Excel for Data Analysis: From Tables to Advanced Insights

A practical 5-day data-analysis program covering Excel data import, Power Query, formulas, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, dashboards, and business insights 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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5 Days

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Course Details

Overview

The "Excel for Data Analysis: From Tables to Advanced Insights" 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 Excel data import, Power Query, formulas, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, dashboards, and business insights.

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.
Clean, transform, prepare, validate, and reshape data using appropriate tools, rules, repeatable workflows, and quality checks.
Build practical analytical outputs such as queries, formulas, data models, dashboards, charts, reports, notebooks, measures, workflows, or project deliverables.
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.
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

Etl Workflows - Data Profiling

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. Covers missing-value diagnosis, deletion, imputation, flagging, and impact assessment. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities. Key source references: 3 ملفات, CSV, Excel.

Pivottables - Report Structure

Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: 3, PivotTables.

Measures - Pivottables

Covers measures, calculated columns, filter context, row context, CALCULATE, FILTER, ALL, RELATED, and time-intelligence calculations. Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. 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. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: Power Pivot, DAX.

Linear Regression

Covers linear regression, coefficients, model interpretation, residuals, prediction, and business use cases.

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. Key source references: Solver.

Dashboard Design Principles

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: Excel Tables

Excel Tables

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, 3, 2, 4.

Applied Data Analysis Topic

Covers relational database querying, SELECT, filtering, sorting, joins, aggregation, subqueries, CTEs, and analytical extraction. 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, CSV, 8, 2, SQL, 3, 4.

Etl Workflows

Covers ETL workflows, data import, type conversion, merging, appending, unpivoting, conditional columns, missing values, and repeatable transformations. Key source references: Power Query.

Etl Workflows - Data Profiling

Covers duplicate detection, duplicate removal, record matching, and validation of unique keys. 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, 0, 2, 3, 4, 5, 1000, 500.

Practical Workshop - Data Profiling

Day 2: Pivottables

Pivottables

Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. 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.

Pivottables

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. Key source references: 1, 2, 3, 4, 5.

Applied Data Analysis Topic

Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. Key source references: 1, 2, 3, 4.

PivotCharts (PivotCharts)

Pivottables

Practical Workshop

Day 3: Measures - Pivottables

Pivottables

Covers measures, calculated columns, filter context, row context, CALCULATE, FILTER, ALL, RELATED, and time-intelligence calculations. Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: Power Pivot, Excel, DAX.

Pivottables

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. Covers window functions, partitions, ranking, running totals, moving averages, and analytical SQL calculations. 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, Excel, 2, 3, Power Pivot.

Pivottables

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. 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.

Pivottables

Covers measures, calculated columns, filter context, row context, CALCULATE, FILTER, ALL, RELATED, and time-intelligence calculations. Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. 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: DAX, Power Pivot, Excel, RELATED.

Pivottables

Covers measures, calculated columns, filter context, row context, CALCULATE, FILTER, ALL, RELATED, and time-intelligence calculations. 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. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: DAX, CALCULATE, SAMEPERIODLASTYEAR.

Pivottables

Practical Workshop - Pivottables

Day 4: Applied Data Analysis Topic

Descriptive Statistics

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers descriptive statistics, probability, sampling, inference, confidence intervals, hypothesis testing, correlation, and regression. Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Covers report structure, findings, recommendations, executive communication, and actionable insight delivery. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, Excel, 2, 3.

Correlation Analysis

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Covers correlation analysis, relationship strength, direction, scatter plots, and interpretation limits. 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, Excel, 0, 7.

Linear Regression

Covers linear regression, coefficients, model interpretation, residuals, prediction, and business use cases. 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. 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, 0, 05, 3, 4.

Applied Data Analysis Topic

Covers Excel tables, formulas, Power Query, PivotTables, PivotCharts, Power Pivot, statistics, what-if analysis, and dashboards. Key source references: Solver, Excel, 1, 2, 3, 4.

Practical Workshop - Linear Regression

Day 5: Hands-On Project Work

Dashboard Design Principles

Etl Workflows - Dashboard Design Principles - Pivottables

Covers ETL workflows, data import, type conversion, merging, appending, unpivoting, conditional columns, missing values, and repeatable transformations. Covers measures, calculated columns, filter context, row context, CALCULATE, FILTER, ALL, RELATED, and time-intelligence calculations. Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. 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: 1, 4 ملفات, CSV, Power Query, 2, Power Pivot, 3, DAX, 4, PivotTables, PivotCharts, 5.

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 ETL workflows, data import, type conversion, merging, appending, unpivoting, conditional columns, missing values, and repeatable transformations. Covers dashboard design principles, executive KPIs, interactive filters, navigation, visual storytelling, and publishing. Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. Covers data profiling, duplicates, missing values, inconsistent formats, outliers, data types, validation, and quality checks. Covers linear regression, coefficients, model interpretation, residuals, prediction, and business use cases. 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. 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: 3, CSV, 1, Power Query, 2, Power Pivot, 4, 5, Dashboard, 6, 10 دقائق.

Final Test and Course Review - Assessment

Enrollment Summary

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Excel for Data Analysis: From Tables to Advanced Insights

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.
Clean, transform, prepare, validate, and reshape data using appropriate tools, rules, repeatable workflows, and quality checks.
Build practical analytical outputs such as queries, formulas, data models, dashboards, charts, reports, notebooks, measures, workflows, or project deliverables.
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.
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