AI and Data Science
AI and Data Science is a practical course for students who want to understand how modern AI systems are built. Students start from Python and data foundations, then move into data analysis, visualization, machine learning, model evaluation, neural network basics and project-based AI development. The course focuses on understanding the logic behind models and using them correctly in real tasks.
Advantages
- Students learn AI step by step: from data and Python to real machine learning projects.
- The course develops strong fundamentals instead of only using ready-made tools.
- Every major topic is connected to datasets, experiments and model evaluation.
What is included in the program?
- Python foundations for AI and data work
- Data cleaning, analysis, visualization and feature understanding
- Classical machine learning: regression, classification, clustering and trees
- Model training, testing, accuracy, overfitting and evaluation
- Introduction to neural networks and deep learning concepts
- Final AI/Data Science project with presentation
Program modules
The program is organized as a connected learning path: theoretical foundations, practical labs, prototype development, testing, and presentation.
Python for AI
Python practice notebookStudents learn variables, conditions, loops, functions, lists, files and libraries needed for AI and data tasks.
Data analysis foundations
Data analysis reportStudents work with tables, datasets, missing values, simple statistics, visualization and data interpretation.
Machine learning basics
First ML modelStudents learn features, labels, training data, supervised and unsupervised learning, and prediction logic.
Regression and classification
Regression/classification mini-projectStudents build models for prediction and decision-making tasks and compare model behavior.
Final AI/Data Science project
Final project presentationStudents choose a dataset, define a problem, prepare data, train a model, evaluate results and present conclusions.