Difference between revisions of "UNIL MSc course: Data Analysis I 2023"

(Created page with "'''Introduction''' This course is for MSc students both from the MLS and BEC master programs. The goal is to review fundamental notions in statistics and show how they are use...")
 
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This course is taught in 4 blocks. Each block has 2h frontal lecture:
 
This course is taught in 4 blocks. Each block has 2h frontal lecture:
  
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22.09.2023  9:00-11:00  Basic notions of probability theory, Central Limit Theorem     
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** 22.09.2023  9:00-11:00  Basic notions of probability theory, Central Limit Theorem     
 
[https://docs.google.com/presentation/d/12JxTXy-eIRgr-5nnYoPlfLrYU2PFCn6JchC6--X6mzg Slides]
 
[https://docs.google.com/presentation/d/12JxTXy-eIRgr-5nnYoPlfLrYU2PFCn6JchC6--X6mzg Slides]
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29.09.2023 9:00-11:00 Important distributions and tests, sampling and estimates Slides
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** 29.09.2023 9:00-11:00 Important distributions and tests, sampling and estimates Slides
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06.10.2023 9:00-11:00 Introduction to linear regression Slides
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** 06.10.2023 9:00-11:00 Introduction to linear regression Slides
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13.10.2023 9:00-11:00 Introduction to mixed models and Cox models Slides
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** 13.10.2023 9:00-11:00 Introduction to mixed models and Cox models Slides
  
 
The lectures are accompanied by supervised exercise sessions (see below).
 
The lectures are accompanied by supervised exercise sessions (see below).
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For each question we indicate how many points one receives for a correct answer.  The grade of each exercise is the sum of these points truncated at 6. The final grade is the average of the grades from the three sets.
 
For each question we indicate how many points one receives for a correct answer.  The grade of each exercise is the sum of these points truncated at 6. The final grade is the average of the grades from the three sets.
  
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[[Exercise set #1|https://docs.google.com/document/d/1AHbYft2Lsv9YvAhusYoj2PxEnLXoK_QawtbE9OPnNpg]] (due on 28.09.2023 22:00)
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[|https://docs.google.com/document/d/1AHbYft2Lsv9YvAhusYoj2PxEnLXoK_QawtbE9OPnNpg Exercise set #1] (due on 28.09.2023 22:00)
 
Exercise set #2 (due on 05.10.2023 22:00)
 
Exercise set #2 (due on 05.10.2023 22:00)
 
Exercise set #3 (due on 12.10.2023 22:00)
 
Exercise set #3 (due on 12.10.2023 22:00)

Revision as of 11:49, 22 September 2023

Introduction This course is for MSc students both from the MLS and BEC master programs. The goal is to review fundamental notions in statistics and show how they are useful for basic data analysis challenges in biology.

Prerequisites Students who follow this course should already have some basic knowledge in statistics and R equivalent to the one achieved by bachelor students at the University of Lausanne. To catch up, if needed, students can participate in dedicated R refresher sessions for Master students by F. Schütz on Wednesday 10:15-12:00 in Gen/C (except 15.11.23 : BIO/Amphi).

Resources The following software is required for this course:

a recent version of R (in doubt, simply download and install the latest version from the R website at https://stat.ethz.ch/CRAN/ -- many potential problems are solved by simply upgrading to the latest R version) RStudio (which you can download from http://www.rstudio.com/products/rstudio/download/) Here are the slides related to the third lecture on Introduction to Regression Analysis

Schedule and content This course is taught in 4 blocks. Each block has 2h frontal lecture:

    • 22.09.2023 9:00-11:00 Basic notions of probability theory, Central Limit Theorem

Slides

    • 29.09.2023 9:00-11:00 Important distributions and tests, sampling and estimates Slides
    • 06.10.2023 9:00-11:00 Introduction to linear regression Slides
    • 13.10.2023 9:00-11:00 Introduction to mixed models and Cox models Slides

The lectures are accompanied by supervised exercise sessions (see below).

Exercises and grades This course has no oral or written exam; instead students must submit their solutions to exercises.

There will be four sets of exercises - one for each session. The exercise corresponding to each session is discussed 11:15-12:00 right after the lecture to make sure all questions are clear. The exercises are due on Thursdays at 22:00, six day after the lecture. They are discussed on the following day in the afternoon at 13:00-14:00.

For each question we indicate how many points one receives for a correct answer. The grade of each exercise is the sum of these points truncated at 6. The final grade is the average of the grades from the three sets.

[|https://docs.google.com/document/d/1AHbYft2Lsv9YvAhusYoj2PxEnLXoK_QawtbE9OPnNpg Exercise set #1] (due on 28.09.2023 22:00) Exercise set #2 (due on 05.10.2023 22:00) Exercise set #3 (due on 12.10.2023 22:00) Exercise set #4 (due on 17.10.2023 22:00)


Assignment icon Submit Exercise set #1 Assignment Assignment icon Submit Exercise set #2 Assignment Assignment icon Submit Exercise set #3 Assignment Assignment icon Submit Exercise set #4

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