Data Visualization with Power BI, Tableau & Modern BI Tools

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Course Description

The Data Visualization with Power BI, Tableau & Modern BI Tools Training Course is a comprehensive program designed to develop practical skills in transforming raw business data into meaningful, interactive, and decision-ready visualizations.

The course introduces participants to the principles of effective data visualization, dashboard design, data preparation, data modeling, analytics, and business intelligence reporting. Participants will work primarily with Microsoft Power BI and Tableau, while also learning visualization concepts and practices that can be applied across other modern analytics and BI platforms.

Throughout the course, participants learn how to connect to different data sources, clean and transform data, build analytical data models, select appropriate chart types, create calculated measures and fields, design interactive dashboards, identify trends and patterns, and communicate insights effectively to business stakeholders.

The training is suitable for professionals who need to create management reports, operational dashboards, KPI scorecards, analytical reports, and executive-level visualizations for data-driven decision-making.

Duration 5 Days – 35 hrs.

Objectives

Explain the fundamentals and business value of data visualization and business intelligence.
Apply essential principles of effective visual communication and dashboard design.
Identify appropriate visualizations for different types of data and analytical requirements.
Connect Power BI and Tableau to common business data sources.
Prepare, clean, transform, and structure data for visualization.
Understand fundamental data modeling concepts used in BI solutions.
Build interactive reports and dashboards using Microsoft Power BI.
Create analytical visualizations and dashboards using Tableau.
Develop KPIs, calculated fields, measures, and business metrics.
Apply filtering, drill-down, drill-through, slicing, and interactive analysis techniques.
Visualize trends, comparisons, distributions, relationships, geographic information, and performance indicators.
Apply introductory DAX concepts in Power BI.
Use calculated fields and table calculations in Tableau.
Design dashboards for operational, analytical, management, and executive audiences.
Apply data storytelling techniques to communicate insights clearly.
Follow visualization best practices and avoid common dashboard design mistakes.
Publish and share BI reports and dashboards appropriately.
Build an end-to-end visualization solution based on business requirements.

Target Audience

Data Analysts
Business Intelligence Analysts
Business Analysts
Reporting Analysts
MIS and Reporting Professionals
Data Visualization Specialists
Data Engineers who support BI and reporting solutions
Financial Analysts
Marketing Analysts
Sales Analysts
Operations Analysts
HR Analysts
Project Managers
Department Managers
Management Reporting Teams
Power BI Users
Tableau Users
Professionals responsible for dashboards, reports, KPIs, and business analytics
Professionals transitioning into data analytics or business intelligence roles

Prerequisites

Basic computer and spreadsheet skills.
Basic familiarity with Microsoft Excel or similar spreadsheet applications.
Basic understanding of tables, rows, columns, filters, and formulas.
General understanding of business reporting and KPIs is beneficial.
Basic knowledge of databases or SQL is helpful but not required.
No advanced programming experience is required.
Prior experience with Power BI or Tableau is helpful but not required.

Course Outline

Day 1 – Data Visualization Fundamentals and Data Preparation

Module 1: Introduction to Data Visualization and Business Intelligence

Understanding data visualization
Data visualization versus reporting
Business intelligence and analytics concepts
From raw data to actionable insights
Descriptive, diagnostic, predictive, and prescriptive analytics
Role of visualization in data-driven decision-making
Overview of modern BI and visualization platforms

Module 2: Principles of Effective Data Visualization

Understanding the audience and business question
Choosing the appropriate visualization
Comparison, trend, composition, distribution, and relationship analysis
Visual hierarchy and information flow
Colors, labels, legends, and formatting
Data-to-ink considerations
Avoiding visual clutter
Common visualization mistakes
Accessibility and readability considerations

Module 3: Common Visualization Types

Tables and matrices
Bar and column charts
Line and area charts
Pie and donut charts
Scatter plots
Histograms
Treemaps
Waterfall charts
Funnel charts
KPI cards and scorecards
Maps and geographic visualizations
Combination charts
Selecting the correct visualization for a business requirement

Module 4: Understanding Data for Visualization

Structured and semi-structured data
Dimensions and measures
Categorical and numerical data
Date and time data
Granularity and aggregation
Data quality considerations
Understanding business metrics and KPIs

Module 5: Data Preparation Fundamentals

Connecting to common data sources
Excel and CSV files
Databases and relational sources
Cloud and online data sources
Cleaning and transforming data
Handling missing and inconsistent values
Changing data types
Filtering and reshaping data
Combining datasets
Preparing visualization-ready datasets

Day 2 – Microsoft Power BI: Data Modeling and Visualization

Module 6: Introduction to Microsoft Power BI

Power BI ecosystem
Power BI Desktop
Power BI Service
Power Query
Data models
Reports and dashboards
Understanding the Power BI development workflow

Module 7: Data Transformation with Power Query

Connecting to data sources
Power Query Editor
Data profiling
Removing and transforming columns
Filtering rows
Splitting and merging columns
Handling nulls and errors
Pivoting and unpivoting
Merging queries
Appending queries
Creating reusable transformation steps

