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Название: Data Smart: Using Data Science to Transform Information into Insight, 2nd Edition
Автор: Jordan Goldmeier
Издательство: Wiley
Год: 2024
Страниц: 445
Язык: английский
Формат: pdf (true)
Размер: 33.6 MB
A straightforward and engaging approach to Data Science that skips the jargon and focuses on the essentials. In the newly revised second edition of Data Smart: Using Data Science to Transform Information into Insight, accomplished data scientist and speaker Jordan Goldmeier delivers an approachable and conversational approach to data science using Microsoft Excel’s easily understood features. The author also walks readers through the fundamentals of statistics, machine learning and powerful Artificial Intelligence (AI) concepts, focusing on how to learn by doing. Data Science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products. At the end of this book, I’ll show you how to implement what we’ve built in Excel in R. In fact, this follows my own path in building data science tools for companies. First, I would lay out my ideas in Excel. Use the spreadsheet as a way to validate my ideas and make sure I understand exactly what the algorithms do. Then, usually, when I’m ready, I move it to R or Python.
Автор: Jordan Goldmeier
Издательство: Wiley
Год: 2024
Страниц: 445
Язык: английский
Формат: pdf (true)
Размер: 33.6 MB
A straightforward and engaging approach to Data Science that skips the jargon and focuses on the essentials. In the newly revised second edition of Data Smart: Using Data Science to Transform Information into Insight, accomplished data scientist and speaker Jordan Goldmeier delivers an approachable and conversational approach to data science using Microsoft Excel’s easily understood features. The author also walks readers through the fundamentals of statistics, machine learning and powerful Artificial Intelligence (AI) concepts, focusing on how to learn by doing. Data Science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products. At the end of this book, I’ll show you how to implement what we’ve built in Excel in R. In fact, this follows my own path in building data science tools for companies. First, I would lay out my ideas in Excel. Use the spreadsheet as a way to validate my ideas and make sure I understand exactly what the algorithms do. Then, usually, when I’m ready, I move it to R or Python.