Python for Data Analysis: From Beginner to Professional Using Pandas, NumPy, Matplotlib, and Seaborn

A practical 5-day data-analysis program covering Python programming basics, NumPy arrays, Pandas dataframes, data cleaning, exploratory data analysis, visualization with Matplotlib and Seaborn, and analytical projects 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

Request Schedule & Quotation

No online payment is open for this course yet. Our team can send you the suitable quotation.

Start Date

Flexible / Always Available

Certificate

Accredited Certificate

days

5 Days

Your quote arrives by email and in the client portal

Ask on WhatsApp

Course Details

Overview

The "Python for Data Analysis: From Beginner to Professional Using Pandas, NumPy, Matplotlib, and Seaborn" 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 Python programming basics, NumPy arrays, Pandas dataframes, data cleaning, exploratory data analysis, visualization with Matplotlib and Seaborn, and analytical projects.

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:

Build practical analytical outputs such as queries, formulas, data models, dashboards, charts, reports, notebooks, measures, workflows, or project deliverables.
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.
Understand the key concepts, methods, tools, platforms, analytical workflows, and professional requirements related to Python for Data Analysis: From Beginner to Professional Using Pandas, NumPy, Matplotlib, and Seaborn.
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

Data Profiling

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. Covers duplicate detection, duplicate removal, record matching, and validation of unique keys. Key source references: CSV.

Applied Data Analysis Topic

Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities.

Applied Data Analysis Topic

Covers Matplotlib charts, figure customization, labels, axes, and exploratory data visualization. Covers charts, visual encoding, chart selection, formatting, comparison, trend analysis, and visual communication. Key source references: Matplotlib.

Applied Data Analysis Topic

Covers Seaborn statistical visualization, distribution plots, relationship plots, categorical plots, and styling. Covers outlier detection, visual inspection, statistical thresholds, business rules, and treatment decisions. 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: Seaborn.

Correlation Analysis

Covers Seaborn statistical visualization, distribution plots, relationship plots, categorical plots, and styling. 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. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: Seaborn.

Applied Data Analysis Topic

Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. Includes calculations, formulas, measures, indicators, queries, scoring, statistical results, comparison metrics, and interpretation of numerical outputs based on the source activity.

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: Python Fundamentals

Applied Data Analysis Topic

Covers Python fundamentals, notebooks, data structures, data analysis libraries, scripts, automation, and reproducible analysis. Covers Pandas dataframes, series, indexing, filtering, grouping, merging, reshaping, cleaning, and analysis workflows. Covers NumPy arrays, vectorized operations, numerical calculations, statistical functions, and efficient computation. Covers Matplotlib charts, figure customization, labels, axes, and exploratory data visualization. Covers Seaborn statistical visualization, distribution plots, relationship plots, categorical plots, and styling. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: Python, NumPy, Pandas, Matplotlib, Seaborn.

Applied Data Analysis Topic

Covers the topic through structured explanations, applied examples, datasets, tool demonstrations, analytical exercises, and practical outputs aligned with data-analysis and business-intelligence work.

Applied Data Analysis Topic

Covers duplicate detection, duplicate removal, record matching, and validation of unique keys.

Applied Data Analysis Topic

Practical Workshop

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.

Day 2: Pandas Dataframes - Numpy Arrays

Numpy Arrays

Covers NumPy arrays, vectorized operations, numerical calculations, statistical functions, and efficient computation. 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: NumPy.

Pandas Dataframes

Covers Pandas dataframes, series, indexing, filtering, grouping, merging, reshaping, cleaning, and analysis workflows. Requires producing practical outputs such as queries, dashboards, reports, charts, data models, formulas, notebooks, cleaned datasets, transformation steps, or executive recommendations. Key source references: Pandas.

Excel Tables

Applied Data Analysis Topic

Covers missing-value diagnosis, deletion, imputation, flagging, and impact assessment.

Practical Workshop

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, CSV, 2, 10, 3, 4, 5.

Day 3: Pandas Dataframes - Data Profiling

Missing-Value Diagnosis

Covers missing-value diagnosis, deletion, imputation, flagging, and impact assessment. Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. 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: 1, 2, 3, 4.

Duplicate Detection

Covers duplicate detection, duplicate removal, record matching, and validation of unique keys. Includes identification, extraction, analysis, classification, measurement, profiling, comparison, prioritization, and interpretation of data, risks, quality issues, results, and business opportunities.

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

Practical Workshop - Data Profiling

Day 4: Pandas Dataframes

Applied Data Analysis Topic

PivotTables (Pivot Tables)

Practical Workshop

Covers PivotTables, PivotCharts, summarization, slicing, grouping, calculated fields, and interactive analysis. Covers mean, median, mode, variance, standard deviation, and interpretation of distributions. 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. Develops business communication skills through concise reports, insight summaries, visual explanations, recommendations, and final presentations for decision makers. Key source references: 1, 3 ملفات, 2, 3, 4, 5.

Day 5: Matplotlib Charts - Seaborn Statistical Visualization - Hands-On Project Work

Matplotlib Charts

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

Seaborn Statistical Visualization

Covers correlation analysis, relationship strength, direction, scatter plots, and interpretation limits.

Applied Data Analysis Topic

Covers Pandas dataframes, series, indexing, filtering, grouping, merging, reshaping, cleaning, and analysis workflows. Covers Matplotlib charts, figure customization, labels, axes, and exploratory data visualization. Covers Seaborn statistical visualization, distribution plots, relationship plots, categorical plots, and styling. 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. Covers descriptive statistics, probability, sampling, inference, confidence intervals, hypothesis testing, correlation, and regression. Covers correlation analysis, relationship strength, direction, scatter plots, and interpretation limits. Key source references: Pandas, Matplotlib, Seaborn, 1, 2, 3, 4, 5, 6, 7.

Practical Workshop - 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 Pandas dataframes, series, indexing, filtering, grouping, merging, reshaping, cleaning, and analysis workflows. Covers Matplotlib charts, figure customization, labels, axes, and exploratory data visualization. Covers Seaborn statistical visualization, distribution plots, relationship plots, categorical plots, and styling. Covers data profiling, duplicates, missing values, inconsistent formats, outliers, data types, validation, and quality checks. Covers report structure, findings, recommendations, executive communication, and actionable insight delivery. 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, 2, Pandas, Matplotlib, Seaborn, 3, 4.

Final Test and Course Review - Assessment

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

Enrollment Summary

Secure Course Registration

Request Schedule & Quotation

No online payment is open for this course yet. Our team can send you the suitable quotation.

Company Name

Full Name

Email Address

Phone Number

+966

Seats

Job Title / Position

Preferred Training Location

Required Certificate Type

Number of Courses

Expected Course Date

Message (Optional)

Quick security check — complete it to enable sending

Your quote arrives by email and in the client portal

Ask on WhatsApp

Python for Data Analysis: From Beginner to Professional Using Pandas, NumPy, Matplotlib, and Seaborn

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:

Build practical analytical outputs such as queries, formulas, data models, dashboards, charts, reports, notebooks, measures, workflows, or project deliverables.
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.
Understand the key concepts, methods, tools, platforms, analytical workflows, and professional requirements related to Python for Data Analysis: From Beginner to Professional Using Pandas, NumPy, Matplotlib, and Seaborn.
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