Intermediate-Level Data Analytics and Visualization

Course Description

Intermediate-level data analytics and visualization builds on foundational analytics skills by teaching you how to transform data into meaningful insights through effective charts, dashboards, and reporting.

Duration 6 Days – 42 hrs.

Overview

The Intermediate-Level Data Analytics and Visualization Training Course equips participants with practical skills to transform multiple sources of business data into reliable analysis, interactive dashboards, and decision-ready insights.

Building on basic knowledge of Microsoft Excel, SQL, and data visualization, the course focuses on more advanced data preparation, analysis, data modeling, dashboard development, and business intelligence techniques. Participants will learn how to identify decision-critical data, distinguish primary information from supporting details, establish meaningful measures and Key Performance Indicators, and evaluate data based on quality, relevance, and business impact.

The course covers advanced Excel functions, PivotTables, Power Query, intermediate SQL, relational data analysis, data modeling, and dashboard creation using Microsoft Power BI or a comparable business intelligence platform. Participants will also learn how to apply analytical frameworks, conduct trend and variance analysis, identify contributing factors, and communicate findings through clear visualizations, executive summaries, and data-driven recommendations.

Hands-on exercises will use realistic business scenarios from sales, finance, operations, human resources, customer service, project management, and other organizational functions.

Objectives

Apply an organized data analytics process to business problems.
Translate business questions into measurable analytical requirements.
Identify primary, decision-critical, supporting, and contextual data.
Evaluate datasets based on relevance, accuracy, completeness, consistency, timeliness, and reliability.
Combine, clean, transform, and structure data from multiple sources.
Use advanced Microsoft Excel functions for business analysis.
Create advanced PivotTables, PivotCharts, and interactive Excel dashboards.
Use Power Query to automate repetitive data preparation activities.
Write intermediate SQL queries involving joins, subqueries, conditional logic, aggregations, and Common Table Expressions.
Analyze related data from multiple database tables.
Develop calculated measures, business metrics, and Key Performance Indicators.
Understand the fundamentals of dimensional data modeling.
Build interactive reports and dashboards using Microsoft Power BI or a similar business intelligence platform.
Select appropriate visualizations based on the analytical question, data type, audience, and intended message.
Conduct trend, variance, segmentation, contribution, and comparative analysis.
Distinguish observations, findings, insights, conclusions, and recommendations.
Identify significant business patterns, performance gaps, risks, and opportunities.
Present analytical findings through clear visualizations and structured data storytelling.
Develop practical, evidence-based recommendations for management and stakeholders.
Apply responsible, ethical, and secure practices when handling organizational data.

Target Audience

Data analysts and reporting analysts
Business intelligence analysts
Business analysts
Management information system personnel
Finance and accounting professionals
Sales and marketing analysts
Operations and supply chain personnel
Human resources analysts
Customer service and customer experience analysts
Project managers and project coordinators
Performance management personnel
Supervisors and team leaders
Management trainees
Employees responsible for preparing recurring reports
Professionals who create dashboards and management presentations
Business users who need to improve their data analysis and visualization capabilities

Prerequisites

Basic understanding of data analytics and business intelligence concepts
Working knowledge of Microsoft Excel
Experience using basic formulas and functions
Familiarity with sorting, filtering, tables, charts, and PivotTables
Basic understanding of SQL, including:
- SELECT
- WHERE
- ORDER BY
- GROUP BY
- Basic aggregate functions
General experience working with business reports or operational data
Basic understanding of business metrics and Key Performance Indicators
Basic programming experience is not required.

Course Outline

Day 1: Analytics Foundations, Data Prioritization, and Data Quality

Module 1: Intermediate Data Analytics and Business Intelligence

Review of data analytics concepts
Descriptive, diagnostic, predictive, and prescriptive analytics
Role of business intelligence in decision-making
Difference between:
- Data
- Information
- Finding
- Insight
- Conclusion
- Recommendation
The data analytics lifecycle
Understanding business questions and decision requirements
Translating business problems into analytical questions
Defining the scope of an analysis
Identifying users and stakeholders
Establishing measurable objectives
Determining required data sources
Identifying expected analytical outputs
Recognizing assumptions, constraints, and limitations

Practical Activity

Participants convert a general business concern into specific analytical questions, required data, metrics, and expected outputs.

Module 2: Identifying and Prioritizing Decision-Relevant Data

Identifying primary and decision-critical data
Supporting and supplementary data
Contextual and reference data
Relevant versus irrelevant information
Internal and external data sources
Operational, transactional, master, and reference data
Quantitative and qualitative information
Leading and lagging indicators
Input, process, output, and outcome measures
Dimensions and measures
Metrics versus Key Performance Indicators
Identifying critical business drivers
Mapping data to business objectives
Prioritizing data based on:
- Decision relevance
- Business impact
- Urgency
- Reliability
- Availability
Identifying missing data
Avoiding information overload
Developing a basic data requirements matrix

Practical Activity

Participants examine a business case and classify available information as primary, supporting, contextual, irrelevant, or missing.

