Data Analysis and Business Intelligence

Day One: Introduction to Data Analysis and Business Intelligence

1. Objectives:

   • Definition of data analysis and business intelligence and their importance in achieving organizational goals.

   • Review of the basic tools and techniques used.

2. The interviewer:

   • Definition of Data Analytics and the stages of work.

   • The concept of Business Intelligence and its role in decision-making.

   • The relationship between data analysis and business intelligence.

3. Learning method:

   • Theoretical lecture.

   • Interactive discussions.

   • Review of real-life examples.

Day Two: Data Collection and Processing Tools

1. Objectives:

   • Identifying methods for data collection and processing.

   • Understanding the importance of clean and reliable data.

2. The interviewer:

   • Data sources (internal and external).

   • Data collection tools (such as Excel and SQL).

   • Data cleaning and preparation for analysis.

3. Learning method:

   • Practical explanation using software tools.

   • Practical applications on real cases.

Day Three: Data Analysis using Modern Tools

1. Objectives:

   • Using data analysis tools such as Power BI, Tableau, and Python.

   • Understanding methods for descriptive and predictive data analysis.

2. The interviewer:

   • Introduction to Power BI and Tableau.

   • Descriptive and predictive analysis using Python.

   • Exploring patterns and relationships in the data.

3. Learning method:

   • Practical training on software tools.

   • Short individual and group projects.

Day Four: Business Intelligence and Decision Making

1. Objectives:

   • Using business intelligence to support decision-making.

   • Building effective dashboards.

2. The interviewer:

   • Data analysis for making strategic decisions.

   • Designing interactive dashboards.

   • Performance measurement using Key Performance Indicators (KPIs).

3. Learning method:

   • Implementation of small projects to build information boards.

   • Review sessions for the outputs.

Day Five: Practical Applications and Final Project

1. Objectives:

   • Applying the acquired knowledge to a comprehensive practical project.

   • Discussion of the main challenges in data analysis and business intelligence.

2. The interviewer:

   • Reviewing case studies from various sectors.

   • Implementation of a complete practical project (data analysis + dashboard creation).

   • Review and receive feedback.

3. Learning method:

   • Teamwork on the final project.

   • Presenting and discussing projects in front of the group.

Target audience of the course:

• Those working in the field of data analysis or business intelligence.

• Project managers and decision-makers who seek to analyze data more deeply.

• Those interested in developing their skills in modern tools such as Power BI, Tableau, and Python.

• Students and graduates wishing to enter the field of data analysis and business intelligence.

Learning methods in the course:

• Interactive lectures.

• Practical training using modern tools.

• Individual and group realistic projects.

• Review and feedback sessions to improve performance.