📚Winning with Data Science — a guide for business leaders in the digital age
Authors: Howard Steven Friedman and Akshay Swaminathan
🔍 What is the book about?
The book Winning with Data Science is not about writing Python code or building neural networks. It is about how businesses can get real value from data science projects, even if you are not a techie.
The authors explain how to be a competent “customer” of analytical solutions: ask the right questions, understand the stages of a data science project, participate in the formation of requirements and evaluation of results.
💡 Key ideas of the book:
✅Data Science ≠ magic. It is a tool for solving specific business problems, and not a reason to chase hype.
✅The role of business is key. The business customer must clearly understand the problem, constraints, and desired outcome.
✅It is important to ask the right questions. Not "which algorithm is better?", but "will this help achieve the goal on time and on budget?"
✅Technical skills are not required. But basic knowledge of data, model types, storage, qualities, and capabilities is desirable.
✅Ethics is not the least important. Models should not reinforce biases or discriminate.
🧠 Who will benefit from this:
✅Managers implementing analytics
✅Product managers and startup founders
✅Marketers and CFOs
✅Anyone who wants to effectively interact with data science teams
🤔General conclusion:
This book is a bridge between business and analysts. Without an overload of terms, but with a deep understanding of the processes.
Authors: Howard Steven Friedman and Akshay Swaminathan
🔍 What is the book about?
The book Winning with Data Science is not about writing Python code or building neural networks. It is about how businesses can get real value from data science projects, even if you are not a techie.
The authors explain how to be a competent “customer” of analytical solutions: ask the right questions, understand the stages of a data science project, participate in the formation of requirements and evaluation of results.
💡 Key ideas of the book:
✅Data Science ≠ magic. It is a tool for solving specific business problems, and not a reason to chase hype.
✅The role of business is key. The business customer must clearly understand the problem, constraints, and desired outcome.
✅It is important to ask the right questions. Not "which algorithm is better?", but "will this help achieve the goal on time and on budget?"
✅Technical skills are not required. But basic knowledge of data, model types, storage, qualities, and capabilities is desirable.
✅Ethics is not the least important. Models should not reinforce biases or discriminate.
🧠 Who will benefit from this:
✅Managers implementing analytics
✅Product managers and startup founders
✅Marketers and CFOs
✅Anyone who wants to effectively interact with data science teams
🤔General conclusion:
This book is a bridge between business and analysts. Without an overload of terms, but with a deep understanding of the processes.