The Modern Business Data Analyst: A Case Study Introduction into Business Data Analytics with CRISP-DM and R

✍ Scribed by Dominik Jung

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 312
• Category: Library

✦ Synopsis

This book illustrates and explains the key concepts of business data analytics from scratch, tackling the day-to-day challenges of a business data analyst. It provides you with all the professional tools you need to predict online shop sales, to conduct A/B tests on marketing campaigns, to generate automated reports with PowerPoint, to extract datasets from Wikipedia, and to create interactive analytics Web apps. Alongside these practical projects, this book provides hands-on coding exercises, case studies, the essential programming tools and the CRISP-DM framework which you'll need to kickstart your career in business data analytics.
The different chapters prioritize practical understanding over mathematical theory, using realistic business data and challenges of the Junglivet Whisky Company to intuitively grasp key concepts and ideas. Designed for beginners and intermediates, this book guides you from business data analytics fundamentals to advanced techniques, covering a large number of different techniques and best-practices which you can immediately exploit in your daily work.
The book does not assume that you have an academic degree or any experience with business data analytics or data science. All you need is an open mind, willingness to puzzle and think mathematically, and the willingness to write some R code. This book is your all-in-one resource to become proficient in business data analytics with R, equipped with practical skills for the real world.

✦ Table of Contents

Preface
Solving Business Problems with Business Data Analytics
R for Business Data Analytics
How to Read this Book
Conventions Used in this Book
Code Examples
Acknowledgments
Contents
1 Introduction
1.1 Motivation
1.2 Whisky Quality Problems in the Junglivet Company
1.2.1 Welcome to the Junglivet Whisky Company
1.2.2 Step 1: Business and Data Understanding
1.2.3 Step 2: Business Data Preparation
1.2.4 Step 3: Business Data Analytics
1.3 The Business Data Analytics Mindset
1.4 How Business Data Analytics Experts Work
1.5 Business Understanding and Project Setup
1.5.1 Business Understanding Methods
1.5.2 Cognitive Maps
1.5.3 Glossaries and Documentations
1.5.4 User Stories
1.5.5 Mockups
1.5.6 Business Objectives and Project Planning
1.6 Checklist
2 Business Data Analytics Toolbox: R and RStudio
2.1 First Steps in RStudio to Run R Code
2.2 Data Structures and Variables in R
2.3 Work With Data in R
2.4 Write Functions and Logic in R
2.5 Expand your Code with External R Packages
2.6 Manage your Projects and Data in RStudio
2.7 Further Beginner Resources
2.8 Useful R Functions for Everyday R Programming
2.9 R Beginner Exercises
3 Business Data Understanding
3.1 Introduction
3.2 Business Data Manipulation with dplyr
3.2.1 select()
3.2.2 filter()
3.2.3 Pipelines with %>%
3.2.4 mutate()
3.2.5 summarize()
3.2.6 n() and count()
3.2.7 across()
3.3 Business Data Visualization with ggplot2
3.3.1 Basic Plotting with Base R
3.3.2 Advanced Plotting with ggplot2
3.3.3 Methods of Data Visualization in R
3.3.4 Density Plot
3.3.5 Box Plot
3.3.6 Line Chart and Stacked Line Chart
3.3.7 Area Chart and Stacked Area Chart
3.3.8 Regression Plot
3.3.9 Sankey Diagram
3.3.10 Venn Diagram
3.3.11 Heatmap
3.3.12 Treemap
3.3.13 Bar Plot and Stacked Bar Plot
3.3.14 Piechart
3.3.15 Parallel Coordinates
3.3.16 Radar Plots
3.3.17 Maps
3.3.18 Export your Visualizations
3.4 Business Data Description
3.4.1 Describe Numerical Features
