THE UNIVERSITY OF CHICAGO UCHICAGO
Start Here Considerations for Looking at these Curricula

There is no single way to teach data science.

To make decisions about your data science course, as with the development of any course, you need to reflect on particular learning goals and target outcomes, anticipated prerequisites, alignment or integration with existing instructional initiatives, and more.

Consider how introductory data science is situated in your school, district, or state. For example:

  • Identify where data science resides in your course sequencing. This could range from being part of a mathematics course sequence to being a senior elective.
  • Take into account instructional approaches you want to see in the classroom. This could include having students collaborate on projects, facilitating their communication with each other, or giving students choice.
  • Determine your need for flexibility in your data science program. This may include accounting for a range of student learning needs, changing lesson sequencing, alignment with other subject areas, or technology options.
  • Account for content emphasis. Data science materials vary with regard to their emphasis on programming, statistics, and other content areas.
  • Take into account the optimal amount of teacher support needed. This can vary depending on teachers’ content background and teaching experience.

There is no best curriculum for teaching data science; there is only a curriculum that is best for you and your students.

Bootstrap

CourseKata

IDS

youcubed

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Format

Bootstrap is a set of 29 online web-based lessons with optional projects. Student worksheets can be printed on site or purchased as a bound workbook.

CourseKata is composed of an online interactive book and a web-based “Teaching Dashboard” that includes Jupyter Notebooks  designed to be integrated into the course.

IDS has online lessons available to teachers who complete the professional development. A clickable PDF is available to all. Lessons are paired with a technology suite of data collection tools and data analysis labs.

Youcubed is a set of 8 online project-based units.

Programming Language

Bootstrap uses its own programming language called “pyret” with lessons also available in

CourseKata uses R with Jupyter Notebooks .

IDS uses R, through the interface of
.

Youcubed uses
and
as well as technology tools such as Tableau and Google Data Commons, Sheets, and Colab using Python.

Standards Alignment

Bootstrap provides alignment information for the Common Core State Standards for mathematics and English language arts, the Computer Science Teacher Association Standards, various mathematics textbooks and individual state standards.

CourseKata provides alignment information for the Common Core State Standards for mathematics It also has a customized alignment document for the state of Utah (created to assist Utah with its pilot).

IDS provides alignment information for the Common Core State Standards for mathematics as well as the California Common Core Standards.

Youcubed provides alignment information for the Common Core State Standards for mathematics and English language arts as well as the California computer science standards.

Assessment

Bootstrap provides assessment rubrics for every project.

There is a Learning Management System (Canvas, Blackboard, or Moodle) that enables the teacher to monitor progress. CourseKata provides quizzes and tests in addition to assessment rubrics for every projects.

IDS provides teachers with periodic rubric based scoring guides.

Youcubed provides criteria for assessing the projects at the end of each unit and explicit opportunities for student self-assessment. Over time, students compile their project work into a portfolio.

Prerequisites

Bootstrap has no identified prerequisites.

Course Kata’s AB book (the one reviewed here) has no required prerequisites. The ABC book recommends Algebra II as a prerequisite.

IDS suggests a first-year Algebra course as a prerequisite

Algebra 1 and Geometry or Math 1 and Math 2.

Cost

Bootstrap materials and its cloud-based software are free.

CourseKata costs $24/student each year with discounts available for multi-year contracts.

A PDF of teaching materials is free and an online version is available to teachers who complete the PD. The technology package costs $5 per student and $13 per teacher per month.

Youcubed materials are free.

Professional Development

Bootstrap offers a variety of PD models to best meet the needs of their partners and teachers. Accordingly, the cost varies. One model, for example, calls for  5 days in the summer and 4 academic-year sessions. Another is 3 days in the summer with 2 days before the beginning of the academic year. All include coaching sessions. Costs start at $1800 per teacher.

CourseKata offers study groups that include either seven one-hour sessions three times a week for two weeks in the summer or one time a week for seven weeks during the academic year. It also offers targeted workshops (e.g. pacing, grading). PD is $800/teacher for each of the first two years of use, which includes permanent access to study groups, office hours and other supports.

IDS has a two-year professional development process that includes 9 days of PD in the first year and then 4 days in the second year. The first year is focused on implementation and costs $5,500 per participant. The second year focuses on deeper data analysis and costs $2,818 per participant. PD is complimentary for 2 administrators or counselors.

Youcubed offers professional development in two formats: weekly 2-hour sessions during the academic year or 4 daylong sessions in the summer online or in person. The 4-day session is $1990 per person and $1800 per person for groups of 2 or more.

Data Collection and Preparation
Data Cleaning
Categorical vs. numerical data
Creating and interpreting data frames
Diverse data formats and representation
Research Design and Communication
Data collection design
Correlation v. causation
Sampling
Simulations
Determining source credibility
Business report writing
Independent research project component
Coding and Programming
Data processing and analysis using spreadsheets
Use of programming language
Introduction to vectors
Probability
Introduction to simple theoretical probability
Law of large numbers
Introduction to vectors
Probability
Introduction to simple theoretical probability
Sampling
Law of large numbers
Descriptive Statistics
Basic univariate data visualizations
Shape and distribution analysis
Mean, median, mode, IQR
Bivariate visualisation
Variance or standard deviation
Standard normal and z distribution
r and r-squared
SST
SSR, SSE
MAD
Inferential Statistics and Sampling Distributions
Difference between probability and statistical inference
Confidence intervals
A/B Testing
Bootstrapping
Simulations
ANOVA
Data Ethics
Privacy
Predictive policing and algorithmic bias
Misuse of data to mislead
Introduction to Models
Explaining and interpreting simple linear regression
Explaining and interpreting multiple linear regression
Explaining and interpreting decision trees
Explaining and interpreting k-means clustering
Modeling – Training, Testing, and Analyzing Models
Fitting simple linear regression
Fitting multiple linear regression
Fitting decision trees
Fitting k-means clustering
Train and test split
Model complexity and overfitting
Diversity and Social Impact
Lesson content on inclusion and diversity
Lesson content on data responsibility
Explicit focus on social impact
Treatment of Data Science
Data science cycle/process
Data scientists
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