ZURICH & AUSTIN, Texas--(BUSINESS WIRE)--KNIME, 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:
Introduction: Overview of core ideas of data science and its motivation.
Practical Data Analysis – An Example: Typical pitfalls encountered when analyzing real-world data.
Project Understanding: Methods to map problems to one or many data analysis tasks.
Data Understanding: General data insights for furthering the data analysis process.
Principles of Modeling: Issues that are inherent to all the methods for analyzing the data.
Data Preparation: Modeling techniques to extract models from the data.
Finding Patterns: Methods to summarize, describe or explore the data set as a whole.
Finding Explanations: Methods to identify an unknown dependency within the data.
Finding Predictors: Methods to construct predictors for class labels or numeric target attributes.
- Evaluation and Deployment: Deployment models for production to be used by other applications or business analysts.
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
- Michael R. Berthold is a professor of bioinformatics and information mining at the University of Konstanz, Germany, and CEO/co-founder of KNIME AG, Zurich, Switzerland.
- Christian Borgelt is a professor of data science at the University of Salzburg, Austria.
- Frank Höppner is a professor of information systems at Ostfalia University of Applied Sciences, Germany.
- Frank Klawonn is a professor in the department of computer science and head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany, as well as head of the bioinformatics and statistics group at the Helmholtz Centre for Infection Research in Braunschweig, Germany.
- Rosaria Silipo is a principal data scientist and head of evangelism at KNIME AG, Zurich, Switzerland.
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