Foundations of Data Science

Introduksjon

This course covers the fundamentals of machine learning and data science. Using Python and AI coding assistants, students will learn to explore data, build models, and evaluate results. The focus is on understanding methods, interpreting outputs, and exercising judgment, while solving real problems with data.

Kursets innhold

The course covers the following topics:

• Framing business problems as data analysis or prediction tasks.
• Data sources and data collection considerations.
• Exploration of data: distributions, relationships, anomalies.
• Visualization and presentation of data analyses.
• Classification methods: use cases, evaluation metrics, and costs of errors.
• Linear regression: assumptions, interpretation, and limitations.
• Regression coefficients, p-values, and practical significance.
• Train/test splitting, overfitting, and data leakage.
• Preprocessing and feature engineering.
• Regularization and non-linear modeling approaches.
• Model comparison and cross-validation.

Forbehold

Dette er et utdrag fra den komplette kursbeskrivelsen for kurset. Dersom du er aktiv student på BI, kan du finne de komplette kursbeskrivelsene med informasjon om bl.a. læringsmål, læreprosess, pensum og eksamen på portal.bi.no. Vi tar forbehold om endringer i denne beskrivelsen.