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Business Analytics with Visualisation
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III : Data Analysis for Strategic Insights
What’s inside every chapter
Authors
Live Analytics Lab
Syllabus
I : Business Analytics Foundations and Frameworks
Introduction to Business Analytics
1
Introduction to Business Analytics: Descriptive, Predictive, and Prescriptive
2
Analytics Maturity Model and Organizational Readiness
3
Data-Driven vs. Intuition-Based Decision Making
4
Analytics Project Lifecycle: CRISP-DM Methodology
Data Foundations and Quality
5
Data Types: Structured, Unstructured, Internal, and External
6
Data Quality Assessment
7
Ethics in Analytics: Privacy, Bias, and Responsible Use
Strategy and Performance Measurement
8
Building a Data Strategy: Governance, Architecture, and Culture
9
Key Performance Indicators and Business Metrics Framework
10
ROI Calculations for Analytics Initiatives
II : Visualization Design for Business Audiences
Visualization Foundations
11
Principles of Effective Data Visualization
12
Visual Perception and Information Processing
13
Chart Types and Their Use Cases
Design Elements
14
Color Theory and Accessibility in Visualizations
15
Typography and Layout Design
16
Interactive vs. Static Visualizations
Dashboard Design and Common Pitfalls
17
Dashboard Design Principles and Best Practices
18
Avoiding Common Visualization Mistakes
19
Cultural Considerations in Global Business Visualization
III : Data Analysis for Strategic Insights
Data Collection and Sources
20
Data Collection Methods: Surveys, Web Scraping, APIs, and Databases
21
Primary vs. Secondary Data Sources
Data Preparation and Integration
22
Data Cleaning Techniques
23
Data Transformation: Normalization, Standardization, and Aggregation
24
Data Integration Challenges and Solutions
25
Introduction to Data Warehousing and Data Lakes
Exploratory Data Analysis
26
Exploratory Data Analysis Techniques and Workflows
27
Statistical Profiling and Pattern Recognition
28
Correlation Analysis and Relationship Identification
29
Time Series Data and Seasonality Detection
IV : Tableau, Power BI, and Excel Mastery
Tableau Proficiency
30
Tableau Proficiency and Data Connections
31
Calculated Fields and Parameters in Tableau
32
Advanced Tableau Charts and Custom Visualizations
33
Interactive Tableau Dashboards with Filters and Actions
34
Tableau Server and Sharing Strategies
Power BI Integration
35
Power Query for Data Transformation
36
DAX (Data Analysis Expressions) Fundamentals
37
Power BI Measures and Calculated Columns
38
Power BI Service and Collaboration Features
39
Power BI Integration with Microsoft Ecosystem
Advanced Excel Analytics
40
Power Pivot and Excel Data Modeling
41
Advanced Excel Charting and Conditional Formatting
42
What-If Analysis and Scenario Modeling
43
Macro Basics for Excel Automation
Business Intelligence Architecture
44
ETL Processes and Data Pipelines
45
Data Warehousing Concepts for BI
46
Real-Time Analytics and Streaming Data
47
Mobile BI and Responsive Design
V : Visual Storytelling and Industry Applications
Storytelling and Functional Analytics
48
Storytelling with Data: Narrative Structure and Flow
49
Marketing Analytics: Segmentation, Campaigns, and Attribution
50
Financial Analytics: Risk, Fraud, and Performance
Operations, HR, and Sales Analytics
51
Operations Analytics: Supply Chain and Forecasting
52
HR Analytics: Talent, Retention, and Performance
53
Sales Analytics: Pipeline, Territory, and Pricing
Industry-Specific Applications
54
Retail Analytics: Customer Journey and Inventory
55
Healthcare Analytics: Patient Outcomes and Operations
56
Financial Services Analytics: Credit Risk and Compliance
57
Manufacturing Analytics: Predictive Maintenance and Quality
58
Technology Analytics: User Behavior and A/B Testing
References
III : Data Analysis for Strategic Insights
Data Collection and Sources
Data Collection Methods: Surveys, Web Scraping, APIs, and Databases
Primary vs. Secondary Data Sources
Data Preparation and Integration
Data Cleaning Techniques
Data Transformation: Normalization, Standardization, and Aggregation
Data Integration Challenges and Solutions
Introduction to Data Warehousing and Data Lakes
Exploratory Data Analysis
Exploratory Data Analysis Techniques and Workflows
Statistical Profiling and Pattern Recognition
Correlation Analysis and Relationship Identification
Time Series Data and Seasonality Detection
19
Cultural Considerations in Global Business Visualization
20
Data Collection Methods: Surveys, Web Scraping, APIs, and Databases