Module 3: Data Quality Assessment and Data Profiling

Importance of data quality
Data-quality dimensions:
- Accuracy
- Completeness
- Consistency
- Validity
- Timeliness
- Uniqueness
- Integrity
- Relevance
Data profiling techniques
Examining data types and structures
Identifying missing values
Detecting duplicate records
Recognizing inconsistent formats
Identifying invalid values
Identifying unusual values and outliers
Evaluating data distributions
Detecting relationship and referential-integrity issues
Differentiating genuine outliers from data errors
Developing validation rules
Documenting data-quality findings
Creating a data-quality checklist
Establishing corrective actions

Hands-On Exercise

Participants profile a business dataset, identify quality issues, assess their impact, and recommend corrective actions.

Day 1 Outputs

Defined business problem and analytical questions
Initial stakeholder and decision requirements
Data requirements matrix
Classification of primary and supporting data
Preliminary data-quality assessment
Capstone project business case selection

Day 2: Advanced Data Preparation and Business Analysis Using Excel

Module 4: Advanced Data Preparation Using Microsoft Excel

Structuring data for analysis
Converting ranges into Excel tables
Using structured references
Managing dates, text, numbers, and categories
Advanced sorting and filtering
Using Advanced Filter
Removing duplicates
Managing blank and missing values
Standardizing inconsistent data
Splitting and combining columns
Flash Fill techniques
Text-cleaning functions:
- TRIM
- CLEAN
- SUBSTITUTE
- REPLACE
- TEXTBEFORE
- TEXTAFTER
- TEXTSPLIT
Text-extraction functions:
- LEFT
- RIGHT
- MID
- FIND
- SEARCH
Date and time preparation
Converting text-based dates and numbers
Error identification and correction
Using Data Validation
Using Conditional Formatting for data-quality checks
Preparing data from multiple worksheets
Creating reusable data-preparation templates

Hands-On Exercise

Participants clean, standardize, and restructure a multi-column business dataset using Excel.

Module 5: Advanced Excel Formulas for Business Analysis

Relative, absolute, and mixed references
Nested logical functions
Using:
- IF
- IFS
- AND
- OR
- NOT
- IFERROR
- IFNA
Conditional aggregation:
- SUMIFS
- COUNTIFS
- AVERAGEIFS
- MAXIFS
- MINIFS
Advanced lookup techniques:
- XLOOKUP
- XMATCH
- INDEX
- MATCH
Multi-criteria lookups
Two-way lookups
Approximate and exact matching
Dynamic array functions:
- FILTER
- SORT
- SORTBY
- UNIQUE
- SEQUENCE
Date-analysis functions:
- YEAR
- MONTH
- DAY
- EOMONTH
- EDATE
- NETWORKDAYS
- WORKDAY
Ranking and percentile functions
Calculating:
- Growth rates
- Contribution percentages
- Variances
- Running totals
- Moving averages
- Performance against targets
Basic statistical functions
Creating reusable formulas and analytical templates

Hands-On Exercise

Participants use advanced formulas to analyze sales, operational, financial, or workforce performance.

Day 2 Outputs

Cleaned and standardized Excel dataset
Reusable data-preparation template
Advanced formula-based analytical report
Lookup and conditional-calculation model
Initial trends, variances, and performance findings

Day 3: Excel Dashboards and Automated Data Transformation

Module 6: Advanced PivotTables and Excel Dashboards

Preparing data for PivotTable analysis
Creating advanced PivotTables
Grouping data by:
- Date
- Month
- Quarter
- Year
- Numeric ranges
Using multiple dimensions and measures
Changing value calculations
Displaying values as:
- Percentage of total
- Difference from
- Percentage difference
- Running total
- Rank
Creating calculated fields
Using report filters
Using slicers and timelines
Connecting slicers to multiple PivotTables
Creating PivotCharts
Developing KPI summary cards
Applying Conditional Formatting
Creating actual-versus-target comparisons
Designing an interactive Excel dashboard
Establishing visual hierarchy
Improving dashboard readability
Adding definitions, notes, and refresh dates
Avoiding common dashboard-design problems

Hands-On Exercise

Participants create an interactive Excel dashboard using PivotTables, PivotCharts, slicers, and KPI indicators.

