This course will introduce students to the fundamentals of data science and teach them how to collect, process, and visualize data using Python and popular libraries. Students will learn how to apply statistical methods and machine learning algorithms to analyze real-world datasets.
Course program
Introduction to data science and Python
- Python basics for data analysis;
- Installing and configuring the environment (Jupyter, Anaconda).
Working with data in Python
- NumPy and Pandas libraries;
- Data loading, cleaning, and preparation.
Data visualization
- Matplotlib and Seaborn libraries;
- Building graphs and charts.
Statistical data analysis
- Basics of statistics: mean, median, variance;
- Correlation and regression.
Basics of machine learning
- Introduction to supervised and unsupervised learning;
- Linear regression, classification.
Practical projects
- Analysis of real data sets;
- Model building and quality assessment.
Course duration
6–8 weeks (2 classes per week, 1.5–2 hours each).