Advanced-Level Data Analytics and Visualization

Duration 7 Days – 49 hrs.

 

Overview

 

The Advanced-Level Data Analytics and Visualization Training Course develops the technical, analytical, and communication capabilities required to transform complex organizational data into reliable, decision-ready intelligence.

 

The course goes beyond routine reporting and basic dashboard development. Participants will learn how to integrate data from multiple systems, design scalable analytical models, write advanced SQL queries, develop sophisticated business calculations, conduct statistical and predictive analysis, and create executive-level dashboards using Microsoft Excel, Power Query, Power Pivot, SQL, and Microsoft Power BI.

 

Participants will also learn how to identify the data that is most critical to a business decision, separate primary indicators from supporting information, evaluate the reliability and limitations of available data, and determine which analytical methods and visualization techniques are most appropriate for the problem.

 

The training emphasizes the complete analytics process—from business problem definition and data acquisition to modeling, analysis, visualization, insight generation, recommendation development, and executive presentation.

 

Hands-on exercises and case studies may use scenarios from finance, sales, marketing, operations, human resources, customer experience, supply chain, project management, risk management, and executive performance reporting.

 

Objectives

 

• Translate complex business problems into structured analytical requirements.
• Identify primary, supporting, contextual, and nonessential data for decision-making.
• Develop analytical frameworks aligned with organizational goals and management priorities.
• Evaluate data sources based on accuracy, relevance, completeness, timeliness, lineage, and reliability.
• Integrate and transform data from multiple files, databases, applications, and business systems.
• Automate advanced data preparation workflows using Power Query.
• Apply advanced Microsoft Excel functions, dynamic arrays, analytical formulas, and statistical techniques.
• Build analytical models using Power Pivot and Microsoft Power BI.
• Design fact tables, dimension tables, hierarchies, relationships, and reusable measures.
• Write advanced SQL queries involving complex joins, Common Table Expressions, window functions, subqueries, and analytical calculations.
• Develop advanced Data Analysis Expressions measures for business intelligence reporting.
• Apply time intelligence, context manipulation, segmentation, ranking, and variance calculations.
• Conduct descriptive, diagnostic, predictive, and prescriptive analysis.
• Perform trend, contribution, sensitivity, scenario, cohort, Pareto, correlation, and variance analysis.
• Develop appropriate forecasts and communicate their assumptions and limitations.
• Design interactive, audience-specific, and executive-level dashboards.
• Apply advanced visualization and data storytelling principles.
• Identify meaningful patterns, drivers, anomalies, risks, and opportunities.
• Convert analytical results into clear business insights and actionable recommendations.
• Optimize analytical models, queries, reports, and dashboards for performance and maintainability.
• Establish controls for data quality, governance, privacy, security, and responsible analytics.
• Present complex findings effectively to executives, managers, technical teams, and other stakeholders.

 

Target Audience

 

• Experienced data analysts
• Business intelligence analysts
• Senior reporting analysts
• Business analysts
• Management information systems personnel
• Database and reporting professionals
• Finance and financial planning analysts
• Sales and marketing analysts
• Operations and supply chain analysts
• Risk and compliance analysts
• Human resources and workforce analysts
• Customer experience analysts
• Performance management professionals
• Project management office personnel
• Data-driven managers and supervisors
• Power BI developers
• Advanced Excel users
• Professionals responsible for enterprise dashboards, scorecards, and management reports

 

Prerequisites

 

• Working experience in data analysis or business intelligence
• Intermediate to advanced Microsoft Excel skills
• Experience using PivotTables and PivotCharts
• Basic working knowledge of Power Query
• Working knowledge of SQL, including:
  • Filtering
  • Aggregations
  • Grouping
  • Table joins
  • Subqueries
• Basic experience with Microsoft Power BI or a comparable business intelligence platform
• Understanding of database tables, fields, keys, and relationships
• Familiarity with business metrics and Key Performance Indicators
• Experience preparing analytical reports or dashboards
• Basic familiarity with statistics is recommended but not required.

