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Business Analytics and SQL Essentials: An Overview of Practical Skills for CRISP-DM

Data Analytics

ISBN #978-1-959142-89-8

Business Analytics and SQL Essentials: An Overview of Practical Skills for CRISP-DM

Mark Keith, PhD

Data mining techniques are changing constantly. Through practice-based video tutorials, this resource teaches the latest and most common techniques for both descriptive and predictive data analytics. Using Tableau, students learn dashboard design and data storytelling in organizational settings. Students also learn basic methods in Excel for multiple and linear regression, then turn to more advanced algorithms and techniques in Microsoft Azure Machine Learning Studio.

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Course Objective

This resource teaches students the latest data mining techniques, including descriptive and predictive analytics, dashboard design with Tableau, data storytelling, Excel regression methods, and advanced algorithms using Microsoft Azure Machine Learning Studio.

Table of Contents

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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