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https://github.com/rbroc/datasci-au-24

Primary repository for the course: Data Science, Prediction, and Forecasting, taught as part of the CogSci masters program at Aarhus University.
https://github.com/rbroc/datasci-au-24

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Primary repository for the course: Data Science, Prediction, and Forecasting, taught as part of the CogSci masters program at Aarhus University.

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# Data Science, Prediction, and Forecasting - Spring 2024



This repository contains all of the code and data related to the module _Data Science, Prediction, and Forecasting_ taken as part of the  [MSc in Cognitive Science](https://masters.au.dk/cognitivescience) at Aarhus University.



This repository is in active development, with new material being pushed on a weekly basis. Slides will be uploaded to Brightspace.



## Technicalities



For the sake of convenience, I advise that everyone uses [UCloud](https://cloud.sdu.dk) for development purposes. You can then fork this repo and pull any changes that are made on a weekly basis.



For those of you who do not wish to use UCloud, you are of course welcome to use your own machine. However, due to time constraints, we will not be providing any technical support if you choose to go this way. 



If you _still_ want to use your own machine, make sure to have _at least_ Python 3.8 installed. Some of the code developed in the classroom will not be backwards compatible with earlier versions of Python.



## Repo structure



This repository has been initialised with the following directory structure:



| Column | Description|

|--------|:-----------|

```classes``` | Instructions for each of the classrooms.

```src``` | A folder for Python scripts developed in class.

```syllabus```| Containing a markdown file with the course syllabus and readings, as well as a file listing additional resources.

```nbs```| Will contain the solutions to assignments and classes.

```data```| Will contain data we will use for some of the exercises.



## Classroom instruction



During classroom instructions, I will present you with some exercises to work on in groups, related to the content of the lecture. This semester, we will emphasize peer programming and interactive coding on UCloud. 

At the end of each class or at the beginning of the next class, we will discuss your solutions to the exercises. Note that unfortunately, due to time limitations, I will not be able to grade individual assignments.



## Class times



Lectures take place on Tuesdays from 14-16; classroom instruction is on Wednesday from 8-10. For security reasons, I'm not going to post the room numbers to Github - you can find this via your [AU Timetable](https://timetable.au.dk).



## Course overview and readings



A detailed breakdown of the course structure and the associated readings can be found in the [syllabus](syllabus/readme.md). Also, be sure to familiarize yourself with the [_studieordning_](https://eddiprod.au.dk/EDDI/webservices/DokOrdningService.cfc?method=visGodkendtOrdning&dokOrdningId=17274&sprog=en) for the course, especially in relation to examination and academic regulations. 



Make sure to read the studieording first if you have any questions relating to the course organisation, exam format, and so forth.



## Contact details



Your lecturers for this course will be [Roberta](https://pure.au.dk/portal/en/persons/roberta-rocca(079b23a2-46f6-4a00-9cd6-9a1339101208)/persons/roberta-rocca(079b23a2-46f6-4a00-9cd6-9a1339101208).html) and Mads Jensen, who is Senior Data Scientist at Norlys, and will take over for 4 weeks to talk about data science in industry settings, causal modeling and time-series modeling.



All communication to you will be sent via Brightspace.