Excerpt from course description

Data Analysis with Programming


This course introduces the basics of statistics and machine learning in the context of Python. It covers:

  • Inferential statistics, such as the bootstrap, p-values, and confidence intervals.
  • Methods for constructing and evaluating statistical estimators.
  • The fundamentals of the two most important regression models: Linear regression and logistic regression.

Additionally, the course introduces students to the Python packages NumPy, SciPy, and statsmodels

Course content

The course covers the following topics:

  • NumPy and SciPy.
  • Statistical simulation in Python.
  • Exploratory data analysis.
  • Statistical models and the bootstrap.
  • Unbiased estimators and the efficiency of estimators.
  • Construction of estimators.
  • Confidence intervals.
  • Hypothesis tests and p-values.
  • The t-test.
  • Foundations of machine learning.
  • Linear regression.
  • Inference for linear regression.
  • Linear regression using categorical covariates (ANOVA).
  • Binary regression, such as logistic regression.


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.