Course Outline

 

Day 1: Advanced Analytics Strategy, Data Requirements, and Data Quality

Module 1: Advanced Analytics Strategy and Problem Definition

 

• Role of advanced analytics in organizational decision-making
• Moving from operational reporting to strategic analysis
• Descriptive, diagnostic, predictive, and prescriptive analytics
• Translating strategic concerns into analytical questions
• Defining the decision to be supported
• Identifying decision-makers and stakeholders
• Defining the unit of analysis
• Establishing analytical scope and boundaries
• Developing analytical hypotheses
• Identifying expected business outcomes
• Selecting appropriate analytical methods
• Establishing success criteria
• Defining assumptions, constraints, and limitations
• Developing an analytics project charter
• Aligning analysis with organizational strategy
• Avoiding analysis without a clear business purpose

Practical Activity

Participants translate a complex organizational concern into a structured analytics project charter.

 

Module 2: Data Prioritization and Analytical Requirements

 

• Identifying decision-critical data
• Primary versus supporting indicators
• Contextual and reference information
• Leading, lagging, and coincident indicators
• Input, activity, output, outcome, and impact measures
• Strategic, tactical, and operational metrics
• Identifying performance drivers
• Mapping metrics to business objectives
• Determining the appropriate level of detail
• Identifying analytical dimensions
• Defining data granularity
• Establishing business definitions
• Identifying data owners and source systems
• Evaluating data availability
• Identifying data gaps
• Assessing the value and cost of acquiring additional data
• Developing a data requirements and traceability matrix
• Preventing metric duplication and information overload

Practical Activity

Participants create a data requirements matrix identifying primary measures, supporting indicators, data sources, owners, and intended decisions.

 

Module 3: Advanced Data Quality, Profiling, and Validation

 

• Establishing a data-quality framework
• Data-quality dimensions:
  • Accuracy
  • Completeness
  • Consistency
  • Validity
  • Timeliness
  • Uniqueness
  • Integrity
  • Traceability
• Advanced data profiling
• Pattern and frequency analysis
• Distribution and missing-value analysis
• Duplicate and entity-resolution issues
• Referential-integrity validation
• Detecting inconsistent business definitions
• Detecting data drift
• Identifying anomalies and unusual values
• Distinguishing errors from genuine exceptions
• Establishing validation thresholds
• Creating reconciliation controls
• Comparing source and reported totals
• Developing data-quality rules
• Quantifying the impact of data-quality issues
• Corrective and preventive actions
• Establishing data-quality scorecards

Hands-On Exercise

Participants profile multiple related datasets, define validation rules, identify critical quality issues, and create a data-quality scorecard.

Day 1 Outputs

• Analytics project charter
• Defined analytical questions and hypotheses
• Stakeholder and decision requirements
• Data requirements and traceability matrix
• Preliminary data-quality assessment
• Data-quality scorecard
• Capstone project business case

 

Day 2: Advanced Excel Analytics and Automated Data Transformation

Module 4: Advanced Excel Analytics

 

• Advanced formula-design principles
• Creating maintainable and auditable Excel models
• Advanced logical and multi-condition calculations
• Advanced lookup and reference techniques
• Multi-dimensional lookups
• Dynamic array functions:
  • FILTER
  • SORT
  • SORTBY
  • UNIQUE
  • SEQUENCE
  • TAKE
  • DROP
  • CHOOSECOLS
  • CHOOSEROWS
  • VSTACK
  • HSTACK
• Advanced functions:
  • LET
  • LAMBDA
  • MAP
  • REDUCE
  • SCAN
  • BYROW
  • BYCOL
• Advanced date and time analysis
• Multi-period and rolling calculations
• Weighted averages
• Compound growth calculations
• Ranking and percentile analysis
• Sensitivity and scenario analysis
• Goal Seek
• Data Tables
• Scenario Manager
• Solver overview
• Forecasting functions
• Advanced Conditional Formatting
• Formula auditing and error tracing
• Model protection
• Workbook performance improvement

Hands-On Exercise

Participants develop an advanced Excel analytical model incorporating dynamic arrays, reusable formulas, scenarios, and management outputs.

