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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- Introduction to Computational Data Science
Students are introduced to the Animals Dataset, learn about Tables, Categorical and Quantitative data, and consider the kinds of questions that can be asked about a dataset.
- Simple Data Types
Students begin to program in Pyret, learning about basic data types, and operations on those data types.
Students learn how to apply Functions in the programming environment, encounter Image data types, and learn how to interpret the information contained in a Contract: Name, Domain and Range.
- Displaying Categorical Data
Students learn to generate pie charts and bar charts, and explore other plotting and display functions.
- Data Displays and Lookups
Students continue to practice making different kinds of data displays, this time focusing less on programming and more on using displays to answer questions. 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 functions as an abstraction over a programming pattern, and are introduced to a structured approach to building them called the Design Recipe.
- Defining Table Functions
Students explore using multiple representations of functions to solve word problems involving Data Rows, using a process called the Design Recipe.
- Method Chaining
Students continue practicing their Design Recipe skills, making lots of simple functions dealing with the Animals Dataset. Then they learn how to chain Methods together, and define more sophisticated subsets.
Students build on their knowledge of the image-scatter-plot function, motivating the need for if-expressions in their programming toolkit. This drives deeper insight into subgroups within a population, and motivates the need for more advanced analysis.
- 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 learn about grouped samples, and practice creating them from the Animals Dataset. In the process, they practice using the Design Recipe to create filter functions, and come up with questions they wish to explore.
- Choosing Your Dataset
Students summarize their dataset by exploring the data and identifying categorical and quantitative columns, data types, and more. They also define a few sample rows, random subsets, and logical subsets.
Students explore new visualizations in Pyret, this time focusing on the distribution in a quantitative dataset. Students are introduced to Histograms by comparing them to bar charts, and learn to construct them by hand and in Pyret.
- 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 learn different ways to report the center of a quantitative data set: mean, median and mode(s). After applying these concepts to a contrived dataset, they apply them to their own datasets and interpret the results.
- Spread of a Data Set
Students learn how to evaluate the spread of a quantitative column using box plots, and explore how this offers a different perspective on shape from what can be achieved with a histogram. After applying these concepts to a contrived dataset, they apply them to their own datasets and interpret the results.
- 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.
Students continue to interpret scatter plots, and think about direction and strength of linear relationships.
- Linear Regression
Students compute the “line of best fit” using 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.