Applied Business Analytics for the Rest of Us

A Practical Guide to Executing Business Analytics Projects that Create Value

First Edition, 2025

Official Resource Page

Now available on Amazon

**Written and self-published by**
Dr. Alfonso Berumen
Faculty, Pepperdine Graziadio Business School
Academic Affiliate, **Libra Analytics**

Overview:

In a world where data is everywhere but value creation from data projects remains challenging, Applied Business Analytics for the Rest of Us offers a clear, actionable path to turning data into real business value, without needing a PhD or a million-dollar budget.

Written for professionals, entrepreneurs, and students alike, this practical guide demystifies business analytics by focusing on execution over theory. Whether you're leading your first analytics project or looking to better structure your approach, you'll learn how to ask the right questions, analyze data, and deliver insights that drive better decision-making.

Drawing on real-world experience from consulting and teaching graduate-level courses, Dr. Alfonso Berumen provides practical strategies for defining business problems, identifying relevant data, choosing accessible tools, and translating results into strategic recommendations.

Inside you'll find:

Guidance on tools like Python (with examples in Google Colaboratory or Colab), BigQuery (SQL), Google Connected Sheets, and Tableau
Tips for navigating messy data, limited resources, and stakeholder communication
A focus on value creation through storytelling and actionable insights

Table of Contents (PREVIEW):

Foreword

Acknowledgements
Chapter 1 – Frameworks & Management Practices
RBV, Data Culture, CRISP-DM, Pizza KPI metaphor
Chapter 2 – The Business Question
Asking the “right” question using conjecture
Chapter 3 – Hypothesis Testing
Types of analytics, data types, and statistical analysis intro
Chapter 4 – Data Quality & Cleaning
Preprocessing, missing data, outliers
Chapter 5 – No-Code EDA & Visualization
Shark Tank case, Tableau, Google Sheets
Chapter 6 – EDA with Code
Python in Colab, BigQuery, Dataplex profiling
Chapter 7 – The Programmer’s Toolbox
Python (Pandas), SQL, BigQuery
Chapter 8 – Common Statistical Tests
T-tests, Chi-square, ANOVA, correlation (with Shark Tank examples)
Chapter 9 – Machine Learning + Forecasting
Regression, classification, forecasting, AutoML
Chapter 10 – Reporting & Recommendations
Project diary, reporting frameworks, Shark Tank pitch report
Chapter 11 – Long Live the Cloud
Cloud platforms (AWS, Azure, GCP), trends & value
Chapter 12 – Success Tidbits
Skills roadmap: technical, business, soft skills
Chapter 13 – Sample Analytics Project Outline
Step-by-step from business problem to ROI
Chapter 14 – Conclusion & Resources
Books, courses, tools, certifications, learning roadmap
References & About the Author

Authorship Statement

This book is an original work by Dr. Alfonso Berumen, Practitioner Faculty of Decision Sciences at Pepperdine University and Academic Affiliate at Libra Analytics LLC. The book is self-published and distributed directly via Amazon. All content, structure, and visualizations are the intellectual property of the author; all external materials or resources are properly cited.

jump into the data (shark Tank)!

Link to Data Used in Applied Data Analysis Chapters

Thirumani, S. (2025). Shark Tank US dataset (Version 1) [Data set]. Kaggle. https://www.kaggle.com/datasets/thirumani/shark-tank-us-dataset

Slides & FileS:

YouTube Playlist for chapters/modules:

Playlist

About The Author:

Dr. Alfonso Berumen has over 15 years of experience in consulting where he has provided data-driven economic, statistical, and analytical support to law firms, Fortune 500 companies, government agencies, and private companies operating in a wide range of industries. He is currently an Academic Affiliate with Libra Analytics and full-time Practitioner of Decision Sciences at the Graziadio Business School at Pepperdine University. His teaching and research interests include Business Analytics and Business Intelligence.

He holds a Doctorate in Business Administration from Pepperdine University, Masters in Predictive Analytics from Northwestern University, Masters in Business Administration from the University of California, Irvine and a Bachelor of Arts in Economics from Occidental College.