Introduction to Data Analytics

What Is Business Intelligence?

• Define Business Intelligence
• Explain the business value delivered by accurate, timely, and actionable information
• Recognize common organizational and cultural barriers to BI success

Business Intelligence Terminology and Challenges

• Distinguish online transactional processing from online analytical processing
• Define data warehouses, data marts, business analytics, and data mining
• Explain dashboards, scorecards, and performance indicators
• Examine challenges involving stakeholders, culture, technology, strategy, data, and processes

Making Decisions with Business Intelligence

• Distinguish strategic decisions from operational decisions
• Apply the principles of economy, efficiency, and effectiveness
• Understand how BI supports scenario analysis, forecasting, classification, and automated decisions

Business Intelligence Tools, Skills, and Applications

• Identify common BI tools, including reporting, dashboards, analytical processing, and data mining
• Examine basic and advanced BI platforms
• Recognize the skills required by BI and data professionals
• Explore BI applications in education, retail, banking, healthcare, manufacturing, government, and marketing
• Understand the role of customer journey mapping

Business Intelligence Initiative Roadmap

• Follow a BI initiative from justification and planning through design, development, deployment, and evaluation
• Identify business requirements and assess organizational readiness
• Examine database design, metadata, ETL, applications, prototypes, and data mining
• Understand the role of the business case in a BI initiative

Introducing Strategy Analysis

• Define strategy analysis
• Examine an organization’s environment, goals, capabilities, and long-term direction
• Distinguish strategy from tactics

Internal Analysis

• Assess organizational strengths and weaknesses
• Use internal-analysis techniques to understand capabilities and performance
• Identify the root cause of a business problem

External Analysis

• Assess opportunities, threats, competitors, customers, suppliers, regulators, and other external influences
• Apply external-analysis frameworks to a business situation

Combining the Analyses

• Combine internal and external findings
• Identify strategic options and priorities
• Connect analytical findings to organizational decisions

Building the Business Case

• Explain the purpose and structure of a business case
• Link proposed actions to organizational objectives
• Identify expected benefits, costs, risks, and measures of success

Components of the Data Analytics Solution

• Explain the role of data warehouses and data marts
• Distinguish transactional and analytical processing
• Understand how ETL, reporting tools, dashboards, scorecards, and analytics work together

Data Warehouse Design

• Identify operational, external, structured, and unstructured data sources
• Recognize business and technical design considerations
• Understand dimensional data structures and data aggregation
• Explain how warehouse design supports efficient reporting and querying

Developing the Data Warehouse

• Extract, transform, clean, combine, and load data
• Understand the importance of metadata and data quality
• Maintain and update warehouse data as organizational information changes

Data Warehousing Best Practices

• Align warehouse development with business requirements
• Design data for accessibility by business users
• Support reporting, managerial decision-making, and future data mining
• Apply practices that improve consistency, usefulness, and maintainability

The Value of Data Mining

• Define data mining and machine learning
• Explain how data mining uncovers patterns, relationships, and insights
• Recognize the benefits of data-driven decision-making

Gathering and Selecting Data

• Identify appropriate internal and external data sources
• Gather data relevant to a defined business problem
• Select useful variables and assess whether sufficient data is available

Cleaning and Formatting Data

• Identify incomplete, inaccurate, inconsistent, duplicated, or biased data
• Clean, transform, and format data for analysis
• Understand how data preparation affects analytical results

Data Mining Techniques

• Apply classification and decision-tree concepts
• Examine association-rule mining, clustering, regression, and time-series analysis
• Recognize anomaly and outlier detection
• Understand the role of artificial neural networks and machine learning

Tools and Platforms for Data Mining

• Recognize common commercial and open-source data mining platforms
• Understand differences among proprietary, open-source, and freemium tools
• Select tools based on the problem, volume of data, skills, and organizational needs

Data Mining Best Practices

• Begin with a clearly defined, valuable business problem
• Validate models and results against new data
• Communicate findings in an understandable and actionable form
• Consider privacy, fairness, bias, transparency, and other ethical issues in data use

Introducing Data Visualization

• Define data visualization and its role in the data lifecycle
• Present the right amount of information in an appropriate order and format
• Align visualizations with the consumer’s needs and the purpose of the analysis

Visual Patterns and Infographics

• Recognize how people interpret visual patterns
• Use visual hierarchy, grouping, comparison, and emphasis
• Understand how infographics can communicate complex information

Excellence in Visualization

• Select visualizations based on audience, context, and data type
• Avoid misleading, cluttered, or unnecessarily complex displays
• Focus attention on high-priority information
• Use titles, labels, scales, and supporting context effectively

Types of Charts

• Select charts appropriate for comparison, composition, distribution, relationships, and trends
• Recognize the appropriate use of bar, line, pie, scatter, and other chart types
• Match the chart to the analytical question being answered