Motivation

This book aims at helping business school students move beyond the spreadsheet and gain a basic understanding of data science, including data literacy and techniques such as data curing, regression models, prediction and analysis while learning some necessary coding in R.

This book also aims at helping business school students:

Recognize the business challenges in an organization that can be addressed using data science to make assumptions, interpret results, and formulate actionable recommendations.
Leverage data science to improve your decision-making skills and learn how to identify and avoid common errors while interpreting datasets, metrics and visualizations.
Understand widely applicable methodologies in statistics, data analytics, and data science—together with necessary coding skills to apply them.

Do you have confidence in the quality of your data? Can you use your data to tell a compelling story and motivate or inform your business decision-making?

Yesterday’s data is tomorrow’s forecast. Today, we are faced with numerous questions that can be resolved using data: should you invest in this new product? Does your employee incentive programme work? Your sales projections are accurate? Flawed or incomplete data can lead you in the wrong direction. Data Science for Business will teach you how to think beyond the spreadsheet and use data effectively to address your business decisions, becoming a more substantial individual contributor and manager.

This book provides a hands-on approach to demystifying the data science ecosystem and making you a more conscientious consumer of information. Starting with the questions you need to ask when you use data for decision-making, this course will help you know when to trust your data and how to interpret the results.

By the end of the book, you should understand how to create a data-driven framework for your organization or yourself; develop visualization hypotheses and insights; identify data errors or missing components; and speak the language of data science across topics such as forecasting, linear regressions, and machine learning to better guide your team to long-term success.

You will learn how to create a compelling story that uses proven, collected data to make core business decisions, and explore coding environments such as R and visualization software.

For this textbook, you will need to have R installed on your machine or have created an account on www.rstudio.cloud.

To reference this book:

Thierry Warin. 2020. Foundations of Quantitative Analysis for International Business with R. https://warin.ca/fqaibr/.