An Analyst’s Guide to Business Analytics and Machine Learning with AI

Business analytics

Artificial Intelligence

ISBN:

978-1-959142-89-8

An Analyst’s Guide to Business Analytics and Machine Learning with AI

Applying the Business Analytics Process with Python

Mark Keith, PhD

Anyone can generate a chart or a model with AI in minutes. The harder question is whether the result is strong enough to guide action. This resource teaches students to answer it, building data literacy and model awareness through Python and AI-assisted workflows. Following one process from business question to recommendation, students learn Python fundamentals, then use the CRISP-DM framework to clean data, engineer features, and build and evaluate regression and classification models. They also learn to communicate results, govern analytics responsibly, and check bias and ethics. An end-to-end pipeline project and technical interview preparation give students a portfolio-ready finish.

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Foreword: Why Data Science? Chapter 1: Introduction to Business Analytics Chapter 2: AI in Business Chapter 3: Databases Chapter 4: Data Storage (ERD) Chapter 5: Data Retrieval (SQL) Chapter 6: Data Project Methodology Chapter 7: Introduction to Tableau Chapter 8: Exploratory Data Analysis in Tableau Chapter 9: Modeling in Excel Chapter 10: ML Studio: Introduction to Pipelines Chapter 11: ML Studio: Data Cleaning and Preparation Chapter 12: ML Studio: Algorithm Selection Chapter 13: ML Studio: Selecting the Features Chapter 14: ML Studio: Optimizing Model Fit and Performance Chapter 15: ML Studio: Natural Language Processing Chapter 16: ML Studio: Recommendation Engines Chapter 17: Project: Putting It All Together Supplement: Datasets Supplement: Computer Vision (in progress)

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