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Business Analytics and Machine Learning: A Practical Overview in Tableau, Excel, and Microsoft ML Studio (classic)

Data Analytics

ISBN #978-1-952023-09-5

Business Analytics and Machine Learning: A Practical Overview in Tableau, Excel, and Microsoft ML Studio (classic)

Mark Keith, PhD

In today’s rapidly changing IT environment, organizations use a variety of technologies and tools to create machine learning environments and perform data analytics. This resource provides a practical introduction to performing exploratory data analytics to create machine learning pipelines using a combination of Tableau, Excel, and Azure ML Studio. Students will learn common practices in each phase of the CRISP-DM methodology.

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Course Objective

This resource offers a practical introduction to exploratory data analytics and machine learning pipeline creation using Tableau, Excel, and Microsoft ML Studio, while teaching common practices in each phase of CRISP-DM.

Table of Contents

Foreword: Why Data Science?
Chapter 1: Introduction to Business Analytics
Chapter 2: AI in Business
Chapter 3: Data Mining Project Methodology
Chapter 4: Visualization: Theory and Design
Chapter 5: Tableau: Core Features
Chapter 6: Tableau: Exploratory Data Analysis
Chapter 7: Tableau: Clustering
Chapter 8: Excel: Multivariate Prediction
Chapter 9: AMLS: Introduction to Pipelines
Chapter 10: AMLS: Data Cleaning and Preparation
Chapter 11: AMLS: Algorithm Selection
Chapter 12: AMLS: Selecting the Features
Chapter 13: AMLS: Optimizing Model Fit and Performance
Chapter 14: AMLS: Natural Language Processing
Chapter 15: AMLS: Recommendation Engines
Chapter 16: Project: Putting It All Together
Supplement: Datasets
Supplement: AMLS: Computer Vision (in progress)

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