TY  - BOOK
AU  - Vaughan,Daniel
TI  - Data science: the hard parts: techniques for excelling at data science 
SN  - 9781098146474
U1  - 006.312 23
PY  - 2023///
CY  - Sebastopol, CA
PB  - O'Reilly Media
KW  - Electronic data processing
KW  - Big data
KW  - Database management
KW  - Data mining
KW  - Données volumineuses
KW  - Bases de données
KW  - Gestion
KW  - Exploration de données (Informatique)
KW  - fast
N1  - "November 2023: First Edition"--Title page verso; Includes index; Data analytics techniques --; So what? Creating value with data science --; Metrics design --; Growth decompositions: understanding tailwinds and headwinds --; 2x2 designs --; Building business cases --; What's in a lift? --; Narratives --; Datavis: choosing the right plot to deliver a message --; Machine learning --; Simulation and bootstrapping --; Linear regression : going back to basics --; Data leakage --; Productionizing models --; Storytelling in machine learning --; From prediction to decisions --; Incrementality: the holy grail of data science? --; A/B tests --; Large language models and the practice of data science
N2  - 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
ER  -