Data Science and Data Analysis

A practical AI business program covering data science lifecycle, business questions, data collection, data understanding, and analytical thinking, Python basics, SQL basics, data structures, datasets, data types, and data preparation, data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data, SQL queries, joins, aggregations, and analytical datasets, regression, classification, clustering, model evaluation, and machine learning fundamentals, with case studies, templates, tool demonstrations, hands-on exercises, and applied outputs for workplace implementation.

Certificate Included

Expert-Led Training

Practical Learning

Enrollment Support

days

5 Days

Language

English / Arabic

Course Summary

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Start Date

Flexible / Always Available

Certificate

Accredited Certificate

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5 Days

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Course Details

Overview

The Data Science and Data Analysis course is a practical artificial intelligence and business applications program designed to build applied capability in data science lifecycle, business questions, data collection, data understanding, and analytical thinking, Python basics, SQL basics, data structures, datasets, data types, and data preparation, data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data, SQL queries, joins, aggregations, and analytical datasets. It helps participants understand how AI can improve productivity, decision-making, customer value, operational efficiency, risk control, and innovation.

The program covers data science lifecycle, business questions, data collection, data understanding, and analytical thinking, Python basics, SQL basics, data structures, datasets, data types, and data preparation, data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data, SQL queries, joins, aggregations, and analytical datasets, along with regression, classification, clustering, model evaluation, and machine learning fundamentals, Power BI, Tableau, dashboards, KPIs, interactive reports, and executive insights, capstone data project, analytical communication, and decision-oriented recommendations. Participants work with realistic business cases, AI tools, templates, demonstrations, scenario analysis, and applied exercises that can be adapted to their organization.

The course is designed for approximately 5 training day(s), with emphasis on practical execution, responsible AI use, data-driven thinking, governance awareness, and measurable workplace outputs.

Objectives

Program Objectives

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

Understand data science lifecycle, business questions, data collection, data understanding, and analytical thinking in realistic business scenarios related to Data Science and Data Analysis.
Apply Python basics, SQL basics, data structures, datasets, data types, and data preparation in realistic business scenarios related to Data Science and Data Analysis.
Analyze data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data in realistic business scenarios related to Data Science and Data Analysis.
Evaluate SQL queries, joins, aggregations, and analytical datasets in realistic business scenarios related to Data Science and Data Analysis.
Develop regression, classification, clustering, model evaluation, and machine learning fundamentals in realistic business scenarios related to Data Science and Data Analysis.
Produce a practical work output such as an AI use-case brief, prompt library, analytical model, dashboard, governance checklist, implementation roadmap, or business-ready AI workflow according to the course topic.

Target Audience

This training program is designed for the following target audiences:

Business leaders, managers, supervisors, specialists, analysts, consultants, and professionals who need to understand or apply AI in their work.
Analysts, reporting staff, data practitioners, graduates, and professionals who want practical data, Python, SQL, dashboard, and analytical skills.
Organizations seeking practical, responsible, and measurable AI adoption across business functions.

Competencies

Core Competencies

Apply Data science lifecycle, business questions, data collection, data understanding, and analytical thinking

Ability to apply data science lifecycle, business questions, data collection, data understanding, and analytical thinking within Data Science and Data Analysis, using appropriate data, tools, templates, governance controls, practical analysis, and business-oriented recommendations.

Apply Python basics, SQL basics, data structures, datasets, data types, and data preparation

Ability to apply Python basics, SQL basics, data structures, datasets, data types, and data preparation within Data Science and Data Analysis, using appropriate data, tools, templates, governance controls, practical analysis, and business-oriented recommendations.

Apply Data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data

Ability to apply data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data within Data Science and Data Analysis, using appropriate data, tools, templates, governance controls, practical analysis, and business-oriented recommendations.

Apply SQL queries, joins, aggregations, and analytical datasets

Ability to apply SQL queries, joins, aggregations, and analytical datasets within Data Science and Data Analysis, using appropriate data, tools, templates, governance controls, practical analysis, and business-oriented recommendations.

Apply Regression, classification, clustering, model evaluation, and machine learning fundamentals

Ability to apply regression, classification, clustering, model evaluation, and machine learning fundamentals within Data Science and Data Analysis, using appropriate data, tools, templates, governance controls, practical analysis, and business-oriented recommendations.

Apply Power BI, Tableau, dashboards, KPIs, interactive reports, and executive insights

Ability to apply Power BI, Tableau, dashboards, KPIs, interactive reports, and executive insights within Data Science and Data Analysis, using appropriate data, tools, templates, governance controls, practical analysis, and business-oriented recommendations.

Learning Journey

Program Outline

Day 1: Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

This session develops practical competence in data science lifecycle, business questions, data collection, data understanding, and analytical thinking as part of Data Science and Data Analysis. Participants review relevant concepts, examine realistic business or organizational scenarios, use AI tools or templates where applicable, discuss risks and governance considerations, and prepare an applied output that supports productivity, insight, responsible AI use, and workplace implementation.

Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

This session develops practical competence in Python basics, SQL basics, data structures, datasets, data types, and data preparation as part of Data Science and Data Analysis. Participants review relevant concepts, examine realistic business or organizational scenarios, use AI tools or templates where applicable, discuss risks and governance considerations, and prepare an applied output that supports productivity, insight, responsible AI use, and workplace implementation.

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

This session develops practical competence in data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data as part of Data Science and Data Analysis. Participants review relevant concepts, examine realistic business or organizational scenarios, use AI tools or templates where applicable, discuss risks and governance considerations, and prepare an applied output that supports productivity, insight, responsible AI use, and workplace implementation.

Sql Queries, Joins, Aggregations, And Analytical Datasets

This session develops practical competence in SQL queries, joins, aggregations, and analytical datasets as part of Data Science and Data Analysis. Participants review relevant concepts, examine realistic business or organizational scenarios, use AI tools or templates where applicable, discuss risks and governance considerations, and prepare an applied output that supports productivity, insight, responsible AI use, and workplace implementation.

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

This session develops practical competence in regression, classification, clustering, model evaluation, and machine learning fundamentals as part of Data Science and Data Analysis. Participants review relevant concepts, examine realistic business or organizational scenarios, use AI tools or templates where applicable, discuss risks and governance considerations, and prepare an applied output that supports productivity, insight, responsible AI use, and workplace implementation.

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

This session develops practical competence in Power BI, Tableau, dashboards, KPIs, interactive reports, and executive insights as part of Data Science and Data Analysis. Participants review relevant concepts, examine realistic business or organizational scenarios, use AI tools or templates where applicable, discuss risks and governance considerations, and prepare an applied output that supports productivity, insight, responsible AI use, and workplace implementation.

Day 2: Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Sql Queries, Joins, Aggregations, And Analytical Datasets

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Day 3: Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Sql Queries, Joins, Aggregations, And Analytical Datasets

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

Capstone Data Project, Analytical Communication, And Decision-Oriented Recommendations

This session develops practical competence in capstone data project, analytical communication, and decision-oriented recommendations as part of Data Science and Data Analysis. Participants review relevant concepts, examine realistic business or organizational scenarios, use AI tools or templates where applicable, discuss risks and governance considerations, and prepare an applied output that supports productivity, insight, responsible AI use, and workplace implementation.

Day 4: Sql Queries, Joins, Aggregations, And Analytical Datasets

Sql Queries, Joins, Aggregations, And Analytical Datasets

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

Capstone Data Project, Analytical Communication, And Decision-Oriented Recommendations

Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Day 5: Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

Capstone Data Project, Analytical Communication, And Decision-Oriented Recommendations

Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Enrollment Summary

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Data Science and Data Analysis

Certificate Included

Expert-Led Training

Practical Learning

Enrollment Support

days

5 Days

Language

English / Arabic

Overview

The course is designed for approximately 5 training day(s), with emphasis on practical execution, responsible AI use, data-driven thinking, governance awareness, and measurable workplace outputs.

Program Objectives

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

Understand data science lifecycle, business questions, data collection, data understanding, and analytical thinking in realistic business scenarios related to Data Science and Data Analysis.
Apply Python basics, SQL basics, data structures, datasets, data types, and data preparation in realistic business scenarios related to Data Science and Data Analysis.
Analyze data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data in realistic business scenarios related to Data Science and Data Analysis.
Evaluate SQL queries, joins, aggregations, and analytical datasets in realistic business scenarios related to Data Science and Data Analysis.
Develop regression, classification, clustering, model evaluation, and machine learning fundamentals in realistic business scenarios related to Data Science and Data Analysis.
Produce a practical work output such as an AI use-case brief, prompt library, analytical model, dashboard, governance checklist, implementation roadmap, or business-ready AI workflow according to the course topic.

Target Audience

This training program is designed for the following target audiences:

Business leaders, managers, supervisors, specialists, analysts, consultants, and professionals who need to understand or apply AI in their work.
Analysts, reporting staff, data practitioners, graduates, and professionals who want practical data, Python, SQL, dashboard, and analytical skills.
Organizations seeking practical, responsible, and measurable AI adoption across business functions.

Core Competencies

Apply Data science lifecycle, business questions, data collection, data understanding, and analytical thinking

Apply Python basics, SQL basics, data structures, datasets, data types, and data preparation

Apply Data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data

Apply SQL queries, joins, aggregations, and analytical datasets

Apply Regression, classification, clustering, model evaluation, and machine learning fundamentals

Apply Power BI, Tableau, dashboards, KPIs, interactive reports, and executive insights

Program Outline

Day 1: Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Sql Queries, Joins, Aggregations, And Analytical Datasets

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

Day 2: Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Sql Queries, Joins, Aggregations, And Analytical Datasets

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Day 3: Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Sql Queries, Joins, Aggregations, And Analytical Datasets

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

Capstone Data Project, Analytical Communication, And Decision-Oriented Recommendations

Day 4: Sql Queries, Joins, Aggregations, And Analytical Datasets

Sql Queries, Joins, Aggregations, And Analytical Datasets

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

Capstone Data Project, Analytical Communication, And Decision-Oriented Recommendations

Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Day 5: Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

Power Bi, Tableau, Dashboards, Kpis, Interactive Reports, And Executive Insights

Capstone Data Project, Analytical Communication, And Decision-Oriented Recommendations

Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Data Science and Data Analysis

Request Schedule & Quotation

Overview

Program Objectives

Target Audience

Core Competencies

Apply Data science lifecycle, business questions, data collection, data understanding, and analytical thinking

Apply Python basics, SQL basics, data structures, datasets, data types, and data preparation

Apply Data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data

Apply SQL queries, joins, aggregations, and analytical datasets

Apply Regression, classification, clustering, model evaluation, and machine learning fundamentals

Apply Power BI, Tableau, dashboards, KPIs, interactive reports, and executive insights

Program Outline

Day 1: Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Day 2: Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Day 3: Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Day 4: Sql Queries, Joins, Aggregations, And Analytical Datasets

Day 5: Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

More courses open for booking

Explore Our Monthly Training Schedules

Data Science and Data Analysis

Request Schedule & Quotation

Overview

Program Objectives

Target Audience

Core Competencies

Apply Data science lifecycle, business questions, data collection, data understanding, and analytical thinking

Apply Python basics, SQL basics, data structures, datasets, data types, and data preparation

Apply Data cleaning, exploratory data analysis, visualization, descriptive statistics, and storytelling with data

Apply SQL queries, joins, aggregations, and analytical datasets

Apply Regression, classification, clustering, model evaluation, and machine learning fundamentals

Apply Power BI, Tableau, dashboards, KPIs, interactive reports, and executive insights

Program Outline

Day 1: Data Science Lifecycle, Business Questions, Data Collection, Data Understanding, And Analytical Thinking

Day 2: Python Basics, Sql Basics, Data Structures, Datasets, Data Types, And Data Preparation

Day 3: Data Cleaning, Exploratory Data Analysis, Visualization, Descriptive Statistics, And Storytelling With Data

Day 4: Sql Queries, Joins, Aggregations, And Analytical Datasets

Day 5: Regression, Classification, Clustering, Model Evaluation, And Machine Learning Fundamentals

More courses open for booking

Explore Our Monthly Training Schedules