Module 7: Data Transformation and Automation Using Power Query

Introduction to Extract, Transform, and Load processes
Role of Power Query in data preparation
Connecting to:
- Excel workbooks
- CSV files
- Text files
- Folders
- Databases
- Web-based tables, when permitted
Understanding the Power Query Editor
Data types and column profiling
Removing and selecting columns
Filtering and sorting rows
Renaming and reordering columns
Replacing values
Managing errors and null values
Splitting and merging columns
Extracting text, numbers, and dates
Creating conditional columns
Creating custom columns
Grouping and aggregating data
Pivoting and unpivoting columns
Appending multiple tables
Merging related tables
Understanding join types
Combining multiple files from a folder
Creating query parameters
Refreshing transformed data
Documenting transformation steps
Improving query efficiency

Hands-On Exercise

Participants combine, clean, transform, and consolidate multiple monthly files into a refreshable analytical dataset.

Day 3 Outputs

Interactive Excel dashboard
KPI summary cards
Actual-versus-target analysis
Automated Power Query workflow
Consolidated and refreshable analytical dataset
Documented data-transformation steps

Day 4: Intermediate SQL and Data Modeling for Business Intelligence

Module 8: Intermediate SQL for Data Analytics

Review of SQL query structure
Using column and table aliases
Filtering with multiple conditions
Working with:
- IN
- BETWEEN
- LIKE
- CASE
- COALESCE
- NULLIF
Handling null values
String functions
Date and time functions
Mathematical functions
Aggregate functions
Grouping across multiple dimensions
Filtering aggregated results using HAVING
Joining related tables:
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- FULL OUTER JOIN
Understanding one-to-one and one-to-many relationships
Avoiding duplicate results caused by incorrect joins
Using self-joins
Combining query results using:
- UNION
- UNION ALL
Subqueries
Correlated and non-correlated subqueries
Common Table Expressions
Introduction to window functions:
- ROW_NUMBER
- RANK
- DENSE_RANK
- LAG
- LEAD
- Running totals
Creating reusable analytical queries
Exporting query results
Basic SQL query validation and performance considerations

Hands-On Exercise

Participants retrieve and analyze data from multiple related tables using joins, conditional logic, subqueries, Common Table Expressions, and basic window functions.

Module 9: Data Modeling for Business Intelligence

Importance of data modeling
Understanding tables, fields, and relationships
Transaction and master data
Fact and dimension tables
Dimensional modeling
Star-schema concepts
Snowflake-schema overview
Identifying the appropriate level of detail
Understanding data granularity
Creating relationships between tables
One-to-one and one-to-many relationships
Active and inactive relationships
Understanding filter direction
Avoiding many-to-many relationship problems
Using a dedicated calendar table
Separating measures from descriptive attributes
Calculated columns versus measures
Basic data-model validation
Common data-modeling mistakes

Practical Activity

Participants design a basic star-schema model for a sales, finance, operations, or human-resources reporting scenario.

Day 4 Outputs

Reusable intermediate SQL queries
Joined and summarized business datasets
Ranking and running-total calculations
Initial fact-and-dimension table design
Basic star-schema data model
Data-model validation checklist

Day 5: Power BI Analysis, Interactive Dashboards, and Business Analysis

Module 10: Introduction to Power BI Data Analysis

Overview of the Power BI environment
Power BI Desktop interface
Connecting to Excel, CSV, and database sources
Using Power Query in Power BI
Loading transformed data
Creating table relationships
Reviewing the model structure
Creating calculated columns
Introduction to Data Analysis Expressions
Understanding row context and filter context
Creating measures using:
- SUM
- COUNT
- DISTINCTCOUNT
- AVERAGE
- DIVIDE
Using CALCULATE
Creating percentage and ratio measures
Creating actual-versus-target measures
Creating year-to-date and period-comparison measures
Introduction to basic time intelligence
Formatting and organizing measures
Validating Power BI calculations

Hands-On Exercise

Participants create a data model and develop calculated measures for a business performance report.

Module 11: Interactive Data Visualization and Dashboard Development

Understanding reports, dashboards, and scorecards
Defining dashboard users and requirements
Selecting meaningful KPIs
Matching visualization types to analytical questions
Visualizing:
- Comparisons
- Trends
- Composition
- Distribution
- Relationships
- Geographic information
- Performance against target
Creating:
- Tables and matrices
- KPI cards
- Bar and column charts
- Line and area charts
- Combination charts
- Scatter plots
- Treemaps
- Waterfall charts
- Maps, when appropriate
Adding slicers and filters
Creating drill-down hierarchies
Using drill-through pages
Creating report tooltips
Applying conditional formatting
Establishing page navigation
Designing detailed and executive report pages
Applying visual hierarchy
Using titles, subtitles, labels, and annotations
Applying consistent formatting
Improving report accessibility
Testing report interactions
Avoiding misleading or unnecessary visuals

Hands-On Exercise

Participants develop an interactive business intelligence dashboard with multiple pages, filters, drill-down features, and KPI indicators.

