Advanced Tools and Techniques in Data Analysis

Objectives:

1. Understanding the fundamentals and techniques of advanced data analysis:

   • Familiarizing oneself with modern tools and techniques in the field of data analysis.

   • Learn how to choose the appropriate tools according to the type of data and analyze it.

2. Mastering advanced statistical analysis techniques:

   • Using advanced statistical methods such as regression models, predictive analysis, and multivariate data analysis.

   • Applying machine learning techniques in data analysis.

3. Learn to use advanced software tools:

   • Training on analytical tools such as R, Python (using libraries like Pandas, NumPy, Scikit-learn), and Power BI.

   • Learn how to use SQL to query and clean data.

4. Preparing advanced reports and visual analyses:

   • Learn how to create interactive charts and reports using the appropriate tools like Tableau and Power BI.

   • Learn how to present analysis results in a visual and effective way.

5. Developing strategic analysis skills:

   • Gaining the ability to analyze big data and extract important patterns.

   • Understanding how to apply analysis in making strategic decisions in companies.

Target audience of the course:

1. Data Analysts:

   • Data analysts with prior experience who wish to enhance their skills in advanced tools.

2. Project Managers:

   • People responsible for data-driven decision-making who wish to improve their skills in using advanced analytical tools.

3. Data Developers:

   • People working in software development and data analysis who want to learn advanced analytical techniques.

4. Students and graduates of Computer Science and Statistics:

   • Students and graduates of Computer Science and Statistics:

Learning method:

1. Theoretical learning:

   • Theoretical lectures to introduce participants to advanced analysis concepts and techniques.

   • Real case studies to explain how technologies are applied in different fields.

2. Practical learning:

   • Practical workshops using advanced software tools such as R, Python, and Power BI.

   • Practical exercises to analyze real data in order to enhance understanding and apply skills.

3. Group projects:

   • Dividing participants into small groups to work on real data analysis projects.

   • Presenting the projects and discussing the methods used in the analysis.

4. Continuous assessment:

   • Short tests at the end of each day to ensure content comprehension.

   • Final project evaluation at the end of the course to apply the acquired concepts.

Course schedule:

In this way, the course will be diverse between theoretical and practical practice, which will help participants understand advanced tools and apply them effectively.