Kirill Dubovikov - Managing Data Science

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Kirill Dubovikov works as a CTO for Cinimex DataLab. He has more than 10 years of experience in architecting and developing complex software solutions for top Russian banks. Now, he leads the compa ny's data science branch. His team delivers practical machine learning applications to businesses across the world. Their solutions cover an extensive list of topics, such as sales forecasting and warehouse planning, natural language processing ( NLP) for IT support centers, algorithmic marketing, and predictive IT operations.

Kirill is a happy father of two boys. He loves learning all things new, reading books, and writing articles for top Medium publications.

Phuong Vo. T.H is a data science manager at FPT Telecom in Vietnam. She graduated with an MSc degree in computer science at Soongsil University, Korea . She has experience in analyzing user behavior and building recommendation or prediction systems for business optimization. She loves to read machine learning and mathematics-related algorithm books, as well as data analysis articles.

Jean-Pierre Sleiman works for BNP Paribas as a project owner in charge of the AI strategy for the retail activities of the Group. His work ranges from delivering data & analytics use cases for the business to co-designing the Group's target operating model for AI. Jean-Pierre has been involved with data analytics & AI projects for more than 3 years, working on several initiatives, such as crafting data strategies and managing the implementation of data products & services within multidisciplinary teams. He was also a lecturer at HEC Paris and cole Polytechnique for MSc Data Science for Business, where he co-created the content of the course and animated the Data science project management and the Business case for data projects lessons.

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Data science and machine learning can transform any organization and open new opportunities. Any data science project is a unique mix of research, software engineering, and business expertise. A substantial managerial effort is needed to guide the solution from prototype development to production. Traditional approaches often fail as they have different conditions and requirements in mind. This book presents a proven approach to data science project management, with tips and best practices to guide you along the way.
With the help of this book, you will learn about the practical applications of data science and AI and will be able to incorporate them into your solutions. You will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a balanced skillful team, and this book will present helpful advice for hiring and growing a skillful data science team for your organization. The book also shows you how you can efficiently manage and improve your data science projects through the use of DevOps.
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