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'Guide to Intelligent Data Science; How to Intelligently Make Use of Real Data' Textbook Now AvailableKNIME, an open source data analytics company, today announced the availability of "Guide to Intelligent Data Science; How to Intelligently Make Use of Real Data," authored by academic and industry experts: Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn and Rosaria Silipo. The textbook, published by Springer, is written for advanced undergraduates, graduate students, and professionals facing data science problems. More details are available at www.datascienceguide.org. Supporting this practical and systematic textbook, KNIME produced supplemental teaching materials from the "Machine Learning and AI for Data Science" lecture series taught by professor Michael Berthold at the University of Konstanz, one of the Universities of Excellence in Germany. The materials, in the form of slides, are assembled into 19 topics that can be combined to cover 13 lessons of 90 minutes each examining the basic principles of machine learning, decision trees, regressions, ensemble learning, clustering, neural networks, deep learning, support vector machines, recommendation engines, data visualization, deployment and much more. The complimentary teaching materials are available for download, reuse and adaptation at www.datascienceguide.org/teaching-material.html. This 2nd edition "Guide to Intelligent Data Science; How to Intelligently Make Use of Real Data" covers the entire data science life cycle, from data access and preparation to modeling, visualization and deployment. It supplies a broad range of perspectives on data science, providing readers with an expanded account of the field and major updates on techniques and subject coverage (including deep learning). While presenting a focus on practical aspects, the textbook details the underlying theory. It emphasizes common pitfalls that often lead to incorrect or insufficient analyses and helps practitioners avoid such errors. Lastly, it adds extensive hands-on examples, enabling readers to gain further insight into the topic. The textbook and supplemental teaching materials cover ten chapters, including:
Since classical statistics encompass many data analysis methods, the textbook provides an appendix of basic statistics, including descriptive statistics, inferential statistics, and fundamentals from probability theory. Note: Instructors can request a free instructor sample as an e-book from Springer at www.springer.com/gp/book/9783030455736 Meet the Authors
About KNIME KNIME, an open source data analytics company, provides software for fast and intuitive access to advanced data science. At the core is the open source KNIME Analytics Platform, a visual workbench providing a wide range of state-of-the-art analytics tools and techniques to handle any use case - from basics to highly advanced. It is complemented by the commercial KNIME Server which makes data science productive in the enterprise, while staying in the same software environment for deployment, collaboration, management and optimization. Headquartered in Zurich, KNIME has offices in Austin, Texas, and Konstanz and Berlin, Germany. Learn more at www.knime.com. KNIME, KNIME Analytics Platform, and KNIME Server are trademarks of KNIME. All other brand names and product names are trademarks or registered trademarks of their respective companies. Tags: KNIME, open source, data science, data analytics, machine learning, deep learning, artificial intelligence, AI, KNIME Analytics Platform, KNIME Server, textbook, teaching materials
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