Bootstrap:Data Science

Students learn about a diverse group of programmers through a short film and a gallery walk of our Pioneers in Computing and Mathematics poster series, then consider the problem solving advantages that diverse teams foster.

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.

Students begin to program, exploring how Numbers, Strings, Booleans and operations on those data types work in a programming language. Booleans offer an excellent opportunity for students to explore the meaning and real-world uses of inequalities.

Students learn about functions for sorting and counting data in tables, then are introduced to one-variable displays.

Students use data displays like bar and pie charts to create 1- and 2-level groupings to visualize the distribution of categorical data.

Infographics are a powerful tool for communicating information, especially when made by people who understand how to connect visuals to data in meaningful ways. This project is an opportunity for students to become more flexible math thinkers while tapping into their creativity. This project supports the learning goals of our lesson on Bar and Pie Charts.

Students are introduced to the Data Cycle, a four-step scaffold for answering questions from a dataset…​and then generating the next question! Students learn to identify - and ask - statistical questions, by comparing and contrasting them with other kinds of questions.

Students explore sampling and probability as a mechanism for detecting patterns. After exploring this in a binary system (flipping a coin), they consider the role of sampling as it applies to relationships in a dataset.

Students practice making a variety of chart types and then begin to investigate a real world dataset, which they will continue to work with for the remainder of the course.

Students choose a real world dataset that is interesting to them and practice making and interpreting a range of displays using that dataset. This project spans up to nine of our Data Science lessons, each of which includes an optional section with project-specific directions. We have built a Library of Datasets to support this project.

Students are introduced to Histograms by comparing them to bar charts, learning to construct them by hand and in the programming environment.

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.

Students are introduced to mean, median and mode(s) and consider which of these measures of center best describes various quantitative data.

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.

Students learn how standard deviation serves as Data Scientists' most common measure of "spread": how far all the values in a dataset tend to be from their mean. When we looked at box plots, we visualized spread based on range and interquartile range. Now we’ll return to histograms and picture the spread in terms of standard deviation.

Students investigate scatter plots as a method of visualizing the relationship between two quantitative variables. In the programming environment, 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.

Students compute the “line of best fit” using the function for linear regression, and summarize linear relationships in a dataset.

Students consider ethical issues and privacy in the context of data science.

Students learn about the importance of careful data collection, by confronting a "dirty" dataset. They then design a simple survey of their own, gather their data, and import it into Pyret

Students come up with a research question and design a survey to gather data to answer it. They exchange surveys to get some hands-on practice with clean and dirty data and incorporate what they learn to polish their surveys. This project supports the learning goals of our lesson on Collecting Data.

Students learn how to extract individual rows from a table, and columns from a row.

Students discover that they can make their own functions.

Students learn to connect function descriptions across three representations: Contracts (a mapping between Domain and Range), Examples (a list of discrete inputs and outputs), and Definitions (symbolic).

Students develop and define a function of their own. The function must take in an image and manipulate it using at least three transformations. This project supports the learning goals of Functions: Contracts, Examples & Definitions.

Students are introduced to the Design Recipe as a scaffold for breaking down word problems into smaller steps. They apply the Design Recipe to fixing a file that launches a rocket!

Students use the Design Recipe to define functions that consume rows, developing a structured approach to answering questions by transforming tables.

Defining functions allows data scientists to create advanced data displays, which expose deeper insight into a dataset. This motivates students to define their own functions and deepen their analysis.

Students define row-consuming functions and combine them with advanced display contracts to create compelling data displays. This project supports the learning goals of Advanced Displays.

Students learn about functions that work with tables, allowing them to filter and build columns

Students learn how to compose functions that operate on tables.

Students practice creating grouped samples (non-random subsets) and think about why it might sometimes be useful to answer questions about a dataset through the lens of one group or another.

Students consider the concept of trust and testing — how do we know if a particular analysis is trustworthy?

Students consider possible threats to the validity of their analysis.

Students investigate four types of threats to validity by pretending to be “bad data scientists” who fail to consider the impact of selection bias, bias in the study design, poor choice of summary data, and confounding variables. This project supports the learning goals of our lesson on Threats to Validity.

This project can be used as a capstone for Bootstrap: Data Science. It is designed to give students a deep dive into a dataset and use everything they’ve learned throughout the course, not only about making and interpreting displays, but about the practice of refining our questions through the Data Cycle and deciding which displays are most useful in telling the data’s story. This project is an extension of the Project: Dataset Exploration.

Students use linear models to investigate relationships in demographic data about US states using an inquiry-based approach, involving hypothesizing, experimental and computational modeling, and sense-making.

Students investigate quadratic relationships in data about Fuel Efficiency, using an inquiry-based model, involving hypothesizing, experimental and computational modeling, and sense-making.

Students investigate exponential relationships in data about Covid spread, using an inquiry-based model involving hypothesizing, experimental and computational modeling, and sense-making. They are introduced to table transformations and inverse functions, which are used to fit exponential models onto nonlinear data.

Students investigate logarithmic relationships in demographic data about countries of the world, using an inquiry-based model, involving hypothesizing, experimental and computational modeling, and sense-making.