Data Analyst Training

Training Duration: 10 Days (09:00–12:00 Theory, 13:00–16:00 Practice)
Level: Beginner + Intermediate/Advanced

Participant Profile

• University students and recent graduates
• Professionals working with data in fields such as business, economics, engineering, health, etc.
• Employees in public institutions (municipalities, governorates, etc.) and the private sector who need data analysis skills
• Managers and administrative staff who want to contribute to data-driven decision-making processes

Prerequisites / Requirements

• Basic programming knowledge (can be provided through Preparatory Courses)
• Basic knowledge of statistics and mathematics
• Ability to follow technical English sources (recommended)

Curriculum

Week 1: Beginner Level

Day 1: Introduction to Data Analytics
• Role of a data analyst and its importance in business
• Data types (numerical, categorical, text) and data sources
• Data lifecycle

Day 2: Data Preparation and Cleaning
• Missing data and data imputation methods
• Data standardization, normalization, outliers
• Practical data cleaning exercises

Day 3: Basic Statistics and Probability
• Mean, median, mode, variance, standard deviation
• Difference between correlation and causation
• Applied basic statistical analyses

Day 4: Data Visualization
• Types of charts and their uses (bar, line, scatter, boxplot)
• Table creation and visualization principles
• Dashboard concept and examples

Day 5: Mini Project – Basic Analysis
• Analysis and visualization on a small dataset
• Group presentations and evaluation

Week 2: Intermediate / Advanced Level

Day 6: Introduction to Databases and SQL
• Database logic, relational databases
• Basic SQL queries (SELECT, WHERE, GROUP BY, JOIN)
• Practical SQL exercises

Day 7: Data Analysis with Programming
• Data processing logic with Python or R
• Introduction to data libraries (pandas, numpy, ggplot – conceptual level)
• Basic data analysis applications

Day 8: Business Intelligence and Reporting
• Business intelligence concepts
• Conceptual use of tools like Power BI and Tableau
• Reporting and contribution to decision support systems

Day 9: Advanced Statistical Methods
• Regression analysis, hypothesis testing
• Time series analysis and forecasting awareness
• Applications with real datasets

Day 10: Final Project – Applied Data Analysis
• Participants analyze a dataset relevant to their field
• Visualization and reporting
• Presentation and evaluation

Training Outcomes

• Participants gain skills in data preparation, analysis and visualization.
• They are introduced to SQL and programming-based data analysis.
• They learn business intelligence and reporting concepts.
• They gain project experience using real datasets.
• They can apply statistical methods to both business and academic environments.

Training Notes

• The instructor may adapt the content according to participant profile.
• The program is independent of technologies and focuses on core principles.
• Participants receive a university-approved certificate upon completion.