Description

Book Synopsis
Written by Matt Taddy, successful author of the McGraw Hill Professional title, Business Data Science graduate of University of Chicago and Amazon Chief Economist. This new higher-ed text takes a practical, modern approach to data science and business analytics for the graduate-level business analytics student or professional. It takes a learn-by-doing approach, with real data analysis examples that explain the why, rather than the what in the decision-making discussions. It uses R as the primary technology throughout the text and includes an end-of-chapter reference to the basic R recipes in each chapter. The text uses tools from economics and statistics in combination with Machine Learning Techniques to create a platform for using data to make decisions.

The Connect product that supports the text includes Interactive Activities that have students explore content more deeply, Excel activities like Integrated Excel & Applying Excel, and a Prep

Table of Contents
Chapter 1: Regression
Chapter 2: Uncertainty Quantification
Chapter 3: Regularization and Selection
Chapter 4: Classification
Chapter 5: Causal Inference with Experiments
Chapter 6: Causal Inference with Controls
Chapter 7: Trees and Forests
Chapter 8: Factor Models
Chapter 9: Text as Data
Chapter 10: Deep Learning
Appendix: R Primer

Modern Business Analytics ISE

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£53.99

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RRP £59.99 – you save £6.00 (10%)

Order before 4pm tomorrow for delivery by Mon 5 Jan 2026.

A Paperback / softback by Matt Taddy

15 in stock


    View other formats and editions of Modern Business Analytics ISE by Matt Taddy

    Publisher: McGraw-Hill Education
    Publication Date: 09/05/2022
    ISBN13: 9781266108334, 978-1266108334
    ISBN10: 1266108335
    Also in:
    Business, Finance & Law Business and Management

    Description

    Book Synopsis
    Written by Matt Taddy, successful author of the McGraw Hill Professional title, Business Data Science graduate of University of Chicago and Amazon Chief Economist. This new higher-ed text takes a practical, modern approach to data science and business analytics for the graduate-level business analytics student or professional. It takes a learn-by-doing approach, with real data analysis examples that explain the why, rather than the what in the decision-making discussions. It uses R as the primary technology throughout the text and includes an end-of-chapter reference to the basic R recipes in each chapter. The text uses tools from economics and statistics in combination with Machine Learning Techniques to create a platform for using data to make decisions.

    The Connect product that supports the text includes Interactive Activities that have students explore content more deeply, Excel activities like Integrated Excel & Applying Excel, and a Prep

    Table of Contents
    Chapter 1: Regression
    Chapter 2: Uncertainty Quantification
    Chapter 3: Regularization and Selection
    Chapter 4: Classification
    Chapter 5: Causal Inference with Experiments
    Chapter 6: Causal Inference with Controls
    Chapter 7: Trees and Forests
    Chapter 8: Factor Models
    Chapter 9: Text as Data
    Chapter 10: Deep Learning
    Appendix: R Primer

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