Week | Module | Monday 14:15 - 16:00 | Thursday 14:15 - 16:00 |
---|---|---|---|
2 | Module 1 | 06.01: Introduction to TECH3/Overview lecture in Aud D | 09.01: Collaborative learning session in Aud J |
3 | Module 1 | 13.01: Practical session in Aud C. | 16.01: Case session in Aud J |
4 | Module 2 | 20.01: Overview lecture in Aud C | 23.01: Collaborative learning session in Aud J |
5 | Module 2 | 27.01: Overview lecture in Aud C | 30.01: Exercise session in Aud J |
6 | Module 2 | 03.02: No lecture | 06.02: Case session in Aud J |
7 | Module 3 | 10.02: Overview lecture in Aud C | 13.02: Collaborative learning session in Aud J |
8 | Module 3 | 17.02: Overview lecture in Aud C | 20.02: No lecture. |
9 | Module 3 | 24.02: Practical session in Aud C. | 27.02: Case session in Aud J |
10 | Module 4 | 03:03: Overview lecture in Aud C | 06.03: Oracle session in Aud J |
11 | Module 4 | 10:03: Overview lecture in Aud C | 13.03: Collaborative learning session in Aud J. |
12 | Module 4 | 17.03: Practical session i Aud C | 20.03: No lecture (Symposium) |
13 | Module 5 | 24.03: Overview lecture in Aud C | 26.03: Case session in Aud J |
14 | Module 5 | 31.03: Overview lecture in Aud C | 03.04: Collaborative learning session in Aud J |
15 | Module 5 | 07.04: Practical session in Aud C | 10.04: Case session in Aud J |
16 | Exam preparations | 14.04: No lecture (Easter) | 17.04: No lecture (Easter) |
17 | Exam preparations | 21.04: No lecture (Easter) | 24.04: Exam prepartion session? in Aud J |
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, 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.