 

Module 5: Advanced Power Query and Data Transformation

 

• Designing reusable Extract, Transform, and Load workflows
• Query dependencies and staging queries
• Connecting to multiple data sources
• Combining files from folders
• Importing data from databases
• Managing changing file structures
• Advanced merge and append operations
• Understanding join behavior
• Fuzzy-matching concepts
• Advanced grouping and aggregation
• Pivoting and unpivoting complex structures
• Creating index and conditional columns
• Working with lists, records, and tables
• Introduction to the Power Query M language
• Writing and modifying M expressions
• Creating reusable custom functions
• Creating dynamic parameters
• Handling errors using conditional logic
• Managing missing files and schema changes
• Query folding
• Identifying transformations that prevent query folding
• Incremental and selective data-loading concepts
• Optimizing refresh performance
• Documenting transformation logic
• Separating development and production parameters
• Establishing repeatable refresh processes

Hands-On Exercise

Participants build a reusable Power Query solution that consolidates, validates, transforms, and refreshes data from multiple sources.

Day 2 Outputs

• Advanced Excel analytical model
• Reusable dynamic formulas
• Scenario and sensitivity analysis
• Documented Power Query workflow
• Consolidated multi-source dataset
• Automated and refreshable data-preparation process

 

 Day 3: Advanced SQL and Enterprise Data Modeling

Module 6: Advanced SQL for Analytical Reporting

 

• Structuring complex analytical queries
• Advanced filtering and conditional logic
• Complex and multi-column joins
• Self-joins and non-equijoins
• Anti-joins and unmatched-record analysis
• Managing one-to-many and many-to-many relationships
• Avoiding duplicated aggregations
• Nested and correlated subqueries
• Common Table Expressions
• Recursive Common Table Expression concepts
• Temporary tables and derived tables
• Set operations:
  • UNION
  • UNION ALL
  • INTERSECT
  • EXCEPT
• Advanced CASE expressions
• Window functions:
  • ROW_NUMBER
  • RANK
  • DENSE_RANK
  • NTILE
  • LAG
  • LEAD
  • FIRST_VALUE
  • LAST_VALUE
• Partitioning and ordering analytical calculations
• Running totals and moving averages
• Period-over-period calculations
• Contribution analysis
• Customer and product ranking
• Cohort-style analysis
• Pivoting and unpivoting data
• Date-dimension and calendar calculations
• Query-execution concepts
• Index awareness
• Reading basic execution plans
• Reducing unnecessary scans and calculations
• Improving query performance
• Creating reusable analytical views

Hands-On Exercise

Participants write advanced analytical queries across multiple tables to produce trends, rankings, running totals, period comparisons, and management summaries.

 

Module 7: Enterprise Data Modeling for Business Intelligence

 

• Role of data modeling in reliable analytics
• Transactional versus analytical data models
• Fact and dimension tables
• Star-schema design
• Snowflake-schema considerations
• Identifying business processes
• Defining the grain of a fact table
• Fact-table types:
  • Transaction
  • Periodic snapshot
  • Accumulating snapshot
• Conformed dimensions
• Role-playing dimensions
• Degenerate dimensions
• Slowly changing dimensions
• Surrogate and natural keys
• Designing calendar and time dimensions
• Handling multiple date fields
• Managing many-to-many relationships
• Bridge tables
• Factless fact tables
• Active and inactive relationships
• Relationship cardinality
• Filter propagation
• Avoiding bidirectional-filter problems
• Managing hierarchical dimensions
• Designing reusable enterprise metrics
• Separating preparation, modeling, and presentation layers
• Validating the analytical model
• Documenting model definitions and relationships

Workshop

Participants design a dimensional data model for a multi-department reporting requirement.

