Teaching

For whom? Aimed for all students interested in digital humanities and social sciences from the BA level onwards.
What? Introductory course will give you an overview of theory and practice including showcases of methods and applications.
How? The course is organized each spring both in Finnish and in English:

• KIK-417 Johdatus digitaalisiin ihmistieteisiin (5 ECTS)
• KUKA-501 Introduction to Digital Humanities and Social Sciences (5 ECTS)

For whom? Aimed mainly for MA students and beyond. Bachelor degree is required for completion of the full study module. However, you can start taking individual courses (for example, Elements of Digital Humanities & Computational Literacy) already before.
What? Study block of digital humanities that will give you the rudiments of theory and practice including the basics of relevant methods.
How? Two-part structure:

• The two mandatory introductory courses of the module are taught each autumn:
  • LDA-H304 Elements of Digital Humanities (5 ECTS)
  • LDA-H305 Computational Literacy (5 ECTS) 
• The module closes with LDA-H302 Helsinki Digital Humanities Hackathon (5 ECTS) that takes place each spring in hackathon spirit (2015, 2016, 2017, 2018 & 2019).

For whom? Aimed mainly for MA students and beyond. Bachelor degree is required for completion of the full study module. However, you can start taking individual courses (for example, Elements of Digital Humanities & Computational Literacy) already before.
What? Modular study block of digital humanities that will give you the rudiments of theory and practice including the basics of relevant methods.
How? Three-part structure:

• The two mandatory introductory courses of the module are taught each autumn:
  • LDA-H304 Elements of Digital Humanities (5 ECTS)
  • LDA-H305 Computational Literacy (5 ECTS) 
• After this, you are free to choose 15 ECTS worth of elective courses from the Theory and practice and Methods selections listed below
• The module closes with LDA-H302 Helsinki Digital Humanities Hackathon (5 ECTS) that takes place each spring in hackathon spirit (2015, 2016, 2017, 2018 & 2019).

For whom? A study track ("major") for students in the MA programme Linguistic Diversity and Digital Humanities.
What? Modular study track of digital humanities that will give you the skills of theory and practice including understanding of relevant methods.
How? Multi-part structure:

• The three mandatory introductory courses of the module are taught each autumn:
  • LDA-310 Approaches to linguistic diversity and digital humanities (10 ECTS)
  • LDA-H304 Elements of Digital Humanities (5 ECTS)
  • LDA-H305 Computational Literacy (5 ECTS) 
• After this, you are free to choose 25 ECTS worth of elective courses from the Theory and practice and Methods selections listed below. One of the courses can be subsituted with LDA-H322 Work Practice (5 ECTS).
• LDA-H303 Digital Humanities Project Course I (5 ECTS)
• LDA-H302 Helsinki Digital Humanities Hackathon (5 ECTS) that takes place each spring in hackathon spirit (2015, 2016, 2017, 2018 & 2019).
• LDA-H800 Master's Thesis (30 ECTS) and the supporting LDA-H301 Digital Humanities Seminar (5 ECTS)

NB! The information will be updated also during the year as more suitable courses turn up.

Please note that some of the courses might be available only students of specific study programmes. Please check the requirements of individual courses.

Autumn 2020

• Arkiven, de digitala hjäpmedlen - HISK-234; Strömberg, John
• Paikallishistoriaa verkkoympäristössä - HISK-234/HISM-333/LDA-H320; Lahtinen, Anu
• Käsiteanalyysi ja termityö - SUKU-S330/SSU351/920202/HYMY-907; Pitkänen-Heikkilä, Kaarina
• Museological Theory - KUMA-MU501; Robbins, Nina & Thomas, Suzanne
• Museum Collections - KUMA-MU502; Robbins, Nina
• Identities and Digital Networks of Migrant Communities: Global Indian Diasporas - KUKA-106; Zeiler, Xenia
• History and popular culture - MKK-322/LDA-H307; Grufstedt, Ylva
• Research Seminar: Game Studies and Historical Culture - HKP-911; Fewster, Derek
• The Political in Human-Computer Interaction - COS-D421; Nelimarkka, Matti
• Digital Inequalities and the Ethical Turn - SOSM-KY304; Ruckenstein, Minna & Charitsis, Vasileios
• Data Journalism - MSV-JK302; Zilliacus-Tikkanen, Henrika
• Music Videos: Ontology, Analysis and Outreach - online course; Pääkkölä, Anna-Elena
• Digital Media and Society - PVK-V205; Rydenfelt, Henrik
• Organizational Communication in the Digital Age - GPC-O315; Sauri, Pekka
• Digitalisoituva yhteiskunta ja koulu - EDUDIGI001; Korhonen, Tiina

Spring 2021

• Digitized Newspapers in Humanities Research - LDA-H306; Tolonen, Mikko & Marjanen, Jani
• Sanomalehtien käyttö historiantutkimuksessa - HISK-234; Hänninen, Reetta
• Historiantutkimuksen tulevaisuus - HISM-311/HISM-333/HISM-321/HISM-322/HISM-323/LDA-H319; Lahtinen, Anu
• Specialiseringsstudier I: Game of History - MKK-344/LDA-H308; Fewster, Derek
• Kulturens digitala dimensioner - MKK-331/HISK-234/HHR233; Fewster, Derek
• New Forms of Mediated Participation - GPC-M323; Harju, Anu
• Digital Ethnography: Concepts, Perspectives and Practice - KUMA-KA303/LDA-H317; Cocq, Coppélie
• Social, Political and Intimate Aspects of Social Media - KUKA-106; Ristivojevic, Dusica
• Open digital heritage (online course) - KUMA-KP506; Kovanen, Heidi & Thomas, Suzanne
• Terminologian perusteet - TRA-503; Pasanen, Päivi
• Language Typology - LDA-L307; Sinnemäki, Kaius & Di Garbo, Francesca
• Mediatization and Digital Culture in Asia: Theories, Methods and Case Studies (online course) - ALKU-303/ICE-CM322; Zeiler, Xenia
• Perspectives to Digitalizing Society - COS-SD302; Lagus, Krista
• Digital Media and Society - PVK-V205; Polynczuk, Kinga
• Media, Activism and Social Change - GPC-M322; Horowitz, Minna
• Digitaalisen yhteiskunnan rajapinnoilla (verkkokurssi) - SOSM-321; Laaksonen, Salla
• Datafication - critical perspectives - SOSM-326; Ruckenstein, Minna
• Analytical approaches to human environmental interaction - ECGS-081; Janasik, Nina
• Imagination in environmental politics - ECGS-085; Janasik, Nina
• The politics of environmental knowledge - ECGS-087; Janasik, Nina & Karhunmaa, Kamilla
• Information society policy and governance - GPC-O314; Jääsaari, Johanna & Hilden, Jockum
• Human-Computer Interaction and Social Psychology - SOSM-SP330; Lampinen, Airi
• Teknologiausko ja ilmastonmuutos - TUM-3244/LDA-H307; Valaskivi, Katja
• Digitalisaatio sosiaali- ja terveysalalla - SOTE-324
• Music Videos: Ontology, Analysis and Outreach - online course; Pääkkölä, Anna-Elena

These are possible optional courses, also other courses can be offered or chosen. If you know a suitable course for digital humanities, please contact mikko.tolonen@helsinki.fi.

NB! The information will be updated also during the year as more suitable courses turn up.

Please note that some of the courses (especially Computer science/Mathematics) may have prerequisites on e.g. programming skills.

