Algorithms for Data Science

Nonfiction, Computers, Database Management, Application Software, General Computing
Cover of the book Algorithms for Data Science by Brian Steele, John Chandler, Swarna Reddy, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Brian Steele, John Chandler, Swarna Reddy ISBN: 9783319457970
Publisher: Springer International Publishing Publication: December 25, 2016
Imprint: Springer Language: English
Author: Brian Steele, John Chandler, Swarna Reddy
ISBN: 9783319457970
Publisher: Springer International Publishing
Publication: December 25, 2016
Imprint: Springer
Language: English

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.

This book has three parts:

(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.

(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.

(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.
This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.

This book has three parts:

(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.

(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.

(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.
This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

More books from Springer International Publishing

Cover of the book Young People Re-Generating Politics in Times of Crises by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Improving the Stability of Meshed Power Networks by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) by Brian Steele, John Chandler, Swarna Reddy
Cover of the book The Quality of Democracy in Africa by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Resistance to Anti-Cancer Therapeutics Targeting Receptor Tyrosine Kinases and Downstream Pathways by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Deploying Foresight for Policy and Strategy Makers by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Computer Supported Qualitative Research by Brian Steele, John Chandler, Swarna Reddy
Cover of the book The Ascent of Mary Somerville in 19th Century Society by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Irreversibility and Dissipation in Microscopic Systems by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Structured Object-Oriented Formal Language and Method by Brian Steele, John Chandler, Swarna Reddy
Cover of the book The Ecosystems Revolution by Brian Steele, John Chandler, Swarna Reddy
Cover of the book The Evolution of Psychopathology by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Controller Tuning with Evolutionary Multiobjective Optimization by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Democracy Promotion and the Normative Power Europe Framework by Brian Steele, John Chandler, Swarna Reddy
Cover of the book Reforming Civil-Military Relations in New Democracies by Brian Steele, John Chandler, Swarna Reddy
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy