| 1 | Philosophy of Open Source Software and Its Advantages in the Healthcare Domain | [1], s. 10-30 |
| 2 | Basics of Python Programming: Syntax, Data Structures, and Control Flow | [1], s. 30-45 |
| 3 | Data Manipulation and Analysis in Python Using the NumPy Library | [2], s. 20-80 |
| 4 | Working with DataFrames in Python Using the Pandas Library | [2], s. 81-148 |
| 5 | Basic Data Visualization in Python: Matplotlib and Seaborn Libraries | [3], s. 30-180 |
| 6 | Fundamentals of R Programming: Syntax, Data Structures, and Functions | [1], s. 118-205 |
| 7 | Data Manipulation and Analysis in R Using Core Packages (dplyr, tidyr, etc.) | [1], s. 150-190 |
| 8 | MIDTERM EXAM | |
| 9 | Basic Data Visualization in R Using the ggplot2 Library | [1], s. 206-217 |
| 10 | Importing and Cleaning Health Datasets in Python and R Environments | [3], s. 190-220 |
| 11 | Basic Statistical Analyses and Hypothesis Testing (Python Statsmodels, R stats package) | [1], s. 214-234 |
| 12 | Introduction to Machine Learning in Python: The Scikit-learn Library | [1] s. 50-85; [2] s 25-37 |
| 13 | Introduction to Machine Learning Packages in R (tidymodels, caret, etc.) | [1], s. 225-260 |
| 14 | Python/R Applications on Real-World Health Data Scenarios | [2], s. 30-80 |
| 15 | Coding Practices, Debugging, and Reproducible Reporting | [3], s. 17-76 |
| 16 | FINAL EXAM | |