Autumn 2020

Courses suitable for beginners in digital methods:

• Data clinic - LDA-T309; Lennes, Mietta
• Introduction to Language Technology - KIK-405; Öhman, Emily & Scherrer, Yves
• Ohjelmointia lingvisteille - KIK-LG208; Creutz, Mathias
• Qualitative Methods in Practice: Data Production and Analysis - ALKU-302; Mietola, Reetta & Mölkänen, Jenni
• Sosiaalitutkimuksen tilastolliset menetelmätaidot 1 - SOSM-401A; Laihiala, Tuomo
• Sosiaalitutkimuksen tilastolliset menetelmätaidot 2 - SOSM-401B; Laihiala, Tuomo
• Introduktion till statistik - KSV-402; Vincze, Laszlo
• Tilastotiede ja R tutuksi I - MAT12001 (MOOC); Piiroinen, Petteri
• R-basic course - FOR-006
• Ohjelmoinnin MOOC
• Ohjelmoinnin perusteet - TKT10002; Kaila, Erkki
• Tiedon haku ja hallinta – Tietoverkot historiantutkimuksen apuna - verkkokurssi; Onnela, Tapio
• Introduction to open data science - PHD-302; Vehkalahti, Kimmo (MOOC + contact teaching)

• Elements of AI (MOOC, open all year)

Other courses:

• Command line tools for linguists - KIK-LG219; Celikkanat, Hande
• Computational morphology - LDA-T302; Creutz, Mathias
• Models and Algorithms in NLP-applications - LDA-T305; Scherrer, Yves
• A practical introduction to neural machine translation - LDA-T306; Raganato, Alessandro & Scherrer, Yves & Tiedemann, Jörg
• Introduction to NLP - LDA-T501; Yangarber, Roman
• Laboratory course in phonetics - LDA-P304; Vainio, Martti & Simko, Juraj
• Experimental laboratory course - LDA-EXP315; Lappi, Otto & Simko, Juraj
• Tietokoneavusteinen kääntäminen (CAT) - TRA-B333
• Johdatus yhteiskuntatilastotieteeseen, osa 1 - VALT-103; Vehkalahti, Kimmo (MOOC + lähiopetus)
• Johdatus yhteiskuntatilastotieteeseen, osa 2 - VALT-104; Vehkalahti, Kimmo (MOOC + lähiopetus)
• Scientific Modeling and Model Validation - COS-D407; Bohk-Ewald, Christina
• Data Wrangling and Analysis - COS-D411; Valaste, Maria
• Register Based Research and Data Analysis - COS-D413; Valaste, Maria & Lagus, Krista
• Quantitative Research Skills - COS-R405; Konttinen, Hanna
• Quantitative Research Practicum - COS-R406; Konttinen, Hanna
• Bridging Quantitative and Qualitative Research - COS-R407; Lagus, Krista
• Tilastotiede ja R tutuksi II - MAT12002; Piiroinen, Petteri
• Data-analyysin projekti - MAT12005; Piiroinen, Petteri
• Todennäköisyyslaskenta IIa - MAT22001; Koskenoja, Mika
• Yleistetyt lineaariset mallit I - MAT22006; Möttönen, Jyrki
• Computational statistics I - MAST32001; Honkela, Antti
• Robust Regression - MAST33004; Möttönen, Jyrki
• Geoinformatiikka 1 - ME-203; Holopainen, Markus & Luoma, Ville
• Topics in biostatistics - LSI34001; Kulathinal, Sangita
• Bayesian inference in biosciences - LSI35002
• Clinical data mining - LSI36001
• Shell Scripting - CSM13501 (MOOC); Varjonen, Samu
• Ohjelmointihaasteita I - TKT21024 (MOOC, open all year); Laaksonen, Antti
• Functional Programming I - TKT21029 (MOOC, open all year); Laaksonen, Antti
• Ohjelmoinnin jatkokurssi - TKT10003; Kaila, Erkki
• Introduction to Data Science - DATA11001; Roos, Teemu & Dönges, Saska & Bouri, Ioanna
• Introduction to Machine Learning - DATA11002; Puolamäki, Kai
• Distributed Data Infrastructures - DATA11003; Kangasharju, Jussi
• Big Data Platforms - DATA14003; Heljanko, Keijo
• Introduction to Artificial Intelligence - DATA15001; Roos, Teemu & Leinonen, Matti & Savela, Jarkko
• Computational Creativity - DATA15002; Toivonen, Hannu & Linkola, Simo
• Educational Data Mining - DATA20007; Leinonen, Juho & Ihantola, Petri
• Computer Vision - DATA20016; Ruotsalainen, Laura
• Trustworthy Machine Learning - DATA20019; Honkela, Antti & Zliobaite, Indre
• Advanced Course in Deep Learning - DATA20027; Koskela, Antti
• Design and Analysis of Algorithms - CSM12101; Kärkkäinen, Juha & Khan, Shahbaz & Alanko, Jarno & Cáceres Reyes, Manuel
• String Processing Algorithms - CSM12102; Puglisi, Simon
• Data analysis with Python (MOOC); Dönges, Saska & Oikarinen, Emilia
• Building AI (MOOC, open all year)

