lecture_learning_objectives.md

DSCI 100 Lecture learning objectives

1. Introduction to Data Science

By the end of the chapter, students will be able to:

• use a Jupyter notebook to execute provided R code
• edit code and markdown cells in a Jupyter notebook
• create new code and markdown cells in a Jupyter notebook
• load the tidyverse library into R
• create new variables and objects in R using the assignment symbol
• use the help and documentation tools in R
• match the names of the following functions from the tidyverse library to their documentation descriptions:
  • read_csv
  • select
  • mutate
  • filter
  • ggplot
  • aes
• chain together two functions using the pipe operator, %>%

2. Reading in data locally and from the web

By the end of the chapter, students will be able to:

• define the following:
  • absolute file path
  • relative file path
  • url
• discuss why relative paths should be used over absolute paths in a Jupyter notebook
• match the following tidyverse read_* function argument descriptions to a simplified version of them:
  • file
  • delim
  • col_names
  • skip
• write a relative file path for the file argument of tidyverse read_* functions
• choose the appropriate tidyverse read_* function and function arguments to load a given tabular data set into R
• use the rvest html_nodes, html_text and html_attr functions to scrape data from a .html file on the web
• compare downloading tabular data from a plain text file (e.g., .csv) from the web versus scraping data from a .html file

3. Cleaning and wrangling data

By the end of the chapter, students will be able to:

• define the term "tidy data"
• discuss the advantages and disadvantages from storing data in a tidy data format
• recall and use the following tidyverse functions and operators for their intended data wrangling tasks:
  • select
  • filter
  • map
  • mutate
  • summarise
  • group_by
  • gather
  • %in%

4. Effective data visualization

By the end of the chapter, students will be able to:

• Define the three key aspects of ggplot objects:
  • aesthetic mappings
  • geometric objects
  • scales
• Use the ggplot2 function in R to create the following visualizations:
  • 2-D scatter plot
  • 2-D scatter plot with a third variable that stratifies the groups
  • count bar chart for multiple groups
  • proportion bar chart for multiple groups
  • stacked bar chart for multiple groups
• List the rules of thumb for effective visualizations
• Given a visualization and a sentence describing it's intended task, evaluate it's effectiveness and suggest ways to improve the visualization with respect to that intended task

5. Classification

By the end of the chapter, students will be able to:

• Recognize situations where a simple classifier would be appropriate for making predictions.
• Explain the k-nearest neighbour classification algorithm.
• Interpret the output of a classifier.
• Compute, by hand, the distance between points when there are two attributes.
• Describe what a training data set is and how it is used in classification.
• In a dataset with two attributes, perform k-nearest neighbour classification in R using caret::train(method = "knn", ...) to predict the class of a single new observation.

6. Classification, continued

By the end of the chapter, students will be able to:

• Describe what a test data set is and how it is used in classification.
• Using R, evaluate k-nn classification accuracy using a test data set and appropriate metrics.
• Using R, execute cross-validation in R to choose the number of neighbours for k-nn classification.
• Identify when it is necessary to scale variables before classification and do this using R
• In a dataset with > 2 attributes, perform k-nearest neighbour classification in R using caret::train(method = "knn", ...) to predict the class of a test dataset.
• Describe advantages and disadvantages of the k-nearest neighbour classification algorithm.

7. Clustering

By the end of the chapter, students will be able to:

• Describe a case where clustering would be an appropriate tool, and what insight it would bring from the data.
• Explain the kmeans clustering algorithm.
• Interpret the output of a kmeans cluster analysis.
• Perform kmeans clustering in R using kmeans.
• Visualize the output of kmeans clustering in R using pair-wise scatter plots.
• Identify when it is necessary to scale variables before clustering and do this using R.
• Use the elbow method to choose the number of clusters for k-means.
• Describe advantages, limitations and assumptions of the kmeans clustering algorithm.

8. Regression

By the end of the chapter, students will be able to:

• Recognize situations where a simple regression analysis would be appropriate for making predictions.
• Explain the k-nearest neighbour (k-nn) regression algorithm and describe how it differs from k-nn classification.
• Interpret the output of a k-nn regression.
• In a dataset with two variables, perform k-nearest neighbour regression in R using caret::knnregTrain() to predict the values for a test dataset.
• Using R, execute cross-validation in R to choose the number of neighbours.
• Using R, evaluate k-nn regression prediction accuracy using a test data set and an appropriate metric (e.g., means square prediction error).
• Describe advantages and disadvantages of the k-nearest neighbour regression approach.

9. Regression, continued

By the end of the chapter, students will be able to:

• In the context of k-nn regression, compare and contrast goodness of fit and prediction properties (namely MSE vs MSPE).
• In a dataset with 2 variables, perform simple ordinary least squares regression in R using lm() to predict the values for a test dataset.
• Compare and contrast predictions obtained from k-nearest neighbour regression to those obtained using simple ordinary least squares regression from the same dataset.

10. Regression, continued some more...

By the end of the chapter, students will be able to:

• Explain in words or using drawings, how bootstrap samples can be generated from a single sample.
• From a single sample, generate multiple samples through bootstrapping using R.
• In R, overlay the ordinary least squares regression lines from multiple bootstrapped samples on a single plot.
• Discuss what the collection of these slopes represents.