Duration: 4–6 months
Format: online lectures + practical training + project + mentoring support
Module 1. Python Basics
- Syntax basics and data types;
- Conditional statements and loops;
- Functions and working with files;
- Practical assignments in Python.
Module 2. Working with data
- NumPy — working with arrays and matrices;
- Pandas — data processing and analysis;
- Visualization (Matplotlib, Seaborn) — graphs, charts, and reports;
- Practical work: analyzing small datasets.
Module 3. SQL for analytics
- SELECT, JOIN;
- Data aggregation and grouping;
- Subqueries;
- Practice with real datasets.
Module 4. Machine learning (basic level)
- Linear regression;
- Data classification;
- Model training with Scikit-Learn;
- Model quality metrics;
- Practice: building your first models.
Module 5. BI tools
- Power BI / Tableau — data visualization and dashboards;
- Creating interactive reports;
- Presenting analysis results.
Final project
- Dataset → analysis → visualization → model → presentation.