Data science: the hard parts : techniques for excelling at data science /
Vaughan, Daniel,
Data science: the hard parts : techniques for excelling at data science / Techniques for excelling at data science Daniel Vaughan. - First edition. - Beijing : O'Reilly Media, 2024. - xvi, 237 pages : illustrations, charts ; 24 cm
Includes index.
This hands-on guide offers a set of techniques and best practices that are often missed in conventional data engineering and data science education. A common misconception is that great data scientists are experts in the "bit themes" of the discipline, namely ML and programming. But most of the time, these tools can only take us so far. In reality, it's the nuances within these large themes, and the ability to impact the business, that truly distinguish a top-notch data scientist from an average one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and an exceptional data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.
9781098146474
Electronic data processing.
Big data.
Database management.
Data mining.
Données volumineuses.
Bases de données--Gestion.
Exploration de données (Informatique)
Big data
Data mining
Database management
Electronic data processing
006.312 / VAU
Data science: the hard parts : techniques for excelling at data science / Techniques for excelling at data science Daniel Vaughan. - First edition. - Beijing : O'Reilly Media, 2024. - xvi, 237 pages : illustrations, charts ; 24 cm
Includes index.
This hands-on guide offers a set of techniques and best practices that are often missed in conventional data engineering and data science education. A common misconception is that great data scientists are experts in the "bit themes" of the discipline, namely ML and programming. But most of the time, these tools can only take us so far. In reality, it's the nuances within these large themes, and the ability to impact the business, that truly distinguish a top-notch data scientist from an average one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and an exceptional data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.
9781098146474
Electronic data processing.
Big data.
Database management.
Data mining.
Données volumineuses.
Bases de données--Gestion.
Exploration de données (Informatique)
Big data
Data mining
Database management
Electronic data processing
006.312 / VAU