Module 12: Business Analysis Techniques

Establishing baselines and comparison points
Comparing actual versus target performance
Period-over-period analysis
Year-over-year and month-over-month analysis
Variance analysis
Trend analysis
Contribution analysis
Pareto analysis
Segmentation analysis
Ranking and prioritization
Cohort and category analysis
Identifying concentration and distribution patterns
Identifying exceptions and anomalies
Basic root-cause analysis
Using the Five Whys
Applying cause-and-effect thinking
Correlation versus causation
Distinguishing symptoms from possible causes
Identifying performance drivers
Recognizing business risks and opportunities
Documenting assumptions and analytical limitations

Practical Activity

Participants examine an underperforming business area and identify its most significant contributing factors.

Day 5 Outputs

Power BI data model
Calculated measures and KPIs
Multi-page interactive dashboard
Drill-down and drill-through report
Trend, variance, contribution, and segmentation analysis
Identification of major performance drivers
Draft capstone dashboard

Day 6: Actionable Insights, Executive Storytelling, Governance, and Capstone Presentation

Module 13: Developing Useful and Actionable Insights

Moving from data to decision-ready insights
Difference between observation and insight
Difference between finding and recommendation
Evaluating whether a finding is significant
Connecting analysis to business objectives
Identifying who is affected
Determining and quantifying business impact
Applying the “What, So What, and Now What” framework
Structuring an analytical insight:
- What happened?
- Where did it happen?
- When did it happen?
- Who or what was affected?
- Why might it have happened?
- What is the business implication?
- What action should be considered?
Developing evidence-based recommendations
Prioritizing recommendations based on:
- Business value
- Urgency
- Cost
- Effort
- Risk
- Feasibility
Avoiding unsupported conclusions
Communicating uncertainty
Documenting assumptions and limitations

Practical Activity

Participants convert charts and analytical results into structured insights and management recommendations.

Module 14: Data Storytelling and Executive Presentation

Purpose of data storytelling
Understanding the audience
Identifying the main message
Structuring an analytical presentation
Building a logical narrative
Presenting the business context
Explaining the analytical question
Selecting supporting evidence
Highlighting key findings
Presenting implications and recommendations
Using visual emphasis effectively
Writing insight-driven chart titles
Creating executive summaries
Presenting detailed analysis to technical users
Presenting concise findings to management
Managing technical terminology
Explaining assumptions and limitations
Responding to questions and objections
Delivering concise and confident presentations

Presentation Preparation Activity

Participants prepare an executive summary and management presentation based on their dashboard and analytical findings.

Module 15: Data Governance, Privacy, and Responsible Analytics

Introduction to data governance
Data ownership and stewardship
Data classification
Confidential, sensitive, and personal information
Role-based access and authorized use
Data privacy principles
Secure storage and sharing of reports
Protecting source files and exported data
Maintaining calculation transparency
Documenting data sources
Maintaining version control
Avoiding selective or misleading reporting
Identifying potential data and analytical bias
Responsible use of automated analytics
Human validation of analytical results
Establishing a report review and approval process

Practical Activity

Participants assess the governance, privacy, and ethical risks associated with a sample dashboard.

Module 16: Final Data Analytics and Visualization Capstone Project

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

Understanding the business problem
Defining analytical questions
Identifying primary and supporting data
Creating a data requirements matrix
Assessing data quality
Cleaning and transforming the data
Combining multiple data sources
Retrieving related information using SQL
Developing a basic data model
Creating calculated metrics and KPIs
Conducting trend, variance, contribution, and segmentation analysis
Building an interactive dashboard
Identifying significant findings and performance drivers
Developing decision-ready insights
Preparing evidence-based recommendations
Presenting the findings to management or stakeholders

Final Presentation Activity

Each participant or group presents:

Business problem and analytical objectives
Primary and supporting data
Data-quality findings
Data-preparation approach
SQL analysis
Data model
Metrics and KPIs
Interactive dashboard
Trends and performance drivers
Key business insights
Recommended actions
Assumptions and limitations

Day 6 and Final Course Outputs

Completed interactive business intelligence dashboard
Structured analytical findings
Decision-ready business insights
Prioritized recommendations
Governance and privacy assessment
Executive summary
Management presentation
Completed capstone project

Recommended Software and Tools

Microsoft Excel 2019, 2021, or Microsoft 365
Microsoft Power Query
Microsoft Power Pivot, when available
Microsoft Power BI Desktop
Microsoft SQL Server, MySQL, or PostgreSQL
SQL Server Management Studio or another SQL client
Microsoft PowerPoint

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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

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Replenishment coordination and stock availability.
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Packing, staging, and dispatch confirmation.
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Item master data, units of measure, and location records.
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Common causes of physical and system stock mismatches.
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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.
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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.
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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

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