| 1 |
M |
Getting Started: R basics |
R installation + Course introduction |
Introductory Slides |
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| 2 |
W |
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Lecture: Introduction to the R syntax: vectors, matrices, functions, concatenation, etc. Watch 1.1 - 1.7, 2.1, 2.3, 2.4, 3.2, 4.1 |
Lab 2: Swirl [Solution] |
Slides: The R-syntax |
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| 3 |
Th |
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Lecture: Introduction to data handling - Importing and transforming data, loading packages (see Lab 3) & Introduction to Rmarkdown a) basics of Rmarkdown b) more comprehensive Rmarkdown video |
Lab 3: Basics of coding in R [Solution] |
Exercise Template for Lab 3 |
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| 4 |
M |
Tidyverse and Basic Probability Models |
Lecture: Using the tidyverse syntax and cleaning up the data |
Lab 4: Tidyverse [Solution] |
Slides: Tidyverse |
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| 5 |
W |
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Lecture: Introduction to basic probability models: probabilities, distributions and the CLT Part I: a) Motivation b) Random Variable c) Probability Distributions d) Bernoulli Distribution e) Binomial Distribution f) Poisson Distribution |
Lab 5: Probability Models I [Solution] |
Exercise Template for Lab 5 |
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| 6 |
Th |
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Lecture: Introduction to basic probability models: probabilities, distributions and the CLT Part II: a) Normal Distribution b) Populations, Samples, Estimates c) Central Limit Theorem d) CLT in Practice |
Lab 6: Probability Models II [Solution] |
Exercise Template for Lab 6 |
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| 7 |
M |
Simulations |
Lecture: Simulations a) Simulations: ex1 b) Simulations: ex2 (CLT) c)Generating simulated data |
Lab 7: Simulations [Solution] |
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| 8 |
W |
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Lecture: Plotting with ggplot2 (I) |
Lab 8: ggplot2 (I) [Solution] |
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| 9 |
Th |
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Lecture: Plotting with ggplot2 (II) |
Lab 9: ggplot2 (II) [Solution] |
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| 10 |
M |
Testing and Statistical models |
Lecture: Testing a)Null Hypothesis b)Types of Errors |
Lab 10: Hypothesis Testing [Solution] |
Lab 10 Material Rmd |
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| 11 |
W |
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Lecture: Multiple Hypothesis Testing: a)The challenge of multiple testing b) randomness of the p-values c)Procedures and error rates d)Procedures and error rates: example e)Bonferonni correctionf)The Benjamini-Hochberg procedure |
Lab 11: Multiple Testing [Solution] |
Lab 11 Material Rmd |
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| 12 |
Th |
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Lecture: ANOVA |
Lab 12: ANOVA [Solution] |
Lab 12 Material Rmd |
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| 13 |
M |
Regression |
Lecture: Simple Linear Regression: slr(1) slr(2) slr(3) |
Lab 13: Simple Linear Regression [Solution] |
Lab 13 Material Rmd |
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| 14 |
W |
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Lecture: Multivariate Regression: mlr(1) mlr(2) mlr(3) mlr(4) |
Lab 14: Multivariate Regression [Solution] |
Lab 14 Material Rmd |
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| 15 |
Th |
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Lecture: Poisson regression |
Lab 15: Poisson Regression with solutions Lab 15: In Class |
Lab 15 Material Rmd Lab 15: In Class Exercise Rmd Lab 15: In Class Exercise Solutions (part1) |
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| 16 |
M |
GLMs and Bootstrap tests |
Lecture: Generalized Linear Models |
Lab 16: GLM [Solution] |
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| 17 |
W |
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Lecture: Logistic Regression |
Lab 17: Logistic Regression [Solution] |
Lab 17 Material Rmd |
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| 18 |
Th |
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Summary: statistical models |
Lab 18 [Solution] |
Lab 18 Rmd [Data] |
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| 19 |
M |
Unsupervised Learning |
Lecture: Multivariate analysis |
Slides |
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| 20 |
W |
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Lecture: Clustering |
Lab 20: Clustering |
Lab 20 [Rmd] sce416b Data |
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| 21 |
Th |
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In class on Clustering |
Lab 21 In class [Solution] |
Lab 21 Rmd |
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| 22 |
M |
Machine Learning |
Lecture: Introduction to Machine Learning |
Lab 22: Machine Learning [Solution] |
Lab 22 Rmd Link to the slides |
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| 23 |
W |
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Lecture: Model selection and trees |
Lab: Model selection and trees |
Lab 23 Rmd |
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| 24 |
Th |
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In-class Exercise |
Lab 24 [Solution] |
Lab 24 Rmd |
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