Ohjelmoillinen lukutaito

Absolutely no prerequisites regarding computational approaches. At least a BA-level understanding of any discipline otherwise.

This is an introductory course on applying modern data processing to complex social and historical data. As a signposting course, the course describes the landscape of computational human sciences. The main learning goals of the course are that after completing it, a student will be able to:

1. make informed decisions on which computational approaches will be of use to themself, and
2. understand, follow and discuss the development of computational approaches within their field in general

• The student understands the multiple ways in which computational approaches benefit work within the human sciences.
• They are able to use ready tools to work with data.
• In addition, they have attained knowledge of the fundamental concepts of programming, through which they can start to expand their capabilities, should they so choose.
• The student also gains a basic understanding of the central fundamental concepts of statistics, which both 1) act as a general framework with regard to which many statistical approaches encountered later can be positioned, and 2) act as a practical foundation from which to pursue further understanding.
• Further, the student gains a general literacy on advanced statistical and computer science methods applicable to computational human sciences, and when to apply them (as well as crucially, when and how not to apply them).
• They also learn how open, reproducible research and publishing is done in practice.
• Finally, the student learns to apply all of the above in practice in a small concrete computational human sciences project. 

Online learning resource at https://jiemakel.gitbook.io/cl4hss/(Avautuu uuteen välilehteen).

Completion methods

Can be completed either through participation in group teaching or through independent study. See the course home page for details on both methods of completion.

Assessment practices and criteria

Assessed through a graded final project. More detailed grading criteria for the project are available on the course home page.

Activities and methods in support of learning

For an up-to-date description of the content of group teaching, see each course instance.

Target groups

Teaching period when the course will be offered

Second period

Recommended time or stage of studies for completion

Before methods and applied courses in the DH modules (suggested for the first year for DH MA students, otherwise free timing)

Study module

LDA-H300 Digital Humanities MA track

LDA-HA500 Digital Humanities, 15 cr Module

LDA-HB500 Digital Humanities, 30 cr Module

Expiry of studies

-

Language of instruction

English