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Course Description
This DataCamp course has been developed for the use of University of Helsinki by Tuomo Nieminen and Emma Kämäräinen, under the supervision of adj. prof. Kimmo Vehkalahti. 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.
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Regression and model validation
FreeData wrangling, simple regression, multiple regression, regression diagnostics
Reading data from the web100 xpScaling variables100 xpCombining variables100 xpSelecting columns100 xpModifying column names100 xpExcluding observations100 xpVisualizations with ggplot2100 xpExploring a data frame100 xpSimple regression100 xpVideo: Linear regression50 xpMultiple regression100 xpGraphical model validation100 xpVideo: Model validation50 xpMaking predictions100 xp - 2
Logistic regression
FreeRegression for binary outcomes, training and testing a (predictive) model, cross-validation
More datasets100 xpJoining 2 datasets100 xpThe if-else structure100 xpMutations100 xpSo many plots100 xpThe pipe: summarising by group100 xpBox plots by groups100 xpVideo: Logistic regression50 xpLearning a logistic regression model100 xpVideo: Odds ratios50 xpFrom coefficients to odds ratios100 xpBinary predictions (1)100 xpBinary predictions (2)100 xpAccuracy and loss functions100 xpVideo: Cross-validation50 xpCross-validation100 xp - 3
Clustering and classification
FreeDatasets in R, Linear Discriminant Analysis (LDA) and K-means clustering
Datasets inside R100 xpCorrelations plot100 xpScale the whole dataset100 xpCreating a factor variable100 xpDivide and conquer: train and test sets100 xpVideo: Linear Discriminant Analysis50 xpLinear Discriminant analysis100 xpPredict LDA100 xpVideo: Distance measures and clustering50 xpTowards clustering: distance measures100 xpK-means clustering100 xpK-means: determine the k100 xp - 4
Dimensionality reduction techniques
FreePrincipal component analysis (PCA), Correspondence analysis (CA)
Meet the human data100 xpString manipulation100 xpDealing with not available (NA) values100 xpExcluding observations100 xpExploring the countries100 xpVideo: Dimensionality reduction with PCA50 xpPCA with R100 xpVideo: Biplots50 xpA biplot of PCA100 xpVideo: Multiple Correspondence Analysis50 xpIt's tea time!100 xpMultiple Correspondence Analysis100 xp - 5
Analysis of longitudinal data
FreeGraphical Displays and Summary Measure Approach, Linear Mixed Effects Models for Normal Response Variables
Meet and Repeat: PART I100 xpGraphical displays of longitudinal data: The magical gather()100 xpIndividuals on the plot100 xpThe Golden Standardise100 xpGood things come in Summary graphs100 xpFind the outlaw... Outlier!100 xpT for test and A for Anova100 xpMeet and Repeat: PART II100 xpLinear Mixed Effects Models: Gather 'round100 xpPlot first, ask questions later100 xpHolding on to independence: The Linear model100 xpThe Random Intercept Model100 xpSlippery slopes: Random Intercept and Random Slope Model100 xpTime to interact: Random Intercept and Random Slope Model with interaction100 xp
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Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and moreKimmo Vehkalahti
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(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
Tuomo Nieminen
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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.
Petteri Mäntymaa
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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.
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