***"This is an important book on an important topic. I particularly like the examples showing different visualizations of the same data and the parallel presentation of graphics and code. And I absolutely love the chapter on visual storytelling. I can't wait to use this book in my classes."***- Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University, New York"A book that gives learners the inspiration, knowledge and worked exampl...

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.This book looks at both classical and ...

Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms

A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable modelsKey FeaturesLearn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have...

A clear easy-to-read guide to presenting your message using statistical dataPoor presentation of data is everywhere; basic principles are forgotten or ignored. As a result, audiences are presented with confusing tables and charts that do not make immediate sense. This book is intended to be read by all who present data in any form.The author, a chartered statistician who has run many courses on the subject of data presentation, presents numerous examples al...

The 5th edition of Model Building in Mathematical Programming discusses the general principles of model building in mathematical programming and demonstrates how they can be applied by using several simplified but practical problems from widely different contexts. Suggested formulations and solutions are given together with some computational experience to give the reader a feel for the computational difficulty of solving that particular type of model. Furthermore, this book illus...

A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesInterpret real-world data, including cardiovascular disease data and the COMPAS recidivism scoresBuild your interpretability toolkit with global, lo...

The economic and political situation of cities has shifted in recent years in light of rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization. At the same time, the way that data are used to understand urban systems has changed dramatically.Urban Analytics offers a field-defining look at the challenges and opportunities of using new and emerging data to study contemporary and future cities through methods including GIS, Rem...

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The third edition of this popular guide expands its practical foundations in R and Python into the modern AI toolkit, with new chapters on neural networks, deep learning, and large language models. Generative AI is integrated throughout, showing how tools such as ChatGPT, Claude, and Gemin...

Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises. Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesChallenge yourself to start thinking like a Kaggle GrandmasterFill your portfolio with impressive case studies that will come in handy during interviewsPacked with exercises and notes pages for you to enha...

The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil. But data is useless without the analysis, interpretation and innovations of data scientists.With Confident Data Science*,* learn the essential skills and build your confidence in this sector through key insights and practical tools for success. In this book, you will discover all of the skills you need to understand this discip...

This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in...