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 like business, economics, engineering, healthcare, etc.
- Employees in public institutions (municipalities, governorships, etc.) and private sector requiring data analysis skills
- Managers and administrative staff looking to contribute to data-driven decision-making processes
Prerequisites / Requirements
- Basic programming knowledge (can be provided with preparation courses)
- Basic knowledge of statistics and mathematics
- Ability to follow technical English resources (recommended)
Curriculum
Week 1: Beginner Level
Day 1: Introduction to Data Analytics
• The role of a data analyst and its importance in business
• Types of data (numerical, categorical, textual) and data sources
• Data lifecycle
Day 2: Data Preparation and Cleaning
• Handling missing data and data imputation methods
• Data standardization, normalization, outliers
• Hands-on data cleaning exercises
Day 3: Basic Statistics and Probability
• Mean, median, mode, variance, standard deviation
• Correlation vs causality
• Hands-on basic statistical analyses
Day 4: Data Visualization
• Types of graphs and their use cases (bar, line, scatter, box plots)
• Creating tables and visualization principles
• Dashboard concepts and examples
Day 5: Mini Project – Basic Analysis
• Analyzing and visualizing 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)
• Hands-on SQL exercises
Day 7: Data Analysis with Programming
• Data processing with Python or R
• Introduction to data libraries (pandas, numpy, ggplot – conceptual level)
• Basic data analysis applications
Day 8: Business Intelligence and Reporting
• Concept of business intelligence
• Conceptual use of tools like Power BI, Tableau
• Contribution to reporting and decision support systems
Day 9: Advanced Statistical Methods
• Regression analysis, hypothesis testing
• Time series analysis and forecasting awareness
• Practical exercises with real-world 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 will gain skills in data preparation, analysis, and visualization.
- Introduction to SQL and programming-based data analysis.
- Learn business intelligence and reporting concepts.
- Gain project experience with real-world datasets.
- Apply statistical methods to business and academic contexts.
Training Notes
- The instructor can tailor the content to participants' profiles.
- The program is independent of technology, focusing on core principles.
- Upon completion, participants will receive a university-approved certificate.