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DTSTART;VALUE=DATE:20260903
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DTEND;VALUE=DATE:20260904
SUMMARY:Basic statistics in R
CLASS:PUBLIC
DESCRIPTION:This introductory training provides a practical foundation in s
tatistical analysis using R\, tailored for participants with no prior prog
ramming experience. Through a combination of e-learning and interactive on
line sessions\, the course covers essential skills for data handling\, vis
ualization\, and basic statistical testing. Participants will learn to use
R and RStudio to perform common analyses—such as t-tests\, ANOVA\, and
non-parametric alternatives—and to produce clear\, publication-ready fig
ures. The training also introduces reproducible workflows\, preparing part
icipants for more advanced data analysis and modelling applications.Target
audienceThis course is designed for PhD students\, postdoctoral researche
rs\, scientists\, technical staff\, and industry professionals who are new
to R and need a practical introduction to statistical analysis. It is par
ticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
X-ALT-DESC;FMTTYPE=text/html:This introductor
y training provides a practical foundation in statistical analysis using R
\, tailored for participants with no prior programming experience. Through
a combination of e-learning and interactive online sessions\, the course
covers essential skills for data handling\, visualization\, and basic stat
istical testing. Participants will learn to use R and RStudio to perform c
ommon analyses—such as t-tests\, ANOVA\, and non-parametric alternatives
—and to produce clear\, publication-ready figures. The training also int
roduces reproducible workflows\, preparing participants for more advanced
data analysis and modelling applications.
Target audience
This course is designed for PhD students\, postdoctoral research
ers\, scientists\, technical staff\, and industry professionals who are ne
w to R and need a practical introduction to statistical analysis. It is pa
rticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
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DTSTAMP:20260430T194012Z
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DTSTART:20260907T070000Z
SEQUENCE:0
TRANSP:OPAQUE
DTEND:20260907T103000Z
SUMMARY:Basic statistics in R
CLASS:PUBLIC
DESCRIPTION:This introductory training provides a practical foundation in s
tatistical analysis using R\, tailored for participants with no prior prog
ramming experience. Through a combination of e-learning and interactive on
line sessions\, the course covers essential skills for data handling\, vis
ualization\, and basic statistical testing. Participants will learn to use
R and RStudio to perform common analyses—such as t-tests\, ANOVA\, and
non-parametric alternatives—and to produce clear\, publication-ready fig
ures. The training also introduces reproducible workflows\, preparing part
icipants for more advanced data analysis and modelling applications.Target
audienceThis course is designed for PhD students\, postdoctoral researche
rs\, scientists\, technical staff\, and industry professionals who are new
to R and need a practical introduction to statistical analysis. It is par
ticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
X-ALT-DESC;FMTTYPE=text/html:This introductor
y training provides a practical foundation in statistical analysis using R
\, tailored for participants with no prior programming experience. Through
a combination of e-learning and interactive online sessions\, the course
covers essential skills for data handling\, visualization\, and basic stat
istical testing. Participants will learn to use R and RStudio to perform c
ommon analyses—such as t-tests\, ANOVA\, and non-parametric alternatives
—and to produce clear\, publication-ready figures. The training also int
roduces reproducible workflows\, preparing participants for more advanced
data analysis and modelling applications.
Target audience
This course is designed for PhD students\, postdoctoral research
ers\, scientists\, technical staff\, and industry professionals who are ne
w to R and need a practical introduction to statistical analysis. It is pa
rticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
DTSTAMP:20260430T194012Z
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UID:69f3b01c51115
DTSTART:20260910T070000Z
SEQUENCE:0
TRANSP:OPAQUE
DTEND:20260910T103000Z
SUMMARY:Basic statistics in R
CLASS:PUBLIC
DESCRIPTION:This introductory training provides a practical foundation in s
tatistical analysis using R\, tailored for participants with no prior prog
ramming experience. Through a combination of e-learning and interactive on
line sessions\, the course covers essential skills for data handling\, vis
ualization\, and basic statistical testing. Participants will learn to use
R and RStudio to perform common analyses—such as t-tests\, ANOVA\, and
non-parametric alternatives—and to produce clear\, publication-ready fig
ures. The training also introduces reproducible workflows\, preparing part
icipants for more advanced data analysis and modelling applications.Target
audienceThis course is designed for PhD students\, postdoctoral researche
rs\, scientists\, technical staff\, and industry professionals who are new
to R and need a practical introduction to statistical analysis. It is par
ticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
X-ALT-DESC;FMTTYPE=text/html:This introductor
y training provides a practical foundation in statistical analysis using R
\, tailored for participants with no prior programming experience. Through
a combination of e-learning and interactive online sessions\, the course
covers essential skills for data handling\, visualization\, and basic stat
istical testing. Participants will learn to use R and RStudio to perform c
ommon analyses—such as t-tests\, ANOVA\, and non-parametric alternatives
—and to produce clear\, publication-ready figures. The training also int
roduces reproducible workflows\, preparing participants for more advanced
data analysis and modelling applications.
Target audience
This course is designed for PhD students\, postdoctoral research
ers\, scientists\, technical staff\, and industry professionals who are ne
w to R and need a practical introduction to statistical analysis. It is pa
rticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
DTSTAMP:20260430T194012Z
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DTSTART;VALUE=DATE:20260914
SEQUENCE:0
TRANSP:OPAQUE
DTEND;VALUE=DATE:20260915
SUMMARY:Basic statistics in R
CLASS:PUBLIC
DESCRIPTION:This introductory training provides a practical foundation in s
tatistical analysis using R\, tailored for participants with no prior prog
ramming experience. Through a combination of e-learning and interactive on
line sessions\, the course covers essential skills for data handling\, vis
ualization\, and basic statistical testing. Participants will learn to use
R and RStudio to perform common analyses—such as t-tests\, ANOVA\, and
non-parametric alternatives—and to produce clear\, publication-ready fig
ures. The training also introduces reproducible workflows\, preparing part
icipants for more advanced data analysis and modelling applications.Target
audienceThis course is designed for PhD students\, postdoctoral researche
rs\, scientists\, technical staff\, and industry professionals who are new
to R and need a practical introduction to statistical analysis. It is par
ticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
X-ALT-DESC;FMTTYPE=text/html:This introductor
y training provides a practical foundation in statistical analysis using R
\, tailored for participants with no prior programming experience. Through
a combination of e-learning and interactive online sessions\, the course
covers essential skills for data handling\, visualization\, and basic stat
istical testing. Participants will learn to use R and RStudio to perform c
ommon analyses—such as t-tests\, ANOVA\, and non-parametric alternatives
—and to produce clear\, publication-ready figures. The training also int
roduces reproducible workflows\, preparing participants for more advanced
data analysis and modelling applications.
Target audience
This course is designed for PhD students\, postdoctoral research
ers\, scientists\, technical staff\, and industry professionals who are ne
w to R and need a practical introduction to statistical analysis. It is pa
rticularly relevant for those working with biological or experimental data
and planning to progress to more advanced topics such as omics analyses\,
computational biology\, or statistical modelling.
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