Data Analytics for Business; AI-ML-PBI-SQL-R

✍ Scribed by Wolfgang Garn

• Year: 2024
• Tongue: English
• Leaves: 283
• Category: Library

✦ Synopsis

We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us store data in a structured way. The structure query language (SQL) allows us to gain first insights about business opportunities. Visualising the data using business intelligence tools and data science languages deepens our understanding of the key performance indicators and business characteristics. This can be used to create relevant classification and prediction models; for instance, to provide customers with the appropriate products or predict the eruption time of geysers. Machine learning algorithms help us in this endeavour. Moreover, we can create new classes using unsupervised learning methods, which can be used to define new market segments or group customers with similar characteristics. Finally, artificial intelligence allows us to reason under uncertainty and find optimal solutions for business challenges. All these topics are covered in this book with a hands-on process, which means we use numerous examples to introduce the concepts and several software tools to assist us. Several interactive exercises support us in deepening the understanding and keep us engaged with the material. This book is appropriate for master students but can also be used for undergraduate students. Practitioners will also benefit from the readily available tools. The material was especially designed for Business Analytics degrees with a focus on Data Science and can also be used for machine learning or artificial intelligence classes. This entry-level book is ideally suited for a wide range of disciplines wishing to gain actionable data insights in a practical manner.

✦ Table of Contents

Cover
Half Title
Title
Copyright
Table of Contents
Part One Fundamental Business Insights
1 Introduction
1.1 Data Analytics and Applications
1.2 Learning Outcomes
1.3 Tools and Data
2 Databases
2.1 Introduction
2.1.1 Overview
2.1.2 Motivation and Managers
2.1.3 Database Integration and Cloud Solutions
2.1.4 Security, Ethics and Law
2.2 Systems
2.2.1 Concept
2.2.2 Database Engines
2.2.3 Setup and Conf guration
2.2.4 Databases
2.3 SQL – Tables
2.3.1 Definition
2.3.2 Inserting Records
2.3.3 Deleting Records
2.3.4 Normal Forms
2.3.5 Data Access and R
2.4 SQL – Queries
2.4.1 Unconditional Query
2.4.2 View
2.4.3 Conditional Query
2.4.4 Group and Aggregate
2.4.5 Join Query
2.5 Entity Relationships
2.5.1 Directional ER
2.5.2 Combining Entities
2.6 Summary
3 Business Intelligence
3.1 Introduction
3.1.1 Definitions
3.2 Tools
3.2.1 PBI Overview
3.3 Design and Visualisations
3.3.1 Visualisations
3.3.2 PBI Crash Course
3.3.3 PBI General Considerations
3.4 Analytics
3.4.1 Trends and Forecasts
3.4.2 PBI Key Influencers Visual
3.4.3 DAX Functions
3.5 Miscellaneous
3.5.1 Best Practices/Shared Experiences
3.5.2 Bit-Size Challenges
3.5.3 Create Custom R Visual for PBI
3.6 Summary
Part Two Coding and Frameworks
4 Data Science Languages
4.1 Introduction
4.1.1 Language R
4.1.2 Projects and Libraries
4.2 Data Types and Operations
4.2.1 Arithmetic and Basic Data Types
4.2.2 Compound Data Types
4.2.3 String Operations and Regular Expressions
4.3 Controls and Functions
4.3.1 If Conditions
4.3.2 Loops – Control Flow
4.3.3 Functions
4.3.4 Coding with GPT
4.3.5 Data Transfer Function
4.4 Summary
5 Data Analytics Frameworks
5.1 Introduction
5.2 Modelling and Applications
5.2.1 Business Scenario Example
5.2.2 Real World and Prediction Model
5.2.3 Business Applications
5.3 Machine Learning Roadmap
5.3.1 Preprocessing, Splitting and Balancing
5.3.2 Learning
5.3.3 Evaluation and Application
5.4 Knowledge Discovery in Databases (KDD)
5.4.1 KDD – Problem and Resourcing
5.4.2 KDD – Data Cleansing and Preprocessing
5.4.3 KDD – Data Mining and Evaluation
5.4.4 KDD – Interpretation and Exploitation
5.5 Cross-Industry Standard Process for Data Mining
5.5.1 Understanding
5.5.2 Preparation and Modelling
5.5.3 Evaluation and Deployment
5.6 Summary
Part Three Learning
6 Statistical Learning
6.1 Introduction
6.2 Models and Quality Measures
6.2.1 Quality Measures
6.2.2 Confusion Matrix
6.3 Descriptive Statistics
6.3.1 Exploring Data
6.3.2 Correlation
6.4 Linear Regression
6.4.1 Simple Linear Regression
6.4.2 Multivariate Linear Regression
6.4.3 Applications, ML Parts and Statistics
6.5 Logistic Regression
6.5.1 Multiple Classes – One versus All
6.5.2 Old Faithful Example
6.6 Summary
7 Supervised Machine Learning
7.1 Introduction
7.2 Regression and Classification Trees
7.2.1 Regression Tree
7.2.2 Classification Trees
7.3 Tree-Based Algorithms
7.3.1 Random Forest
7.3.2 Trees
7.4 Nearest Neighbours Classifier
7.5 Summary
8 Unsupervised Machine Learning
8.1 Introduction
8.2 K-Means Clustering
8.2.1 Application
8.2.2 Concept and Algorithm
8.3 Hierarchical Clustering
8.3.1 Dendrogram
8.4 Self-Organising Maps
8.5 Expectation Maximisation
8.6 Summary
Part Four Artificial Intelligence
9 Artificial Intelligence
9.1 Introduction
9.1.1 AI Applications
9.1.2 Classic AI Areas
9.2 Learning – ANN and SVM
9.2.1 Recap
9.2.2 ANN Model
9.2.3 Support Vector Machines (SVM)
9.3 Problem Solving – GA and SA
9.3.1 Knapsack Problem
9.3.2 Brute Force
9.3.3 Genetic Algorithm
9.3.4 Methods Overview
9.4 Games – Minimax
9.4.1 Chess
9.4.2 Minimax Algorithm
9.5 Reasoning Under Uncertainty – BBN
9.6 Summary
Part Five
References
Index