphics, the use of a graph and a dashboard, the software and libraries that can be used, the role and use of color. 55 types of graphs are then analyzed, reporting meaning, use, examples and visual dimensions also with a vocabulary of graphs and summary tables. Examples are given in Orange and the possible use of Python with Orange is explained. Visualization-based inference is discussed, exploratory and confirmatory analysis is defined and techniques are reported. The book is accompanied by supporting material and it is possible to download the project samples in Orange and sample data.

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. Third of a series of books, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. Since this text uses Orange for the application aspects, it describes its installation and widgets. Then it considers the concept of model, its life cycle and the relationship with measures and metrics. The measures of localization, dispersion, asymmetry, correlation, similarity, distance are then described. The test and score metrics used in machine learning, those relating to texts and documents, the association metrics between items in a shopping cart, the relationship between objects, similarity between sets and between graphs, similarity between time series are considered. As a preliminary activity to the modeling phase, the Exploration Data Analysis is deepened in terms of questions, process, techniques and types of problems. For each type of problem, the recommended graphs, the methods of interpreting the results and their implementation in Orange are considered. The text is accompanied by supporting material and you can download the samples in Orange and the test data.

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. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Deep Learning techniques are described considering the architectures of the Perceptron, Neocognitron, the neuron with Backpropagation and the activation functions, the Feed Forward Networks, the Autoencoders, the recurrent networks and the LSTM and GRU, the Transformer Neural Networks, the Convolutional Neural Networks and Generative Adversarial Networks and analyzed the building blocks. Regularization techniques (Dropout, Early stopping and others), visual design and simulation techniques and tools, the most used algorithms and the best known architectures (LeNet, VGGnet, ResNet, Inception and others) are considered, closing with a set of practical tips and tricks. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

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. First of a series of books, it covers methodological aspects, data acquisition, management and cleaning. It describes the CRISP DM methodology, the working phases, the success criteria, the languages and the environments that can be used, the application libraries. Since this book uses Orange for the application aspects, its installation and widgets are described. Dealing with data acquisition, the book describes data sources, the acceleration techniques, the discretization methods, the security standards, the types and representations of the data, the techniques for managing corpus of texts 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. Examples are given in Orange. Data quality dimensions are analysed, and then the book considers algorithms for entity identification, truth discovery, rule-based cleaning, missing and repeated value handling, categorical value encoding, outlier cleaning, and errors, inconsistency management, scaling, integration of data from various sources and classification of open sources, application scenarios and the use of databases, datawarehouses, data lakes and mediators, data schema mapping and the role of RDF, OWL and SPARQL, transformations. Examples are given in Orange. The book is accompanied by supporting material and it is possible to download the project samples in Orange and sample data.

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. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

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. Part of a series of books, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. Since this text uses Orange for the application aspects, it describes its installation and widgets. Then it considers the concept of model, its life cycle and the relationship with measures and metrics. The data modeling phase is considered from the point of view of machine learning by deepening the types of machine learning, the types of models, the types of problems and the types of algorithms. After considering the ideal characteristics of models and algorithms, a vocabulary of the types of models and algorithms is compiled and their use in Orange is considered through two supervised and unsupervised projects respectively. The text is accompanied by supporting material and you can download the samples in Orange and the test data.

Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.