| Week | Module | Tuesday 12:15 -14:00 | Thursday 12:15 - 14:00 |
|---|---|---|---|
| 3 | Module 1 | 13.01: Introduction to TECH3/Overview lecture 1 in Aud C | 15.01: Collaborative learning session 1 in Aud J |
| 4 | Module 1/2 | 20.01: Overview lecture 2 in Aud C. | 22.01: Case session 1 in Aud S |
| 5 | Module 2 | 27.01: Overview lecture 2 in Aud C | 29.01: Collaborative learning session 2 in Aud S |
| 6 | Module 2 | 03.02: Seminar 1 in Aud C | 05.02: Exercise session 2 in Aud S |
| 7 | Module 2/3 | 10.02: Overview lecture 3 in Aud C | 12.02: Case session 2 in Aud S |
| 8 | Module 3 | 17.02: Overview lecture 3 in Aud C | 19.02: Collaborative learning session 3 in Aud S |
| 9 | Module 3 | 24.02: Seminar 2 in Aud C. | 26.02: Case session 3 in Aud J |
| 10 | Module 4 | 03.03: Overview lecture 4 in Aud C | 05.03: Collaborative learning session 4 in Aud S |
| 11 | Module 4 | 10:03: Overview lecture 4 in Aud C | 12.03: Case session 4 in Aud S |
| 12 | Module 5 | 17.03: Overview lecture 5 i Aud C | 19.03: Collaborative learning session 5 in Aud S |
| 13 | Module 5 | 24.03: Overview lecture 5 in Aud C | 26.03: Case session 5 in Aud S |
| 14 | 31.03:No lecture (Easter) | 02.04: No lecture (Easter) | |
| 15 | Module 5 | 07.04: No lecture (oral exams in MAB1) | 09.04: Oracle session in Aud S |
| 16 | Module 5 | 14.04: Seminar 3 in Aud C | 16.04: Practical information about exam in Aud C |
Introduction
Welcome to the website for TECH3 Applied Statistics. We will use this website as a supplement to lectures. The website is ongoing development, so not all subjects will have content yet. Below you will find a detailed (preliminary) lecture plan for the Spring semester of 2026, link to the textbook and curriculum. The course description can be found here.
Lecture plan
Literature
Curriculum
All the material on this website, including chapters 1-10, 12-14, and 17 of Statistical Thinking in the 21st Century and Python Companion to Statistical Thinking in the 21st Century.
Learning outcomes
Upon completing the course, the students can:
Knowledge
- Understand basic statistical theory and corresponding methods, and how to apply this knowledge in practical situations.
Skills
- Explore data using software that can summarize and visualize data.
- Master basic probability theory.
- Make inferences about an entire population based on a sample of individuals from that population using both classical statistical methods and modern resampling techniques.
- Design basic experiments, perform hypothesis testing, and quantify effects.
- Measure relationships between both categorical and continuous variables.
- Fit and evaluate regression models for both inference and prediction.
General Competence
- Identify and solve statistical problems.
- Perform basic data analysis using modern computer tools.
- Perform data-driven decision-making for a sustainable future.