Tableau for Data Visualization: From Data to Interactive Stories

A practical 5-day data-analysis program covering Tableau Desktop, data connections, dimensions and measures, calculated fields, filters, interactive dashboards, storytelling, publishing, and visual analytics best practices with hands-on labs, datasets, analytical outputs, and business reporting.

Certificate Included

Expert-Led Training

Practical Learning

Enrollment Support

days

5 Days

Language

English / Arabic

Course Summary

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Flexible / Always Available

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Accredited Certificate

days

5 Days

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Course Details

Overview

The "Tableau for Data Visualization: From Data to Interactive Stories" course is an applied professional training program within the Data Analysis Programs category. It is designed to enable participants to convert raw data into reliable analytical outputs, business insights, interactive reports, dashboards, models, queries, scripts, statistical results, and actionable recommendations related to Tableau Desktop, data connections, dimensions and measures, calculated fields, filters, interactive dashboards, storytelling, publishing, and visual analytics best practices.

The program follows the full source structure and moves participants from essential concepts to hands-on application through datasets, practical labs, business scenarios, software demonstrations, analytical exercises, transformation workflows, visualization tasks, project deliverables, and final review activities aligned with data-analysis and business-intelligence needs.

Objectives

Program Objectives

By the end of this course, participants will be able to:

Connect to, import, extract, combine, and organize data from multiple sources such as spreadsheets, databases, web services, cloud platforms, and business systems.
Build practical analytical outputs such as queries, formulas, data models, dashboards, charts, reports, notebooks, measures, workflows, or project deliverables.
Clean, transform, prepare, validate, and reshape data using appropriate tools, rules, repeatable workflows, and quality checks.
Apply analytical techniques to identify patterns, trends, outliers, relationships, performance gaps, risks, business opportunities, and evidence-based recommendations.
Use realistic datasets and business cases to convert technical steps into meaningful analytical outputs.
Prepare clear reports, dashboards, summaries, and recommendations for managers, teams, and decision makers.
Apply quality, accuracy, privacy, and evidence-based thinking when handling data and communicating insights.

Target Audience

This program is designed for the following target groups:

Data analysts, business analysts, business-intelligence professionals, reporting specialists, and analytics teams.
Department managers, team leaders, and functional professionals in sales, marketing, finance, HR, operations, and management roles who make data-driven decisions.
IT administrators, system administrators, cloud administrators, security professionals, database users, and technology teams working with data platforms.
Accountants, auditors, economists, statisticians, researchers, and professionals who use analytical evidence in their work.
Students, graduates, and early-career professionals in business, accounting, engineering, economics, statistics, computer science, and information technology.
Entrepreneurs, SME owners, and anyone who wants to strengthen practical data-analysis and reporting skills.

Competencies

Core Competencies

Tableau Data Connections

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers data profiling, duplicates, missing values, inconsistent formats, outliers, data types, validation, and quality checks. Key source references: CSV, Excel.

Applied Data Analysis Topic

Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations.

Applied Data Analysis Topic

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers.

Dashboard Design Principles

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work. Key source references: 1, 2, 3, 10.

Applied Data Analysis Topic

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations.

Applied Data Analysis Topic

Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, 2, 3, 4.

Learning Journey

Program Outline

Day: Applied Data Analysis Topic

Applied Data Analysis Topic

Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: 5 أيام, 25 ساعة.

Day 1: Tableau Data Connections

Tableau Data Connections

Covers Power BI Desktop, Power Query, data models, DAX, visualizations, Power BI Service, dashboards, sharing, and scheduled refresh. Covers Tableau data connections, worksheets, calculated fields, filters, dashboards, stories, publishing, and visual analytics. Covers data profiling, duplicates, missing values, inconsistent formats, outliers, data types, validation, and quality checks. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: Tableau, Power BI.

Tableau Data Connections

Covers Tableau data connections, worksheets, calculated fields, filters, dashboards, stories, publishing, and visual analytics. Key source references: Tableau, 14.

Applied Data Analysis Topic

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers relational database querying, SELECT, filtering, sorting, joins, aggregation, subqueries, CTEs, and analytical extraction. Key source references: Excel, CSV, SQL, Google Analytics, Salesforce.

Tableau Data Connections

Covers Tableau data connections, worksheets, calculated fields, filters, dashboards, stories, publishing, and visual analytics. Covers data profiling, duplicates, missing values, inconsistent formats, outliers, data types, validation, and quality checks. Covers duplicate detection, duplicate removal, record matching, and validation of unique keys. Key source references: Tableau, CSV.

