Foundations of Data Science

Introduction

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.

Course content

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.

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.