Spring 2021

Courses suitable for beginners in digital methods:

• Digital Humanities Project Course I - LDA-H303; Tolonen, Mikko
• Citizen Science: Crowd-sourcing as a tool for collecting quantitative and qualitative data - LDA-H309; Thomas, Suzanne & Öhman, Emily
• Method Course in Digital Humanities I: Linked Data for Digital Humanities - LDA-H311; Hyvönen, Eero & Tuominen, Jouni & Koho, Mikko (basic Python programming skills needed, links to be self-learning material will be provided)
• Method Course in Digital Humanities II: Introduction to NLP for Digital Humanities - LDA-H312; Janicki, Maciej
• Method Course in Digital Humanities III: Spatial Analysis and Data Exploration in History and Archaeology - LDA-H313; Oksanen, Eljas
• Tutorial for Digital Methods in Humanities - LDA-H319; Tolonen, Mikko & Kanner, Antti
• Statistics Literacy for Humanists - LDA-H502; Mäkelä, Eetu & Janicki, Maciej
• Basic Data Visualization - LDA-M504; Palomäki, Jussi
• Corpus Linguistics and Statistical Methods (online course) - KIK-404; Lennes, Mietta
• Tilastomenetelmiä lingvisteille - KIK-LG207; Sinnemäki, Kaius
• Working with Text in Python - ENG-Ling352; Hiippala, Tuomo
• Sosiaalitutkimuksen tilastolliset menetelmätaidot 1 - SOSM-401A; Silfver-Kuhalampi, Mia
• Sosiaalitutkimuksen tilastolliset menetelmätaidot 2 - SOSM-401B;  Silfver-Kuhalampi, Mia
• Kvantitatiiviset menetelmät - YK-264; Eloranta, Jari
• Ohjelmoinnin perusteet - TKT10002; Kaila, Erkki
• Tiedon haku ja hallinta – Tietoverkot historiantutkimuksen apuna - verkkokurssi; Onnela, Tapio

Other courses:

