Category Archives: Books

Books worth a look

Book Memo: “Presto: The Definitive Guide”

06 Saturday Jul 2019

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SQL at Any Scale, on Any Storage, in Any Environment
Perform fast interactive SQL analytics against different data sources using the Presto distributed SQL query engine. With this practical book, you’ll learn how to conduct analytics on data where it lives, including Hive, Cassandra, relational databases, and proprietary data stores. Matt Fuller from Starburst Data and Presto cocreator Martin Traverso show analysts how to manage, use, and even develop with Presto. Initially developed by Facebook, open source Presto is now used by Netflix, Airbnb, LinkedIn, Twitter, Uber, and many other companies. You’ll learn how a single Presto query can combine data from multiple sources to allow for analytics across your entire organization.

Book Memo: “Graph Algorithms”

05 Friday Jul 2019

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Practical Examples in Apache Spark and Neo4j
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they’re used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns – from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics.

Book Memo: “The Experience-Centric Organization”

27 Thursday Jun 2019

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How to Win Through Customer Experience
Is your organization prepared for the next paradigm of customer experience? Or will you be left behind? This practical book will make you a winner in a market driven by experience and enable you to develop desirable offerings and standout service and attract loyal customers. Author Simon Clatworthy shows you how to transform into an organization that aligns your customers’ experiential journey with platforms, organizational structures, and strategic alliances. Rather than treat customer experience as an add-on to product and service design, you’ll discover how experience-centricity can drive the whole organization.

Book Memo: “Mathematical Theories of Machine Learning”

17 Monday Jun 2019

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Theory and Applications
This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.

Book Memo: “Managing Your Data Science Projects”

16 Sunday Jun 2019

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Learn Salesmanship, Presentation, and Maintenance of Completed Models
At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way.

Book Memo: “Applied Data Science”

14 Friday Jun 2019

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Lessons Learned for the Data-Driven Business
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other.

Book Memo: “Probability and Statistics”

07 Friday Jun 2019

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Inference, Causal and Stochastic Analysis
STATISTICS (Statistical Science) can be briefly described as the science of problem solving and decision making based on data, being observed in all the sciences of humanity, in governments and corporate bodies. Statistical methods essentially reveal important information and knowledge in data sets monitored in application domains such as quality and process control, business governance, supply chain management, urban traffic management, industrial manufacturing and public health. DATA ANALYTICS is a series of texts that provide the essential concepts, statistical methods and practical approaches for problems in sciences, engineering and technology. The main point is to develop Statistical Science-based solutions in specific areas of engineering and technology, in the context of sustainable economic development. The book is written to honestly convey basic probabilistic models and statistical methods to readers in sectors of computing, industry, engineering, production, management, environmental and actuarial sciences. Hopefully the book is a good support for students and professionals, who are finding practical solutions, or making optimal decisions using actual observed data.

Book Memo: “Foundations of Data Science”

03 Monday Jun 2019

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A Practical Introduction to Data Science with Python
Data science underlies Amazon’s product recommender, LinkedIn’s People You Know feature, Pandora’s personalized radio stations, Stripe’s fraud detectors, and the incredible insights arising from the world’s increasingly ubiquitous sensors. In the future, the world’s most interesting and impactful problems will be solved with data science. But right now, there’s a shortage of data scientists in every industry, traditional schools can’t teach students fast enough, and much of the knowledge data scientists need remains trapped in large tech companies.
This comprehensive, practical tutorial is the solution. Drawing on his experience building Zipfian Academy’s immersive 12-week data science training program, Jonathan Dinu brings together all you need to teach yourself data science, and successfully enter the profession.
First, Dinu helps you internalize the data science ‘mindset’: that virtually anything can be quantified, and once you have data, you can harvest amazing insights through statistical analysis and machine learning. He illuminates data science as it really is: a holistic, interdisciplinary process that encompasses the collection, processing, and communication of data: all that data scientists do, say, and believe.

Book Memo: “Big Data Science and Analytics for Smart Sustainable Urbanism”

02 Sunday Jun 2019

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Unprecedented Paradigmatic Shifts and Practical Advancements
We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power – manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development.

Book Memo: “Data Science for Supply Chain Forecast”

31 Friday May 2019

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Data Science for Supply Chain Forecast is a book for practitioners focusing on data science and machine learning; it demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. As one will discover in this book, artificial intelligence (AI) & machine learning (ML) are not simply a question of coding skills. Using data science in order to solve a problem requires a scientific mindset more than coding skills. The story behind these models is one of experimentation, of observation and of constant questioning; a true scientific method must be applied to supply chain. In the data science field as well as that of the supply chain, simple questions do not come with simple answers. In order to resolve these questions, one needs to be both a scientist as well as to use the correct tools. In this book, we will discuss both.