Data Science and Machine Learning

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Ss. Cyril and Methodius University in Skopje

Deepening knowledge in the field of Data Science and Machine Learning. This course provides practical usage of advanced tools and software for data processing, development of machine learning models, evaluation, and result comparison.

Chapter 1: Study of Data Science and Data Analysis with Machine Learning Algorithms

Explore the fundamentals of data science and analysis in Chapter 1. Understand the key concepts shaping these fields and the role of machine learning algorithms in extracting insights from large datasets. Gain a foundational understanding, laying the groundwork for practical applications.

Chapter 2: Working with Datasets and Application of Machine Learning Algorithms

Immerse yourself in the practical side of data science in Chapter 2. Learn to handle datasets, prepare them, and apply machine learning algorithms, including Decision Trees, Naive Bayes, SVM, Random Forests, Ensemble models, and XGBoost. Acquire hands-on skills for deeper exploration.

Chapter 3: Building Classification and Regression Models, Clustering, and Visualization

Move beyond basics in Chapter 3. Construct classification and regression models, explore clustering techniques, and understand data and model visualization. Set the stage for analyzing and comparing different model evaluation techniques with practical proficiency.

Chapter 4: Implementation and Evaluation Using Java or Python Environment

In the final chapter, dive into practical implementation and evaluation of algorithms and models, whether using Java or Python. Equip yourself with skills to bring learning to life and assess the performance and effectiveness of developed models in real-world scenarios. Gain hands-on experience in implementing machine learning algorithms.

As the basic course introducing g concepts of data science and machine learning, this course is coupled with the course Numerical Methods in Stochastic Processes, whose concepts are used here. Additionally, this course is further developed in the course Ambient Intelligence and Smart Devices, where the introduced concepts are being studied in detail for a specialized application area. Data Warehouse and Purpose-built Data is also tightly connected to this course.

Lectures, independent work on project tasks and preparation of seminar work.
Each student must complete a mandatory project assignment. The final grade is determined based on the total points from the project assignment and the final exam.

Regular attendance on lectures and consultations.