“…Automate data and model pipelines for faster machine learning applications About This Book Build automated modules for different machine learning components Understand each component of a machine learning pipeline in depth Learn to use different open source AutoML and feature engineering platforms Who This Book Is For If you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. …”

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“…If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. …”

Tabla de Contenidos: “…Inserting PivotCharts -- Inserting picture, shapes, and icons -- Building the sales performance dashboard -- Creating measures -- Inserting slicers and timelines -- Inserting a PivotTable and a PivotChart -- Inserting shapes and a picture -- Connecting slicers to the PivotTables and PivotCharts -- Building the supply chain inventory dashboard -- Summary -- Chapter 12: Best Practices for Real-World Dashboard Building -- Gathering the dashboard requirements -- Existing established analysis dashboards -- Newly established analysis dashboards -- Ad hoc analysis dashboards -- An overview of different data professionals -- Data analyst -- Business intelligence analyst -- Data engineer -- Data scientist -- Database administrator -- Advantages and limitations of Excel dashboards -- Summary -- Index -- Other Books You May Enjoy…”

“…Demonstrate your Data Science skills by earning the brand-new CompTIA DataX credential In CompTIA DataX Study Guide: Exam DY0-001, data scientist and analytics professor, Fred Nwanganga, delivers a practical, hands-on guide to establishing your credentials as a data science practitioner and succeeding on the CompTIA DataX certification exam. …”

Tabla de Contenidos: “…Background in theology, philosophy and science. 1. Scientists and beliefs / Christian de Duve. 2. Evolution and intelligent design. …”
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Tabla de Contenidos: “…AtrapaelTigre.com : enlisting citizen-scientists in the war on tiger mosquitoes / Aitana Oltra, John R.B. …”

“…Jahrhundert bis in die Gegenwart: Bilder vom Frosch im Laborexperiment bis zu den Phantasiewelten der Nanotechnologie, denen im Spielfilm und anderen populären Medien die Bilder des ›mad scientist‹ wie Frankenstein und Dr. Caligari, aber auch des Fortschritts im Hochtechnologielabor gegenüberstehen. …”

“…Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages Understand how to apply useful data analysis techniques in R for real-world applications An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. …”

“…It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. …”

“…Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This Book This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way Work with different types of datasets, and reshape the layout of your data to make it easier for analysis Get simple examples and real-life data wrangling solutions for data pre-processing Who This Book Is For If you are a data scientist, data analyst, or a statistician who wants to learn how to wrangle your data for analysis in the best possible manner, this book is for you. …”

“…This book is ideal for data scientists, analysts, and engineers; software and machine learning engineers; and system administrators. …”

“…With a Preface by noted satellite scientist Dr. Ahmad Ghais, the second edition reflects the expanded user base for this technology by updating information on historic, current, and planned commercial and military satellite systems and by expanding sections that explain the technology for non-technical professionals. …”

“…If you are an engineer or scientist who wants to create great visualizations with Python, rather than yet another specialized language, this is the book for you. …”

“…Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Foreword -- Acknowledgments -- Chapter 1: A Value-Centric Perspective Towards Analytics -- Introduction -- Business Analytics -- Profit-Driven Business Analytics -- Analytics Process Model -- Analytical Model Evaluation -- Analytics Team -- Profiles -- Data Scientists -- Conclusion -- Review Questions -- Multiple Choice Questions -- Open Questions -- References -- Chapter 2: Analytical Techniques -- Introduction -- Data Preprocessing -- Denormalizing Data for Analysis -- Sampling -- Exploratory Analysis -- Missing Values -- Outlier Detection and Handling -- Principal Component Analysis -- Types of Analytics -- Predictive Analytics -- Introduction -- Linear Regression -- Logistic Regression -- Decision Trees -- Neural Networks -- Ensemble Methods -- Bagging -- Boosting -- Random Forests -- Evaluating Ensemble Methods -- Evaluating Predictive Models -- Splitting Up the Dataset -- Performance Measures for Classification Models -- Performance Measures for Regression Models -- Other Performance Measures for Predictive Analytical Models -- Descriptive Analytics -- Introduction -- Association Rules -- Sequence Rules -- Clustering -- Survival Analysis -- Introduction -- Survival Analysis Measurements -- Kaplan Meier Analysis -- Parametric Survival Analysis -- Proportional Hazards Regression -- Extensions of Survival Analysis Models -- Evaluating Survival Analysis Models -- Social Network Analytics -- Introduction -- Social Network Definitions -- Social Network Metrics -- Social Network Learning -- Relational Neighbor Classifier -- Probabilistic Relational Neighbor Classifier -- Relational Logistic Regression -- Collective Inferencing -- Conclusion -- Review Questions -- Multiple Choice Questions -- Open Questions -- Notes -- References -- Chapter 3: Business Applications -- Introduction…”