Day 3 Outputs

• Advanced analytical SQL queries
• Period, ranking, and contribution calculations
• Reusable SQL views
• Query-performance assessment
• Dimensional data model
• Fact-and-dimension table design
• Documented relationships and business definitions

 

Day 4: Advanced DAX and Time Intelligence

Module 8: Advanced Power Pivot and Data Analysis Expressions

 

• Using the Excel Data Model and Power Pivot
• Calculated columns versus measures
• Row context and filter context
• Context transition
• Evaluation context
• Measure branching
• Using variables in DAX
• Advanced use of CALCULATE
• Filter-manipulation functions:
  • FILTER
  • ALL
  • ALLSELECTED
  • ALLEXCEPT
  • REMOVEFILTERS
  • KEEPFILTERS
• Table functions:
  • SUMMARIZE
  • SUMMARIZECOLUMNS
  • ADDCOLUMNS
  • SELECTCOLUMNS
• Iterator functions:
  • SUMX
  • AVERAGEX
  • MINX
  • MAXX
  • COUNTX
• Relationship functions:
  • RELATED
  • RELATEDTABLE
  • USERELATIONSHIP
  • TREATAS
• Ranking and segmentation measures
• Percentage-of-total calculations
• Dynamic-target calculations
• Actual-versus-budget analysis
• Variance amount and variance percentage
• Contribution and share calculations
• Dynamic titles and labels
• Disconnected tables
• What-if parameters
• Measure organization and documentation
• Testing and validating complex measures

Hands-On Exercise

Participants develop reusable advanced measures for an interactive management performance model.

 

Module 9: Advanced Time Intelligence and Comparative Analysis

 

• Designing and marking a calendar table
• Calendar year versus fiscal year
• Year-to-date, quarter-to-date, and month-to-date calculations
• Previous-period calculations
• Year-over-year comparisons
• Month-over-month comparisons
• Rolling-period calculations
• Moving averages
• Trailing 3-, 6-, and 12-month analysis
• Same-period-last-year calculations
• Cumulative values
• Period-to-date targets
• Working-day calculations
• Business-day comparisons
• Dynamic period selection
• Current-versus-prior-period measures
• Variance decomposition
• Identifying seasonality
• Interpreting trend changes
• Avoiding incomplete-period comparison errors

Hands-On Exercise

Participants build dynamic time-intelligence measures for fiscal and calendar reporting.

 

Day 4 Outputs

• Advanced reusable DAX measures
• KPI and metric dictionary
• Dynamic actual-versus-budget analysis
• Ranking and segmentation measures
• Calendar and fiscal time-intelligence model
• Rolling-period and year-over-year calculations
• Validated management performance model

 

Day 5: Statistical, Diagnostic, Forecasting, and Predictive Analysis

Module 10: Advanced Statistical and Diagnostic Analysis

 

• Selecting appropriate analytical techniques
• Descriptive statistics
• Measures of central tendency
• Measures of dispersion
• Percentiles and quartiles
• Distribution analysis
• Normal and non-normal distributions
• Identifying outliers
• Z-score concepts
• Correlation analysis
• Correlation versus causation
• Simple linear-regression concepts
• Interpreting coefficients and goodness of fit
• Comparing groups and segments
• Sampling considerations
• Confidence intervals
• Hypothesis-testing concepts
• Statistical significance versus business significance
• Pareto and ABC analysis
• Variance and contribution analysis
• Cohort analysis
• Retention and attrition analysis
• Funnel and conversion analysis
• Basic root-cause analysis
• Driver analysis
• Avoiding misleading statistical conclusions

Practical Activity

Participants select and apply appropriate analytical techniques to diagnose a business performance issue.

