Data Science for Managers

In today's world of Big Data, data scientists must stay ahead of the game, especially in environments that are increasingly unstructured and semi-structured. They need fresh skills to navigate these complex environments. It is also important to question whether Big Data analytics fundamentally differs from Small Data analytics. In Data Science for Managers, industry expert Richard Boire breaks down the vast field of data science into easy-to-understand insights. Using real-world examples from leading global companies, this book provides a clear, step-by-step guide to help business managers harness the power of data science. Boire's 4-step process walks you through deciding when to use data science, implementing it, and making sense of the results to improve customer ROI and business outcomes. Packed with engaging stories and case studies, this work also highlights potential pitfalls in data science and shares strategies for avoiding them. This updated edition focuses on new developments related to AI and machine learning, with guidance for data managers on how to use this technology in developing business solutions. Data Science for Managers is a book for marketers, IT professionals, analysts, and anyone else who wants to transform mountains of data into actionable insights and effective business strategies. With the rise of AI and cutting-edge technology, it equips you with the tools you need to navigate this new landscape and turn complex data into tangible results. Whether you're new to data science or looking to sharpen your skills, this book is your guide to unlocking the full potential of big data. Richard Boire is a seasoned professional, industry visionary, and leader with 30+ years' experience in developing and implementing data Science/Analytics solutions across all industry sectors. As President of Boire Analytics, he offers analytical and data management services to companies seeking solutions to their existing predictive analytics or database/digital marketing challenges. He has taught applied statistics, data mining and database marketing at a variety of institutions across Canada which include University of Toronto, George Brown College, and Seneca College.