<p>This DataCamp course has been developed for the use of University of Helsinki by <b>Tuomo Nieminen</b> and <b>Emma Kämäräinen</b>, under the supervision of adj. prof. <b>Kimmo Vehkalahti</b>. The corresponding HY course is titled Introduction to Open Data Science (IODS). The core themes of the course are open data, reproduciple research and data science.</p><p><a href = 'https://tuomonieminen.github.io/Helsinki-Open-Data-Science/#/'>IODS course slides</a><p>
Regression and model validation
Data wrangling, simple regression, multiple regression, regression diagnostics
Regression for binary outcomes, training and testing a (predictive) model, cross-validation
Clustering and classification
Datasets in R, Linear Discriminant Analysis (LDA) and K-means clustering
Dimensionality reduction techniques
Principal component analysis (PCA), Correspondence analysis (CA)
Analysis of longitudinal data
Graphical Displays and Summary Measure Approach, Linear Mixed Effects Models for Normal Response Variables
Helsinki University student who studies statistics and computer science. Believes in the power of data and finds herself interested in statistics more and more every day. Currently works as a Data Scientist at DNA Oy.
Studies statistics and computer science at the University of Helsinki and works at the National Health and Welfare Institute (THL).
Believes that data analysis can make the world a better place.
(Super) Social Data Scientist, D.Soc.Sci, Fellow of the Teachers' Academy of Uni HELsinki, running #tilastoMOOC - the 1st Social Statistics MOOC & #IODS, the 1st Open Data Science MOOC in Finland, powered by DataCamp, VuoLearning, and Moodlerooms
Statistics student at the University of Helsinki. Teaching assistant on the statistics courses Helsinki Social Statistics and Introduction to Open Data Science. Part-time R-dude at the Finnish National Institute for Health and Welfare. Passtime hobbies include powerlifting, astronomy.