Applied Data Analysis Topic

Covers charts, visual encoding, chart selection, formatting, comparison, trend analysis, and visual communication.

Practical Workshop - Data Profiling - Charts

Covers Tableau data connections, worksheets, calculated fields, filters, dashboards, stories, publishing, and visual analytics. Covers data profiling, duplicates, missing values, inconsistent formats, outliers, data types, validation, and quality checks. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: Tableau.

Day 2: Applied Data Analysis Topic

Applied Data Analysis Topic

Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, 2, 3, 4.

Applied Data Analysis Topic

Covers Tableau data connections, worksheets, calculated fields, filters, dashboards, stories, publishing, and visual analytics. Covers charts, visual encoding, chart selection, formatting, comparison, trend analysis, and visual communication. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Key source references: Tableau.

Applied Data Analysis Topic

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Key source references: 1, 2, 3, 4.

Applied Data Analysis Topic

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: 1, 2, 3.

Practical Workshop

Day 3: Dashboard Design Principles

Dashboard Design Principles

Applied Data Analysis Topic

Tableau Data Connections

Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: 1, 2, 3, 4.

Applied Data Analysis Topic

Covers assessment, quizzes, simulation tests, exam-readiness checks, and review of core concepts. Key source references: 1, 2, 3, 4, 5.

Practical Workshop - Dashboard Design Principles

Day 4: Tableau Data Connections

Applied Data Analysis Topic

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: 1, 2, 3.

Applied Data Analysis Topic

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity.

Applied Data Analysis Topic

Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Key source references: DATEADD.

Practical Workshop

Day 5: Hands-On Project Work

Applied Data Analysis Topic

Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers.

Applied Data Analysis Topic

Tableau Data Connections

Covers SharePoint sites, libraries, permissions, pages, document management, and collaboration governance. Covers Tableau data connections, worksheets, calculated fields, filters, dashboards, stories, publishing, and visual analytics. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity. Key source references: 1, 2, Tableau, 3, SharePoint, 4.

Tableau Data Connections

Applied Data Analysis Topic

Hands-On Project Work

Practical application: participants work individually or in small teams on realistic datasets, business scenarios, cloud environments, dashboards, database queries, notebooks, visualization files, or analytics projects using structured templates and guided steps. Covers Tableau data connections, worksheets, calculated fields, filters, dashboards, stories, publishing, and visual analytics. Covers data profiling, duplicates, missing values, inconsistent formats, outliers, data types, validation, and quality checks. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, CSV, 2, Tableau, 3, 4, 5, 6, 7.

Final Test and Course Review - Assessment

Enrollment Summary

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Tableau for Data Visualization: From Data to Interactive Stories

Certificate Included

Expert-Led Training

Practical Learning

Enrollment Support

days

5 Days

Language

English / Arabic

Overview

Program Objectives

By the end of this course, participants will be able to:

Connect to, import, extract, combine, and organize data from multiple sources such as spreadsheets, databases, web services, cloud platforms, and business systems.
Build practical analytical outputs such as queries, formulas, data models, dashboards, charts, reports, notebooks, measures, workflows, or project deliverables.
Clean, transform, prepare, validate, and reshape data using appropriate tools, rules, repeatable workflows, and quality checks.
Apply analytical techniques to identify patterns, trends, outliers, relationships, performance gaps, risks, business opportunities, and evidence-based recommendations.
Use realistic datasets and business cases to convert technical steps into meaningful analytical outputs.
Prepare clear reports, dashboards, summaries, and recommendations for managers, teams, and decision makers.
Apply quality, accuracy, privacy, and evidence-based thinking when handling data and communicating insights.

Target Audience

This program is designed for the following target groups:

Data analysts, business analysts, business-intelligence professionals, reporting specialists, and analytics teams.
Department managers, team leaders, and functional professionals in sales, marketing, finance, HR, operations, and management roles who make data-driven decisions.
IT administrators, system administrators, cloud administrators, security professionals, database users, and technology teams working with data platforms.
Accountants, auditors, economists, statisticians, researchers, and professionals who use analytical evidence in their work.
Students, graduates, and early-career professionals in business, accounting, engineering, economics, statistics, computer science, and information technology.
Entrepreneurs, SME owners, and anyone who wants to strengthen practical data-analysis and reporting skills.

Core Competencies