 

Module 11: Forecasting, Scenario, and Predictive Analysis

 

• Purpose and limitations of forecasting
• Understanding historical patterns
• Identifying trend and seasonality
• Time-series decomposition concepts
• Moving-average forecasting
• Exponential-smoothing concepts
• Linear-trend forecasting
• Excel forecasting functions
• Power BI analytical and forecasting features
• Baseline, optimistic, and pessimistic scenarios
• Sensitivity and what-if analysis
• Identifying forecast drivers
• Measuring forecast accuracy
• Mean Absolute Error
• Mean Absolute Percentage Error
• Comparing forecast versus actual results
• Updating forecasts
• Documenting assumptions
• Communicating uncertainty and prediction intervals
• Avoiding false precision
• Introduction to predictive analytics workflows
• Determining when machine-learning models are appropriate

Hands-On Exercise

Participants create a forecast, compare alternative scenarios, and prepare a management interpretation of the results.

Day 5 Outputs

• Statistical and diagnostic analysis
• Outlier and correlation assessment
• Business-driver analysis
• Root-cause findings
• Baseline forecast
• Alternative business scenarios
• Forecast-accuracy measures
• Management interpretation of forecast uncertainty

 

Day 6: Advanced Power BI Dashboards, Visualization, and Executive Storytelling

Module 12: Advanced Power BI Report and Dashboard Development

 

• Planning an enterprise-level report
• Defining report audiences
• Separating operational, analytical, and executive views
• Designing reusable report templates
• Creating detailed and summary pages
• Drill-down and drill-through analysis
• Report-page tooltips
• Bookmarks and navigation
• Field parameters
• Dynamic measure and dimension selection
• Conditional formatting
• Small multiples
• Decomposition trees
• Key influencer visuals
• Custom-visual considerations
• Designing KPI scorecards
• Presenting actual, target, forecast, and variance
• Exception-based reporting
• Designing mobile-friendly report layouts
• Applying themes and visual standards
• Ensuring accessibility
• Managing visual interactions
• Creating user-guided analytical experiences
• Designing reports for self-service analysis
• Avoiding overcrowded dashboards
• Testing report usability

Hands-On Exercise

Participants develop a multi-page interactive dashboard for executive and operational users.

 

Module 13: Advanced Data Visualization Design

 

• Matching visualizations to analytical objectives
• Visualizing:
  • Comparisons
  • Trends and changes
  • Distributions
  • Relationships
  • Composition and contribution
  • Uncertainty
  • Targets and thresholds
  • Geographic information
• Using reference lines and benchmarks
• Applying visual hierarchy
• Directing audience attention
• Using pre-attentive attributes
• Managing color intentionally
• Designing for accessibility
• Choosing meaningful scales
• Avoiding truncated and misleading axes
• Reducing chart clutter
• Designing effective tables and matrices
• Using annotations and callouts
• Creating insight-driven chart titles
• Selecting executive-level visualizations
• Evaluating visualization effectiveness
• Avoiding decorative but nonfunctional visuals

Practical Activity

Participants redesign ineffective management charts and dashboards using advanced visualization principles.

 

Module 14: Data Storytelling and Executive Decision Support

 

• Structuring a strategic analytical narrative
• Identifying the central business message
• Separating essential evidence from supporting detail
• Organizing analysis into a logical sequence
• Presenting:
  • Business context
  • Analytical question
  • Evidence
  • Findings
  • Implications
  • Options
  • Recommendations
  • Required decisions
• Creating executive summaries
• Writing decision-oriented headlines
• Quantifying business impact
• Presenting uncertainty and limitations
• Explaining technical analysis to non-technical audiences
• Tailoring presentations to executives and operational managers
• Developing recommendation alternatives
• Communicating risks and trade-offs
• Handling stakeholder objections
• Responding to analytical questions
• Supporting recommendations with traceable evidence
• Avoiding unsupported claims
• Facilitating data-driven decision discussions

Presentation Activity

Participants deliver an executive briefing supported by an interactive dashboard and analytical evidence.