Module 8: Data Modeling in Power BI

Fundamentals of analytical data modeling
Tables and relationships
Fact and dimension tables
Star schema concepts
Cardinality and filter direction
Date tables
Data granularity
Model organization
Data modeling best practices

Module 9: Building Power BI Visualizations

Creating reports
Adding and configuring visuals
Tables and matrices
Charts and graphs
Cards and KPI visuals
Maps
Slicers
Visual-level, page-level, and report-level filters
Sorting and formatting
Conditional formatting

Module 10: Introduction to DAX for Visualization

Understanding DAX
Calculated columns versus measures
Basic aggregation functions
Creating business measures
Basic logical calculations
Percentage and ratio calculations
Time-based measures
Using DAX measures in visualizations

Day 3 – Advanced Power BI Dashboards and Analytics

Module 11: Interactive Power BI Reports

Visual interactions
Cross-filtering and cross-highlighting
Drill-down and drill-up
Drill-through reports
Hierarchies
Report page tooltips
Bookmarks
Buttons and navigation
Dynamic report experiences

Module 12: KPI and Performance Dashboards

Understanding KPIs
Actual versus target analysis
Variance analysis
Period-over-period comparisons
Trend indicators
Performance scorecards
Management dashboards
Operational dashboards
Executive dashboard considerations

Module 13: Advanced Visualization Techniques in Power BI

Analytical reference lines
Trend analysis
Top-N and ranking analysis
Dynamic titles
Conditional visualization
Small multiples
Geographic analysis
Custom visuals considerations
Using decomposition and analytical visuals
Designing responsive report layouts

Module 14: Dashboard Design and Data Storytelling

Dashboard layout principles
Visual hierarchy
Designing for different audiences
Guiding users through insights
Highlighting exceptions and important findings
Creating an analytical narrative
Turning findings into business recommendations
Executive-level presentation techniques

Module 15: Publishing and Sharing Power BI Content

Publishing reports to Power BI Service
Workspaces
Reports versus dashboards
Sharing and collaboration concepts
Refresh considerations
Data security fundamentals
Row-level security overview
Governance and responsible report distribution

Day 4 – Tableau Visualization and Dashboard Development

Module 16: Introduction to Tableau

Tableau ecosystem
Tableau Desktop
Tableau Cloud and Tableau Server concepts
Tableau terminology and workspace
Dimensions and measures
Discrete and continuous fields
Data types
Tableau workflow

Module 17: Connecting and Preparing Data in Tableau

Connecting to files and databases
Understanding data sources
Relationships and joins
Unions
Data types and field properties
Extracts and live connections
Data filtering
Data preparation considerations

Module 18: Creating Tableau Visualizations

Text tables
Bar and column charts
Line charts
Area charts
Scatter plots
Histograms
Treemaps
Highlight tables
Heat maps
Geographic maps
Dual-axis and combination visualizations
Using the Marks card
Formatting visualizations

Module 19: Calculations and Analytics in Tableau

Creating calculated fields
Basic arithmetic calculations
Logical calculations
Date calculations
Aggregations
Table calculations
Percent of total
Running totals
Ranking
Level of Detail concepts
Trend lines
Reference lines
Analytics features

Module 20: Interactive Tableau Dashboards

Creating dashboards
Dashboard objects and layouts
Filters
Parameters
Highlight actions
Filter actions
Navigation actions
Dashboard interactivity
Device layouts
Designing user-friendly Tableau dashboards

Day 5 – Cross-Platform Visualization, Storytelling, and Integrated Dashboard Project

Module 21: Power BI and Tableau Comparison

Comparing Power BI and Tableau workflows
Data connectivity
Data preparation capabilities
Modeling approaches
Calculation approaches
Visualization capabilities
Dashboard interactivity
Sharing and collaboration
Enterprise deployment considerations
Selecting tools based on business requirements

Module 22: Modern Data Visualization Tools and Ecosystem

Overview of additional visualization platforms
Excel visualization and PivotCharts
Looker and Looker Studio concepts
Cloud-based BI platforms
Embedded analytics concepts
Self-service BI
Enterprise BI environments
Selecting the appropriate visualization platform

Module 23: Advanced Data Storytelling

Building a story from business data
Identifying the central insight
Context, comparison, and explanation
Designing an analytical flow
Using annotations effectively
Presenting trends and exceptions
Communicating uncertainty
Avoiding misleading visualizations
Translating analytical findings into business language

Module 24: Designing Enterprise-Ready Dashboards

Gathering dashboard requirements
Identifying users and stakeholders
Defining KPIs and business metrics
Selecting appropriate visualizations
Dashboard wireframing
Consistency and design standards
Performance considerations
Maintainability and scalability
Security and governance considerations
Dashboard lifecycle management

Module 25: Integrated Business Visualization Project

Understanding a business visualization requirement
Reviewing and preparing source data
Defining business questions
Selecting KPIs and measures
Building the analytical data structure
Creating visualizations
Developing an interactive dashboard
Applying filters and navigation
Performing trend and comparative analysis
Identifying key findings
Refining dashboard design
Preparing an executive-level visualization
Presenting insights and recommendations

Module 26: Visualization Best Practices and Course Wrap-Up

Review of visualization principles
Power BI best practices
Tableau best practices
Dashboard design checklist
Common visualization pitfalls
Maintaining consistency and usability
Improving reports for business audiences
Applying visualization skills to real-world business scenarios

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Inventory controllers and stock control personnel.
Logistics and distribution supervisors.
Internal auditors and operational compliance personnel.
Supply chain and operations professionals.
Personnel responsible for reviewing warehouse controls and performance.

