If you’re teaching remotely, we’ve assembled an Implementation Notes page that makes specific recommendations for in-person v. remote instruction.
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We provide all of our materials free of charge, to anyone who is interested in using our lesson plans or student workbooks.
- Introduction to Computational Data Science
Students learn about Categorical and Quantitative data, are introduced to Tables by way of the Animals Dataset, and consider what questions can and cannot be answered with available data.
- Simple Data Types
Students begin to program, explorings how Numbers, Strings, Booleans and operations on those data types work in this programming language.
Students learn how to apply Functions in the programming environment and interpret the information contained in Contracts: Name, Domain and Range. Image-producing functions provide an engaging context for this exploration.
- Displaying Categorical Data
Students learn to generate and compare pie charts & bar charts, explore other plotting & display functions, and (optionally) design an infographic.
- Data Displays and Lookups
Students use displays to answer questions, focusing on which displays make sense for the data they are working with. They also learn how to extract individual rows from a table, and columns from a row.
- Table Methods
Students learn about table methods, which allow them to order, filter, and build columns to extend the animals table.
- Defining Functions
Students discover that they can make their own functions and are introduced to a structured approach to building them called the Design Recipe.
- Defining Table Functions
Students use the Design Recipe to define operations on tables, developing a structured approach to answering questions by transforming tables.
- Method Chaining
Students learn how to chain Methods together, and define more sophisticated subsets.
Image-scatter-plots explose deeper insight into subgroups within a population, motivating the need for more advanced analysis and adding if-expressions to students' programming toolkit.
- Randomness and Sample Size
Students learn about random samples and statistical inference, as applied to the Animals Dataset. In the process, students get a light introduction to the role of sample size and the importance of statistical inference.
- Grouped Samples
Students practice creating subsets and think about why it might sometimes be useful to answer questions about a dataset through the lens of specific subsets.
- Choosing Your Dataset
Students select a real world dataset to investigate for the remainder of the course. They begin their analysis by identifying categorical and quantitative columns, and defining a few random and logical subsets.
Students are introduced to Histograms by comparing them to bar charts, learning to construct them by hand and in the programming environment.
- Visualizing the “Shape” of Data
Students explore the concept of "shape", using histograms to determine whether a dataset has skewness, and what the direction of the skewness means. They apply this knowledge to the Animals Dataset, and then to their own.
- Measures of Center
Students are introduced to mean, median and mode(s) and consider which of these measures of center best describes various quantitative data.
- Spread of a Data Set
Students are introduced to box plots, learn to evaluate the spread of a quantitative column, and deepen their perspective on shape by matching box plots to histogram.
- Checking Your Work
Students consider the concept of trust and testing — how do we know if a particular analysis is trustworthy?
- Scatter Plots
Students investigate scatter plots as a method of visualizing the relationship between two quantitative variables. In the programming environemt, points on the scatter plot can be labelled with a third variable!
Students deepen their understanding of scatter plots, learning to describe and interpret direction and strength of linear relationships.
- Linear Regression
Students compute the “line of best fit” using the function for linear regression, and summarize linear relationships in a dataset.
- Ethics and Privacy
Students consider ethical issues and privacy in the context of data science.
- Threats to Validity
Students consider possible threats to the validity of their analysis.
- All the lessons
This is a single page that contains all the lessons listed above.
Of course, there’s more to a curriculum than software and lesson plans! We also provide a number of resources to educators, including standards alignment, a complete student workbook, an answer key for the programming exercises and a forum where they can ask questions and share ideas.
These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, and 1738598). Bootstrap:Data Science by the Bootstrap Community is licensed under a Creative Commons 4.0 Unported License. This license does not grant permission to run training or professional development. Offering training or professional development with materials substantially derived from Bootstrap must be approved in writing by a Bootstrap Director. Permissions beyond the scope of this license, such as to run training, may be available by contacting contact@BootstrapWorld.org.