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

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

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.