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Opleiding: Data Science Fundamentals in R

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Data Science Fundamentals in R
6 uur
Fundamental Methods for Data Science in R
start the course
distinguish data science from statistics and computer science
identify some of the problems data scientists solve
use various sources of data to learn data science
use data frames to store data in tables in R
use the R str function to display the internal structure of data
use summary statistics to catch problems before data analysis in R
use the rjson R package to import json formatted files
use the foreach loop in R
reshape values in your data in R
join data frames using the merge function in R
use the transpose function "t" in R
aggregate data frames in R
perform a fixed value imputation and perform a list wise deletion imputation in R
perform an imputation using the impute functions from the Hmisc package in R
use the R cut function to turn continuous data into discrete categories
identify the most frequently used functions for data analysis in R
fit a linear model using lm function in R
computing ANOVA using the aov function in R
extract coefficients from a modeling function in R
extract the fitted values from a modeling function in R
extract the residuals from a modeling function in R
calculate the variance-covariance matrix in R
calculate a confidence interval in R using confint
fit a generalized linear model using the glm function in R
use the ggplot2 library to plot models in R
compute the t-test in R
perform a TukeyHSD test in R
use the predict function in R
create a time series in R
use the forecast package in R
use common statistical methods for data analysis in R
Machine Learning Examples for Data Science in R
start the course
distinguish between supervised and unsupervised learning
perform classical multidimensional scaling using cmdscale in R
perform hierarchical cluster analysis in R
use the corclust function in the klaR package in R
perform k-means clustering on data in R
use the kselection package to select k for a k-means clustering in R
use the clusplot function to perform a cluster plot on a clara object in R
perform a fully C-Means clustering from the e1071 package in R
create a basic classification tree using rpart in R
create a basic regression tree using rpart in R
create a basic classification tree with the trees package in R
create a basic regression tree with the trees package in R
perform a K-Nearest Neighbor classification in R
use the randomforest package for classification in R
combine random forest ensembles into a single object in R
use random forests for unsupervised classification in R
use the clusplot function to perform a cluster plot on a pam cluster in R
build a nave bayes classifier using the klaR package in R
use the lda function in R
use the qda function from the MASS package in R
perform a MDS using the mda package in R
use the SVM function from the e1071 library in R
perform a curve fit using the LOESS method in R
perform a PLS regression using the pls package in R
plot a smoothing spline from the splines packages in R
use the boosting function from the adabag package in R
use the bagging function from the adabag package in R
create a scatterplot matrix using the caret package in R
create an overlayed density plot using the caret package in R
create a 3D Scatterplot in R
provide a basic understanding of how to use common statistical methods for data analysis in R

Toelatingseisen: wat heb je nodig?

Er is geen specifieke voorkennis vereist.

Duur van de cursus

6 uur

Bijzonderheden

Award Winning E-learning

Plaatsen / leslocaties

Heel Nederland, E-learning, Online

Algemene informatie over de cursus

Bestel deze unieke E-learning cursus Data Science Fundamentals in R online, 1 jaar 24/ 7 toegang tot rijke interactieve video’s, spraak, voortgangsbewaking door rapportages en testen per hoofdstuk om de kennis direct te toetsen.

Duur: 6 uur
Taal: Engels
Certificaat van deelname: Ja
Online toegang: 365 dagen
Voortgangsbewaking: Ja
Award Winning E-learning: Ja
Geschikt voor mobiel: Ja

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