Detalles MARC
| 000 -• Cabecera (Nr) |
|---|
| fixed length control field |
03575nam a22002657a 4500 |
| 005 - • Fecha Y Hora De La Ultima Transaccion (Nr) |
|---|
| control field |
20210723170342.0 |
| 008 - • Elementos de longitud fija (NR) |
|---|
| fixed length control field |
210723b ck ||||| |||| 00| 0 eng d |
| 020 ## - • Número Internacional Normalizado para Libros (ISBN) (R) |
|---|
| ISBN |
9781633694286 |
| 040 ## - • Fuente/Origen de la catalogación (NR) |
|---|
| Agencia Catalogadora |
CO-BoUGC |
| Agencia que realiza la transcripción |
CO-BoUGC |
| 041 ## - • Idiomas (NR) |
|---|
| idioma |
Inglés |
| idioma original |
Inglés |
| 082 ## - • Número de clasificación decimal de Dewey (R) |
|---|
| Número de clasificación (R) |
658.4038 |
| Número del ítem |
H339 |
| 110 ## - • Nombre corporativo (NR) |
|---|
| Corporate name or jurisdiction name as entry element |
Harvard Bussiness Review Press |
| Location of meeting |
Boston, Massachusetts |
| 245 ## - • Titulo propiamente dicho (NR) |
|---|
| Title |
HBR Guide to. Data analytics basics for managers |
| 250 ## - • Mencion de La Edicion (Nr) |
|---|
| Edition statement |
1a ed. |
| 260 ## - • Area De Publicacion, Distribucion, Etc. (Pie de Imprenta) (R) |
|---|
| Place of publication, distribution, etc |
Boston |
| Name of publisher, distributor, etc |
Harvard Business Review Press |
| Date of publication, distribution, etc |
2018 |
| 300 ## - • Descripción física (R) |
|---|
| Extent |
231 pág. |
| Dimensions |
23 cm. x 13 cm. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Getting started: Keep up with your quants: an innumerate's guide to navigating big data / by Thomas H. Davenport. -- A simple exercise to help you think like a data scientist: an easy way to learn the process of data analytics / by Thomas C. Redman.-- Gather the right information: Do you need all that data?: questions to ask for a focused search / by Ron Ashkenas.-- How to ask your data scientists for data and analytics: factors to keep in mind to get the information you need / by Michael Li, Madina Kassengaliyeva, and Raymond Perkins.-- How to design a business experiment: tips for using the scientific method / by Oliver Hauser and Michael Luca.-- Know the difference between your data and your metrics: understand what you're measuring / by Jeff Bladt and Bob Filbin.-- The fundamentals of A/B testing: how it works and mistakes to avoid / by Amy Gallo.-- Can your data be trusted?: gauge whether your data is safe to use / by Thomas C. Redman.-- Analyze the data: A predictive analytics primer: look to the future by looking at the past / by Thomas H. Davenport.-- Understanding regression analysis: evaluate the relationship between variables / by Amy Gallo.-- When to act on a correlation, and when not to: assess your confidence in your findings and the risk of being wrong / by David Ritter.-- Can machine learning solve your business problem?: steps to take before investing in AI / by Anastassia Fedyk.-- A refresher on statistical significance: check if your results are real or just luck / by Amy Gallo.-- Linear thinking in a nonlinear world: a common mistake that leads to errors in judgment / by Bart de Langhe, Stefano Puntoni, and Richard Larrick.-- Pitfalls of data-driven decisions: the cognitive traps to avoid / by Megan MacGarvie and Kristina McElheran.-- Don't let your analytics cheat the truth: always ask for the outliers / by Michael Schrage.-- Communicate your findings: Data is worthless if you don't communicate it: tell people what it means / by Thomas H. Davenport.-- When data visualization works, and when it doesn't: not all data is worth the effort / by Jim Stikeleather.-- How to make charts that pop and persuade: questions to help give your numbers meaning / by Nancy Duarte.-- Why it's so hard for us to communicate uncertainty: illustrating.- and understanding.- the likelihood of events: an interview with Scott Berinato / by Nicole Torres.-- Responding to someone who angrily challenges your data: ensure the data is thorough, then make them an ally / by Jon M. Jachimowicz.-- Decisions don't start with data: influence others through story and emotion / by Nick Morgan |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Fuente del encabezamiento o término |
LEMB |
| Topical term or geographic name as entry element |
GESTIÓN DE LA INFORMACIÓN |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
SISTEMAS DE INFORMACIÓN EN LOS NEGOCIOS |
| Fuente del encabezamiento o término |
LEMB |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
NEGOCIOS POR INTERNET |
| Fuente del encabezamiento o término |
LEMB |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
ANÁLISIS DE REGRESIÓN |
| Fuente del encabezamiento o término |
LEMB |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
GESTIÓN |
| General subdivision |
MÉTODOS ESTADÍSTICOS |
| Fuente del encabezamiento o término |
LEMB |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
TOMA DE DECISIONES |
| General subdivision |
MÉTODOS ESTADÍSTICOS |
| Fuente del encabezamiento o término |
LEMB |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Libro Colección General |
| Oculto en el OPAC |
No |
