STK-IN1050 – Statistics for computer scientists
Course content
The course builds a solid understanding of fundamental concepts in probability theory, statistics, and prediction by creating an intuitive connection between these theoretical concepts and variables and calculations in programming code. It establishes a concrete understanding of concepts such as probability distributions, estimators, hypothesis testing, and regression. The course aims to develop fundamental conceptual understanding and generic solution strategies, enabling students to understand and find approximate solutions to a wide range of problems through techniques such as simulation.
Learning outcome
After completing the course, you will:
- Be familiar with the basic concepts of random variables, outcomes, events, probabilities, and probability distributions.
- Be able to explore datasets through plotting and calculating simple descriptive statistics such as mean, standard deviation, and quantiles.
- Understand that many random processes result in probability distributions that can be described with simple mathematical expressions (parametric distributions). Be familiar with the properties of the binomial distribution and the normal distribution, and know that there are many similar examples of parametric distributions of broad utility.
- Be familiar with the basic concepts of statistical hypothesis testing, such as null hypothesis, null model, test statistic, null distribution, and threshold values, and be able to relate these concepts to code that calculates a p-value through simulation from a null model.
- Understand the concept of regression and its common formulations, such as linear regression, polynomial regression, and logistic regression. Be able to estimate the coefficients of a regression model and assess how well the model fits the data.
- Know basic concepts related to prediction and evaluation of prediction models, such as cross-validation.
- Be able to connect statistical concepts to code that simulates random processes and through simulation approximate probabilities, expected values, standard deviations, and similar measures for a described problem of varied nature.
- Master fundamental constructs and libraries in Python for practical data analysis.
Admission to the course
Students at UiO register for courses and exams in Studentweb.
Recommended previous knowledge
MAT1080 – Mathematical foundation of machine learning and IN1000 – Introduction to Object-oriented Programming
Teaching
4 hours of lectures and 2 hours of problem sessions in groups per week throughout the semester.
The number of groups offered can be adjusted during the semester, depending on attendance.
Examination
Midterm exam which counts 1/4 towards the final grade.
Final written exam which counts 3/4 towards the final grade.?
This course has?4 mandatory assignments, which all must be approved before you can sit the final exam.
Examination support material
Approved calculators and formula sheet for STK-IN1050 are allowed.
Information about approved calculators in Norwegian.
Language of examination
The examination text is given in Norwegian. You may submit your response in Norwegian, Swedish, Danish or English.
Grading scale
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Resit an examination
This course offers both postponed and resit of examination. Read more:
More about examinations at UiO
- Use of sources and citations
- How to use AI as a student
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.