Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results Key FeaturesMaster the concepts of statistics and ML to interpret models and guide decisionsIdentify valuable AI use cases and manage data science projects from start to finishEmpower top data science teams to solve complex problems and build AI productsPurchase of the print Kindle book includes a free PDF eBookBook DescriptionAs data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI.

This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently.

By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements.

By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems. What you will learnDiscover how to interpret common statistical quantities and make data-driven decisionsExplore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learningFind out how to evaluate statistical and machine learning modelsUnderstand the data science lifecycle, from development to monitoring of models in productionKnow when to use ML, statistical modeling, or traditional BI methodsManage data teams and data science projects effectivelyWho this book is forThis book is designed for executives who want to understand and apply data science methods to enhance decision-making.

It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers. Table of ContentsIntroducing Data ScienceCharacterizing and Collecting DataExploratory Data AnalysisThe Significance of SignificanceUnderstanding RegressionIntroducing Machine LearningSupervised Machine LearningUnsupervised Machine LearningInterpreting and Evaluating Machine Learning ModelsCommon Pitfalls in Machine LearningThe Structure of a Data Science ProjectThe Data Science TeamManaging the Data Science TeamContinuing Your Journey as a Data Science Leader.