
501 Data Science Questions
An essential checklist. Ask the right questions, avoid dead ends, and supercharge your project
No se pudo agregar al carrito
Add to Cart failed.
Error al Agregar a Lista de Deseos.
Error al eliminar de la lista de deseos.
Error al añadir a tu biblioteca
Error al seguir el podcast
Error al dejar de seguir el podcast

Compra ahora por $3.99
No default payment method selected.
We are sorry. We are not allowed to sell this product with the selected payment method
-
Narrado por:
-
Virtual Voice
-
De:
-
Bill Yarberry

Este título utiliza narración de voz virtual
Acerca de esta escucha
501 Data Science Questions overclocks your planning, coding, and analysis of results
Simply by asking relevant questions inspired by the latest tools and algorithms, you’ll improve your project design and avoid pursuing “rabbit holes.” What are your unknown unknowns? Are there important statistical packages, functions or algorithms you have forgotten or never used?
The unknown unknowns
This book does not include answers (otherwise it would be three feet thick). Asking the right questions is the hard part. Once you have nailed down good questions, books and websites with detailed solutions are readily available. What is critical to your success is identifying a) what you already know but have not considered in your analysis and b) the “unknown unknowns”—approaches you are not aware of.
Both newbies and pros benefit from the right questions
Most data science books focus on either beginners or experienced practitioners. Here, everything is thrown in the mix. Newbies will learn from the simple questions and ignore the rest. Deeply experienced practitioners gain from a review of alternatives they may already know but have not brought to bear on their current projects.
With this unique set of questions, you’ll: a) save time b) dramatically expand your awareness of real-world tools and algorithms, and c) assess limitations of models and algorithms.
- Model planning, building, and testing
- First cut sizing of datasets
- Data preparation, transformation, cleansing, and validation
- Data anonymization
- Machine learning/deep learning
- Time series
- Survival analysis
- Text analytics
- Visualization techniques
- Technical infrastructure factors affecting efficiency and run times (real world stuff)
Start Today:
No single action can save more time or improve your results more than asking the right questions and then thinking through all your options. Click the buy now button and get your copy of 501 Data Science Questions.