Prerequisites

Basic familiarity with warehouse, logistics, or inventory activities.
General understanding of business processes and operational documentation.
Basic numerical skills, including percentages and simple calculations.
Basic spreadsheet skills are helpful.
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Course Outline

Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls

Warehouse roles within the supply chain.
End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
Physical stock movement and corresponding system transactions.
Responsibilities, authorization, and segregation of duties.
Common operational weaknesses and audit evidence.

Module 2: Warehouse Layout and Storage Systems

Functional zones, movement paths, and space utilization.
Receiving, storage, picking, staging, and dispatch areas.
Storage options: floor stacking, shelving, pallet racking, and bin storage.
Fixed and dynamic location systems.
Product slotting based on movement, size, handling needs, and compatibility.
Layout bottlenecks and their effects on productivity and inventory control.

Module 3: Receiving and Put-Away

Purchase orders, delivery documents, and expected receipts.
Quantity, condition, and item identification checks.
Recording shortages, overages, damage, and discrepancies.
Controls for acceptance, quarantine, and release.
Put-away rules and location confirmation.
Traceability through labels, barcodes, lot numbers, and serial numbers.
Timely recording of receipts and stock movements.

Module 4: Storage, Stock Rotation, and Replenishment

Storage discipline and location integrity.
FIFO and FEFO principles, applications, and limitations.
Batch, lot, and expiry-date management.
Controls for slow-moving, obsolete, damaged, and expired stock.
Replenishment of picking locations.
Supervision and review of stock rotation compliance.

Day 2: Order Fulfillment and Inventory Accuracy Module 5: Picking Strategies and Order Fulfillment

Discrete, batch, zone, and wave picking.
Selecting picking strategies for different order profiles.
Pick paths, location sequencing, and travel reduction.
Replenishment coordination and stock availability.
Picking verification and error prevention.
Packing, staging, and dispatch confirmation.
Handling short picks, substitutions, cancellations, and returns.

Module 6: Inventory Records and Accuracy Controls

Item master data, units of measure, and location records.
On-hand, available, allocated, and quarantined stock.
Transaction controls for receipts, transfers, issues, returns, and adjustments.
Common causes of physical and system stock mismatches.
Barcode scanning and WMS control points.
Inventory reconciliation and supporting documentation.
Measuring inventory accuracy and interpreting discrepancies.

Module 7: Cycle Counting and Physical Inventory

Cycle counting versus full physical inventory.
ABC classification and risk-based count priorities.
Count frequency based on value, movement, and discrepancy history.
Count preparation, blind counting, and movement controls.
Recounts, variance investigation, and adjustment approval.
Root cause analysis and corrective action follow-up.
Reviewing the effectiveness of the counting program.

Module 8: Shrinkage Reduction and Inventory Protection

Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
Access controls and accountability for stock movements.
Controls for high-value and sensitive inventory.
Returns, write-offs, scrap, and disposal controls.
Exception reporting and unusual transaction patterns.
Investigating recurring losses and unexplained adjustments.
Preventive and detective controls for shrinkage reduction.

Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs

Receiving productivity and dock-to-stock time.
Put-away productivity and replenishment performance.
Picking productivity and order accuracy.
Inventory accuracy and cycle count variance.
Space utilization and storage capacity.
Shrinkage, damage, and expiry indicators.
Defining KPI formulas, data sources, and reporting boundaries.
Interpreting trends while balancing speed, accuracy, and service.

Module 10: Supervising and Auditing Warehouse Processes

Process mapping and identification of critical control points.
Reviewing procedures against actual warehouse practices.
Tracing transactions from documents to systems and physical stock.
Reviewing approvals, access rights, and segregation of duties.
Identifying control gaps and recurring process exceptions.
Documenting findings with clear supporting evidence.
Prioritizing corrective actions and tracking closure.

Module 11: Warehouse Improvement Planning

Identifying bottlenecks, unnecessary movement, and rework.
Improving layout, slotting, and picking arrangements.
Addressing inventory discrepancy root causes.
Standardizing transaction recording and exception handling.
Assigning improvement owners and measurable targets.
Monitoring results and sustaining operational improvements.

Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan

Review a warehouse scenario containing layout, transaction, inventory, and performance information.
Identify weaknesses across receiving, put-away, storage, picking, and dispatch.
Recommend appropriate FIFO or FEFO controls.
Develop a cycle counting and inventory reconciliation plan.
Propose controls to reduce shrinkage and improve traceability.
Select relevant KPIs and define their calculation and reporting requirements.
Produce a prioritized improvement plan with responsibilities and expected outcomes.

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