Data Analysis and Business Intelligence

Day One: Introduction to Data Analysis and Business Intelligence

• Today's goals:

  • Introducing the participants to the concept of data analysis and business intelligence.

  • Reviewing the importance of data analysis in making business decisions.

  • Familiarization with data analysis tools and techniques.

• Content:

  • What is data analysis?

  • What is business intelligence?

  • The difference between traditional analysis and data analysis using artificial intelligence.

  • Introduction to the tools used (Excel, Power BI, Tableau).

• Learning method:

  • Interactive lectures.

  • Group discussions.

  • Presenting practical examples.

Day Two: Data Collection and Analysis

• Today's goals:

  • Understanding the importance of data collection.

  • Exploring methods of data collection and organization.

  • Learning how to clean and prepare data for analysis.

• Content:

  • Data sources (internal and external).

  • Data collection techniques (surveys, databases, API).

  • Cleaning data using tools like Excel or Python.

  • Dealing with missing data and outliers.

• Learning method:

  • Practical workshops.

  • Applications of data collection and analysis tools.

  • Real case studies.

Day Three: Statistical Data Analysis

• Today's goals:

  • Learn the statistical basics for data analysis.

  • Using statistical tools to interpret data.

• Content:

  • Basic statistical concepts (mean, standard deviation, distribution).

  • Data analysis using tables and graphs.

  • Understanding the relationship between variables using regression analysis.

  • Data analysis tools using Power BI or Python (Pandas).

• Learning method:

  • Practical applications on real data.

  • Analysis of a set of statistical data.

  • Individual and group training.

Day Four: Designing Dashboards using Power BI

• Today's goals:

  • Learn how to design interactive dashboards.

  • Understanding the importance of data visualization.

• Content:

  • Introduction to Power BI.

  • Designing interactive reports using Power BI.

  • Creating dynamic dashboards.

  • Strategies for interpreting and presenting data effectively.

• Learning method:

  • Practical training on Power BI.

  • Creating dashboards using real data.

  • Sharing patterns and techniques with colleagues.

Day Five: Practical Applications in Business Intelligence and Decision Making

• Today's goals:

  • Understanding how to apply data analysis in improving business performance.

  • Exploring the role of business intelligence in strategic decision-making.

• Content:

  • Practical applications in the business industry (sales, marketing, finance).

  • Case study on the application of data analysis in decision-making.

  • The use of artificial intelligence and machine learning in data analysis.

• Learning method:

  • Discussion of case studies.

  • Practical applications of business intelligence tools.

  • Solving business problems using data analysis.

Target audience of the course:

• Data analysts.

• Business and project managers.

• Marketing department employees.

• IT professionals.

• Anyone interested in learning data analysis and deriving business value from it.

Learning method:

• A mix of theoretical and practical lectures.

• Interactive workshops and solving real problems.

• Case studies.

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