This introductory course provides a high-level overview of the concepts, processes, and techniques used in data analytics. You will learn how organizations transform raw data into useful insights, use Business Intelligence to support decisions, assess business strategy, organize data in data warehouses, discover patterns through data mining, and communicate findings through effective data visualization. The course emphasizes the conceptual foundations behind common analytics practices rather than any specific software tool, making it suitable for professionals who need to understand how data supports organizational performance and decision-making. Introduction to Data Analytics Benefits By the end of this course, attendees will be able to: Define data analytics and explain how it supports effective decision-making Recognize the fundamentals of pattern recognition and the data processing chain Explain the business value, terminology, tools, applications, and challenges of Business Intelligence Distinguish between strategic and operational decisions supported by Business Intelligence Apply internal and external strategy-analysis concepts to a business situation Identify root causes and understand how strategy analysis contributes to a business case Explain how data warehouses support reporting, analysis, and managerial decision-making Identify common data sources and the processes used to develop and maintain a data warehouse Explain how data mining uncovers knowledge, patterns, insights, and organizational advantage Recognize key data mining techniques, tools, platforms, best practices, and ethical considerations Define data visualization and select appropriate visual formats based on the audience, context, and data Recognize how visual patterns, charts, and infographics communicate information to stakeholders Training Prerequisites No previous data analytics experience is required. Familiarity with basic business concepts and general computer use is helpful. This is a conceptual course and does not require experience with a specific analytics, database, or visualization tool. The existing reference to “basic worksheet or spreadsheet skills” may be removed, as the revised course does not focus on a specific spreadsheet application. Certification Information Learning Tree Exam included Data Analytics Introduction Training Outline Chapter 1: Data Analytics Introduction What Is Data Analytics? Define data analytics and its value to an organization Understand how raw data is transformed into useful business information Recognize how analytics helps optimize organizational performance Making Decisions Define a decision and examine different dimensions of decision-making Distinguish the quality of a decision from its eventual outcome Identify the characteristics of well-framed, informed, and actionable decisions Business Intelligence Introduce Business Intelligence and its relationship to data analytics Examine internal and external business information Recognize tools such as PESTLE analysis, the Balanced Scorecard, reports, and dashboards Pattern Recognition Define pattern recognition and identify different types of patterns Examine classification, association, anomaly detection, clustering, regression, and neural networks Understand the importance of data quality and business-domain knowledge when identifying patterns Data Mining Projects and the Data Processing Chain Select appropriate, high-value data mining projects Follow the data processing chain from data storage through visualization Understand data, metadata, data types, and datafication Databases and Data Modeling Define databases and data models Distinguish conceptual, logical, and physical data models Recognize common database relationships and structures Data Warehouses, Data Mining, and Data Visualization Explain the purpose of a data warehouse Use aggregated data to identify patterns and answer business questions Recognize how visualization makes analytical results easier to understand Chapter 2: Business Intelligence Concepts and Applications 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 Chapter 3: Strategy Analysis 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 Chapter 4: Data Warehousing 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 Chapter 5: Data Mining 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 Chapter 6: Data Visualization 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