• Luonnollisen kielen käsittelyn sovellusten rakentaminen - KIK-LG211; Creutz, Mathias
• Mathematics for Linguistics - KIK-LG209; Scherrer, Yves
• Koneoppimisen perusteet lingvisteille - KIK-LG210; Creutz, Mathias
• Scientific Programming - LDA-C303; Pekkanen, Jami & Tuhkanen, Samuel
• Computational syntax - LDA-T303; Scherrer, Yves & Sulubacak, Umut
• Computational Semantics - LDA-T304; Creutz, Mathias
• Approaches to Natural Language Understanding - LDA-T307; Raganato, Alessandro
• Introduction to deep learning - LDA-T308; Yangarber, Roman & Hande Celikkanat, Hande
• Kääntämisen ja tulkkauksen tietotekniikka - TRA-B331; Eskelinen, Juha
• Speech analysis methods - LDA-P302; Lennes, Mietta
• Natural Language Processing for Linguists - ENG-Ling353; Hiippala, Tuomo
• Musiikkisignaalin spektrianalyysi - TTK-MU243; Lassfolk, Kai
• Survey Methodology - COS-D415; Valaste, Maria
• Survey Sampling - COS-D416; Valaste, Maria
• Exploratory Methods of Multivariate Data Analysis - COS-D418; Vehkalahti, Kimmo
• Syventävät kvantitatiiviset menetelmät - YMT-3505; Saaritsa, Sakari & Heino, Saska
• Digital urban - USP-363; Jama, Teemu, Joutsiniemi, Anssi
• Data-analyysin projekti - MAT12005; Piiroinen, Petteri
• Advanced Bayesian inference - MAST32004; Vanhatalo, Jarno
• Nonparametric and robust methods - MAST33003; Möttönen, Jyrki
• Tilastollinen päättely I - MAT12004; Piiroinen, Petteri
• Bayes-päättely - MAT22005; Piiroinen, Petteri
• Inverse Problems 1: convolution and deconvolution - MAST31401; Siltanen, Samuli & Virtanen, Heli & Murthy, Rashmi & Ratti, Luca
• Spatial Modelling and Bayesian Inference (online course) - MAST32005; Vanhatalo, Jarno
• Time series analysis II - MAST32008; Kalliovirta, Leena
• Open GIS and spatial data infrastructures - GEOG-323
• Applied spatial statistics and urban modelling - GEOG-325; Votsis, Athanasios
• Introduction to advanced geoinformatics - GEOG-G301; Toivonen, Tuuli
• Survey methods in marketing and social sciences - FOR-103; Berghäll, Sami & Vainio, Annukka
• Tilastollisia malleja: Yleistettyjen lineaaristen mallien perusteet - FOR-007; Mattsson, Teppo
• Geoinformatiikka 2 - ME-230; Luoma, Ville & Holopainen, Markus
• Statistical epidemiology - LSI34005; Pirinen, Matti
• Monimuuttujamenetelmät - PSYM-501; Lipsanen, Jari
• Statistical Inverse Methods - PAP303; Penttilä, Antti
• Ohjelmoinnin jatkokurssi - TKT10003; Kaila, Erkki
• Data Science Project I - DATA11004; Puolamäki, Kai & Mathioudakis, Michail
• Advanced Course in Machine Learning - DATA12001; Tatti, Nikolaj
• Probabilistic Graphical Models - DATA12002; Hyttinen, Antti
• Interactive Data Visualization - DATA15003
• Network Analysis - DATA16001; Hui, Pan & Mathioudakis, Michail
• Natural Language Processing - DATA20015; Granroth-Wilding, Mark
• Information Retrieval - DATA20021; Glowacka, Dorota & Puglisi, Simon
• Bayesian Machine Learning - DATA20023; Klami, Arto
• Computational Cognitive Neuroscience - DATA20024; Hyvärinen, Aapo
• Numerical Methods of Scientific Computing - MATR322; Kuronen, Antti & Lehtola, Susi
• Data analysis with Python (MOOC); Dönges, Saska & Oikarinen, Emilia

Possible method courses offered at Aalto University (Aalto CS) include (some of the courses are part of the network university FITech):

Autumn 2020

Courses suitable for beginners in digital methods:

• Introduction to Digital Transformation
• Social Media Analytics
• Johdatus ohjelmointiin (online course, open all year)
• Ohjelmoinnin peruskurssi Y1
• Ohjelmointi 1
• Object oriented programming with C++

Other courses:

• Data ja tieto (online course, open all year)
• Machine Learning with Python
• Deep Learning with Python
• Methods of Data Mining
• Machine Learning: Supervised Methods
• Bayesian Data Analysis
• Complex Networks
• Explorative Information Visualization
• Computer Vision
• Speech Processing
• Speech Recognition

Spring 2021

• Semantic Web
• Machine Learning
• Machine Learning: Advanced Probabilistic Methods
• Deep Learning
• Kernel Methods in Machine Learning
• Introduction to Artificial Intelligence
• Artificial Intelligence
• Information Visualization
• Hands-on Network Analysis

These are possible courses, also other courses can be offered and chosen. If you know a suitable course for digital humanities, please contact: mikko.tolonen@helsinki.fi.

Those with advanced skills in methods may get credit by proving your skills (i.e. free credit for methods) when you commit to the LDA-H302 Helsinki Digital Humanities Hackathon.