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"This book is an introduction to the long history of human learning, the environment and sustainable development - about our struggles with the natural world; first for survival, then for dominance, currently for self-preservation, and in future perhaps, even for long-term, mutually-beneficial co-existence. It charts the long arc of human-environment relationships through the specific lens of human learning, putting on record many of the people, ideas and events that have contributed, often unwittingly, to the global movement for sustainable development. Human learning has always had a focus on the environment. It's something we've been engaged in ever since we began interacting with our surroundings and thinking about the impacts, outcomes and consequences of our actions and interactions. This unique story, told by the authors is episodic rather than a connected, linear account; it probes, questions and re-examines familiar issues from novel perspectives, and looks ahead. The book is of particular interest to those studying (and teaching) courses with a focus on socio-economic and environmental sustainability, and NGOs whose work brings them face-to-face with the general public and social enterprises"--
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"Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years there has been an explosion of terms and methods born that automate and improve decision-making and operations. One term called Analytics is an overarching description of a compilation of methodologies. But, AI (Artificial Intelligence), statistics, decision science, optimization which have been around for decades has resurged. Also, things like business intelligence, On-line Analytical Processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology, terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series"--
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Up to 70% and even more of corporate Analytics Efforts fail!!! Even after these corporations have made very large investments, in time, talent, and money, in developing what they thought were good data and analytics programs. Why? Because the executives and decision makers and the entire analytics team have not considered the most important aspect of making these analytics efforts successful. In this Book II of "It's All Analytics!" series, we describe two primary things: 1) What this "most important aspect" consists of, and 2) How to get this "most important aspect" at the center of the analytics effort and thus make your analytics program successful. This Book II in the series is divided into three main parts: Part I, Organizational Design for Success, discusses ....... The need for a complete company / organizational Alignment of the entire company and its analytics team for making its analytics successful. This means attention to the culture - the company culture culture!!! To be successful, the CEO's and Decision Makers of a company / organization must be fully cognizant of the cultural focus on 'establishing a center of excellence in analytics'. Simply, "culture - company culture" is the most important aspect of a successful analytics program. The focus must be on innovation, as this is needed by the analytics team to develop successful algorithms that will lead to greater company efficiency and increased profits. Part II, Data Design for Success, discusses ..... Data is the cornerstone of success with analytics. You can have the best analytics algorithms and models available, but if you do not have good data, efforts will at best be mediocre if not a complete failure. This Part II also goes further into data with descriptions of things like Volatile Data Memory Storage and Non-Volatile Data Memory Storage, in addition to things like data structures and data formats, plus considering things like Cluster Computing, Data Swamps, Muddy Data, Data Marts, Enterprise Data Warehouse, Data Reservoirs, and Analytic Sandboxes, and additionally Data Virtualization, Curated Data, Purchased Data, Nascent & Future Data, Supplemental Data, Meaningful Data, GIS (Geographic Information Systems) & Geo Analytics Data, Graph Databases, and Time Series Databases. Part II also considers Data Governance including Data Integrity, Data Security, Data Consistency, Data Confidence, Data Leakage, Data Distribution, and Data Literacy. Part III, Analytics Technology Design for Success, discusses .... Analytics Maturity and aspects of this maturity, like Exploratory Data Analysis, Data Preparation, Feature Engineering, Building Models, Model Evaluation, Model Selection, and Model Deployment. Part III also goes into the nuts and bolts of modern predictive analytics, discussing such terms as AI = Artificial Intelligence, Machine Learning, Deep Learning, and the more traditional aspects of analytics that feed into modern analytics like Statistics, Forecasting, Optimization, and Simulation. Part III also goes into how to Communicate and Act upon Analytics, which includes building a successful Analytics Culture within your company / organization. All-in-all, if your company or organization needs to be successful using analytics, this book will give you the basics of what you need to know to make it happen.
Artificial intelligence. --- Decision making. --- Visual analytics. --- Big data.
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