Specialty Area

Data Science

Example Course Pathway

In this section, we present examples of how to package this content into meaningful course sequences across particular specialty areas. The boxes in each diagram represent discrete courses.

Course Definitions

This section contains example courses and descriptions, with the assumption that individual schools and districts may modify the offerings to meet local contexts and needs.

Computer Science Foundations

Programming the Future

Data Science and Analytics

Content Progression

This content may lead to a data science major and a career as, for example, a data scientist, data modeler, statistician, or data ethicist.

Foundational CS Content

Programming basics
Cleaning and using data
Social and ethical implications
Data bias
Testing and debugging
Inclusive collaboration on data projects

Fundamentals

Extract meaning from tabular data using a function
Descriptive statistics
Simple visualizations
Data forms and bias (ethics)
Transform and prepare data
Data validity (clean and accurate)
Data privacy, security, bias, missing data, ethics
Make predictions (using e.g., frequentist and Baysian statistics)
Legal and ethical implications
Data science tools
Structured problem-solving (case studies; case analysis)
Query formation (prompt engineering; SQL; elastic search)
Statistics (normal distribution, descriptive statistics, regression analysis)
Data fairness and bias (mitigating bias)
Data security
Intersection of data science and other fields
Careers in data science
Common machine learning algorithms associated with data science, including linear regression and hypothesis testing
IDEs for Data Science (e.g., PyCharm, RStudio, Azure)

Specialty

Distributed cloud based systems
Data storage locations and data manipulation (e.g., APIs, building .json, data scraping, finding and processing data outside of a traditional data location)
Databases (structured and unstructured data) (e.g., relational, graph, vector databases, other NoSQL)
Data modeling (e.g., how to map tables together)
Machine learning basics
Data validity, credibility, and reliability (data consciousness),
Data visualization
Data from wearables and its implications
Data privacy and security
Database architecture
Interface development for data analysis (e.g., BI tools, such as PowerBI, Tableau)
Common algorithms for data science (e.g., gradient boosting machines, support vector machines, random forest)
Designing, imagining, and critiquing new ways to get, use, and restrict data