The Basics Of Working With Knime Analytics Platform
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The basics of working with KNIME Analytics Platform
Author: Barbora Štětinová
language: en
Publisher: Barbora Štětinová
Release Date: 2024-01-01
Unlock the power of data with KNIME and transform your analytics skills - no programming required! In a world driven by data, mastering the right tools can be your greatest advantage. The Basics of Working with KNIME Analytics Platform is your ultimate guide to navigating and harnessing the full potential of KNIME, one of the most powerful and user-friendly platforms for data analysis and machine learning. Written by data analytics expert Barbora Štětinová, this comprehensive guide simplifies complex data processes through hands-on examples and practical workflows. Whether you're a business analyst, a data science enthusiast, or a professional looking to automate your data tasks, this book equips you with the knowledge to transform data into actionable insights. What You'll Learn: Effortless Data Preparation: Load, filter, and transform data with simple drag-and-drop nodes, eliminating the need for coding.Powerful Data Manipulation: Master techniques for data joining, aggregation, and cleaning to make your data analysis efficient and accurate.Insightful Data Visualization: Create compelling charts and dashboards to communicate your findings effectively.Machine Learning Made Easy: Build predictive models and deploy them using KNIME's intuitive interface, complete with real-world examples.Best Practices and Tips: Discover expert tips for optimizing your workflows and learn how to handle common challenges in data analytics. With easy-to-follow explanations, step-by-step guides, and engaging examples, this book turns beginners into proficient KNIME users and empowers experienced professionals to expand their analytical capabilities. By the end, you'll be able to create, evaluate, and deploy sophisticated data workflows that drive value and insight across your projects. Get ready to revolutionize your data analytics journey with KNIME!
Basics in Business Informatics
This book takes you on a journey into the world of business informatics. It has a modular structure and covers the key aspects of business informatics. Besides the thematic introductions, each chapter includes excursuses, review questions, and practical exercises, for which solutions are provided in a separate chapter. The book concludes with two teaching cases on digital transformation. It is designed for students and lecturers at universities and technical colleges, but also as a resource for IT trainings.
Guide to Intelligent Data Science
Author: Michael R. Berthold
language: en
Publisher: Springer Nature
Release Date: 2020-08-06
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.