3.4.2 Describe Categorial Features
3.4.3 Summary Statistics
3.5 Business Data Quality and Validation
3.5.1 Validate Business Data Generation
3.5.2 Validate Business Data Provision and Loading
3.5.3 Validation During Business Data Analysis
3.6 Useful R Functions for Everyday Business Data Understanding
3.7 Checklist
3.8 Business Case Exercise: Visualizing Whisky Data
3.8.1 Scenario
3.8.2 Task
3.8.3 Remarks
3.8.4 Solutions
4 Business Data Preparation
4.1 Introduction
4.2 Business Data
4.2.1 Flat Files
4.2.2 Web data
4.2.3 APIs
4.2.4 Databases
4.3 Business Data Cleaning
4.3.1 Fix Corrupted Values
4.3.2 Reduce Noise and Outliers in your Data
4.3.3 Unify your Data Structure
4.3.4 Overcome General Data Issues
4.4 Feature Engineering
4.4.1 Generate New Features
4.4.2 Select Relevant Features
4.4.3 Extract New Features
4.4.4 Learn New Features
4.5 Business Data Integration
4.5.1 Loading Different Datasets
4.5.2 Combining Different Datasets
4.5.3 Saving and Exporting your Data
4.6 Useful R Functions for Everyday Business Data Preparation
4.7 Checklist
4.8 Business Case Exercise: Investigating Quality Production Problems
4.8.1 Scenario
4.8.2 Task
4.8.3 Remarks
4.8.4 Solutions
5 Modeling
5.1.1 Introduction
5.2 Modeling Methods in Analytics
5.2.1 Modeling Workflow and Test Design
5.2.2 The Junglivet Online Shop Dataset
5.3 Compare Business Decisions
5.3.1 Hypothesis Test
5.3.2 A/B Test
5.4 Find Clusters
5.4.1 k-means
5.4.2 k-nearest-neighbor
5.5 Find Rules and Relationships
5.5.1 Correlation Analysis
5.5.2 Association Analysis
5.6 Predict Categorial Values
5.6.1 C5.0 Tree
5.6.2 Random Forest
5.6.3 XGBoost
5.7 Predict Numeric Values
5.7.1 Linear Regression
5.7.2 Multiple Linear Regression
5.7.3 GLM
5.8 Predict Developments
5.8.1 ARIMA
5.8.2 Prophet
5.9 Useful R Functions for Everyday Business Data Analytics
5.10 Checklist
5.11 Business Case Exercise: Finding Clusters in the Whisky Market
5.11.1 Scenario
5.11.2 Task
5.11.3 Remarks
5.11.4 Solutions
6 Business Data Products
6.1 Introduction
6.2 Evaluation and Deployment of Business Data Products
6.3 Business Data Reporting
6.3.1 Automated Reports and Presentations with officer
6.3.2 Poster and Flyers
6.3.3 Interactive Notebooks with R Markdown
6.3.4 Dashboards with Flexdashboard
6.4 Business Analytics Systems
6.4.1 Decision Support Systems with Shiny
6.4.2 Recommender Systems
6.4.3 APIs with Plumber
6.4.4 Analytics Systems Deployment
6.5 Useful R Functions for Everyday App Development
6.6 Checklist
6.7 Business Case Exercise: A Dashboard for the Marketing Management
6.7.1 Scenario
6.7.2 Task
6.7.3 Remarks
6.7.4 Solutions
7 Mastering Business Data Analytics
7.1 Introduction
7.2 Start your Career as Business Data Analyst
7.2.1 Related Job Roles
7.2.2 Companies
7.2.3 Career Paths
7.3 Prepare for your Business Data Analyst Job
7.3.1 Building a Portfolio
7.3.2 Coding Challenges and Hackathons
7.3.3 Technical Interview
7.3.4 Business Cases
7.3.5 How to Continue
7.4 Business Data Analyst Best Practices
7.4.1 Getting Things Done
7.4.2 Cracking Problems
7.4.3 Pitching your Project
7.5 Some Last Words
8 Appendix
8.1 100 Technical Interview Questions
8.1.1 Business Understanding (15)
8.1.2 Business Data Analytics Toolbox (17)
8.1.3 Business Data Understanding (17)
8.1.4 Business Data Preparation (17)
8.1.5 Modeling (17)
8.1.6 Business Data Products (17)
8.2 Business Case Study 1: Analyzing Transactions in the Junglivet Online Shop
8.2.1 Scenario
8.2.2 Task
8.2.3 Remarks
8.3 Business Case Study 2: Developing a Car Maintenance System
8.3.1 Scenario
8.3.2 Task
8.3.3 Remarks
8.4 Business Case Study 3: Take Part in an Analytics Competition
8.4.1 Scenario
8.4.2 Task
8.4.3 Remarks
References