| Week | Lecture | Topic | Lecturer | Assignment | Seminar | Assignment due |
|---|---|---|---|---|---|---|
| 35 | 27.08 08:15 - 12:00 (Aud M) | Introduction to R | HO | Assignment 1 | 29.08 08:15 - 10:00 (LAB2) | 03.09.2024 (07:00) |
| 36 | 03.09 08:15 - 12:00 (Aud M) | Tidy data | OPMH | Assignment 2 | 05.09 08:15 - 10:00 (AUD G) | 10.09.2024 (07:00) |
| 37 | 10.09 08:15 - 12:00 (Aud M) | Plotting | HO | Assignment 3 | 12.09 08:15 - 10:00 (AUD N) | 17.09.2024 (07:00) |
| 38 | 17.09 08:15 - 12:00 (Aud M) | Functions and loops | OPMH | Assignment 4 | 19.09 08:15 - 10:00 (LAB2) | 24.09.2024 (07:00) |
| 39 | 24.09 08:15 - 12:00 (Aud M) | Project organization | HO | 26.09 08:15 - 10:00 (LAB2) |
Welcome!
This is the companion website for the course BAN400 - R Programming for Data Science, given at The Norwegian School of Economics (NHH). The purpose of this website is to provide study material such as lecture videos, exercises and assignments for students taking the course.
BAN400 has previously consisted of two separate modules; one intensive one-week introduction to R that could be taken separately as a 2.5 ECTS course as BAN420, as well as the main course itself (pun intended), which, together with BAN420, completed the 7.5 ECTS unit BAN400.
From the fall semester of 2023, we offer BAN400 as one regular course. We will, however, still make an explicit transition from Part 1, where we introduce basic programming, to Part 2 where we will learn a number of useful techniques that are particularly useful when working with data.
All announcements and course administration such as homework delivery and feedback will be carried out through the course page at Canvas, which is the learning management system used by NHH. You will need to sign up to the course in order to gain access to the Canvas page.
Schedule
Part 1: The basics of R
Part 2: Specialized topics
| Week | Lecture | Topic | Lecturer | Assignment | Seminar | Assignment due |
|---|---|---|---|---|---|---|
| 40 | 01.10 08:15 - 12:00 (Aud M) | Git and Github | HO | Git | 03.10 08:15 - 10:00 (LAB2) | 08.10.2024 (07:00) |
| 41 | 08.10 08:15 - 12:00 (Aud M) | Machine learning | HO | Machine learning | 10.10 08:15 - 10:00 (LAB2) | 15.10.2024 (07:00) |
| 42 | 15.10 08:15 - 12:00 (Aud M) | Iterations | OPMH | Iterations | 17.10 08:15 - 10:00 (AUD G) | 22.10.2024 (07:00) |
| 43 | 22.10 08:15 - 12:00 (Aud M) | Parallel computing | OPMH | Parallel computing | 24.10 08:15 - 10:00 (LAB2) | 29.10.2024 (07:00) |
| 44 | 29.10 08:15 - 12:00 (Aud M) | Many models and Making maps | HO | Many Models/Making maps | 31.10 08:15 - 10:00 (LAB2) | 12.11.2024 (07:00) |
| 45 | 12.11 08:15 - 12:00 (Aud M) | Documentation and deployment | OPMH | 05.11 08:15 - 10:00 (LAB2) |
NB! Please read this guide on submitting assignments through Github Classroom before clicking on the assignment links!
Practical information
- The Compendium (This web page)
- This page contains the study material needed for the course as well as links to other sources when needed. Most of the modules contain video lectures as well as comments, links and sometimes small exercises.
- Text book
- We will link to R for Data Science (R4DS) by Hadley Wickham many times. This is a great reference to bookmark once and for all.
- Lectures
- Lectures are held on Tuesdays from 08:15 to 12:00. The lectures will have the same core content as this web page, but some lectures will contain additional discussions and coding workshops.
- Assignments and course approval
- Most lectures have an accompanying assignment. See the overview above for a detailed overview and deadlines.
- The assignments from the first part of the course can be found here on this web page, and they must be handed in via Canvas. You must also complete two peer reviews for each of these assignments for approval.
- The remaining assignments will be administered via Github, please stand by for links and instructions for that.
- To obtain course approval (and hence to be eligible to take the exam), you must hand in:
- All four assignments from the first part of the course, and
- minimum four of the remaining six assignments.
- Please make careful note of the deadlines.
- Teaching assistants and seminar
- The teaching assistants will give feedback on your written work.
- They will also run a weekly seminar, each X at XX:15-XX:00, mostly in X (but with some exceptions). In the seminar the TAs will discuss the assignment from the previous week, and provide support for next week’s assignment.