Day 6 Outputs

• Multi-page enterprise dashboard
• Executive and operational report views
• Dynamic report navigation and interactions
• Redesigned management visualizations
• Insight-driven chart titles
• Executive summary
• Strategic analytical narrative
• Draft capstone presentation

 

 

Day 7: Performance Optimization, Governance, and Final Capstone Presentation

Module 15: Performance Optimization and Deployment

 

• Identifying slow data-preparation processes
• Query folding and source-level processing
• Reducing unnecessary data volume
• Removing unused columns and rows
• Optimizing data types
• Improving SQL-query performance
• Reducing model cardinality
• Choosing calculated columns or measures appropriately
• Optimizing DAX
• Avoiding expensive iterator calculations
• Using variables for readability and efficiency
• Star-schema performance advantages
• Managing report visual load
• Reducing unnecessary visual interactions
• Performance Analyzer overview
• Refresh planning
• Scheduled-refresh concepts
• Gateway concepts
• Incremental-refresh concepts
• Development, testing, and production environments
• Report version control
• Release and deployment considerations
• Maintaining documentation
• Establishing report ownership and support procedures

Hands-On Exercise

Participants review and optimize a slow analytical model and dashboard.

 

Module 16: Data Governance, Security, and Responsible Analytics

 

• Data-governance principles
• Data ownership and stewardship
• Business glossary and metric definitions
• Data lineage and traceability
• Master and reference-data management concepts
• Access control
• Role-based data access
• Row-level security concepts
• Protecting confidential and personal information
• Data-privacy requirements
• Data-retention and disposal considerations
• Secure data export and sharing
• Report certification and endorsement
• Version control and change management
• Auditability of calculations
• Model and report documentation
• Bias in data and analysis
• Transparent analytical assumptions
• Responsible use of predictive analysis
• Human review of automated recommendations
• Ethical visualization and reporting
• Establishing a report review and approval process

Practical Activity

Participants develop a governance and control checklist for an enterprise analytics solution.

 

Module 17: Final Advanced Analytics Capstone Project

Participants complete an end-to-end advanced analytics project involving:

• Defining a complex business problem
• Identifying the decision to be supported
• Developing analytical questions and hypotheses
• Identifying primary and supporting data
• Preparing a data requirements matrix
• Evaluating data quality and reliability
• Integrating multiple data sources
• Automating data preparation using Power Query
• Retrieving and analyzing data using advanced SQL
• Designing a dimensional data model
• Developing advanced measures and calculations
• Conducting trend, variance, segmentation, and driver analysis
• Developing a forecast or scenario model
• Creating an enterprise-level dashboard
• Identifying significant findings, risks, and opportunities
• Developing actionable recommendations
• Documenting assumptions and limitations
• Preparing an executive summary
• Presenting the analysis to a management panel
• Responding to stakeholder questions

Final Presentation Activity

Each participant or group presents:

• Business problem and decision requirement
• Analytics project charter
• Primary and supporting data
• Data-quality assessment
• Transformation and integration process
• Advanced SQL analysis
• Dimensional data model
• Advanced measures and KPIs
• Statistical and diagnostic findings
• Forecast and alternative scenarios
• Enterprise dashboard
• Significant findings, risks, and opportunities
• Recommendations and required decisions
• Assumptions and limitations
• Governance and deployment controls

Day 7 and Final Course Outputs

• Optimized analytical model
• Performance assessment
• Deployment and refresh plan
• Governance and control checklist
• Completed enterprise dashboard
• Decision-ready business insights
• Prioritized recommendations
• Executive summary
• Executive-level presentation
• Completed advanced analytics capstone project

Recommended Software and Tools

 

• Microsoft Excel 2021 or Microsoft 365
• Microsoft Power Query
• Microsoft Power Pivot
• Microsoft Power BI Desktop
• Microsoft SQL Server, PostgreSQL, or MySQL
• SQL Server Management Studio or another SQL client
• Microsoft PowerPoint
• Python with pandas, NumPy, and visualization libraries, when included as an optional component

The technologies may be adjusted based on the systems used by the organization.