Please enable JavaScript to use CodeHS

Standards Mapping

for Certiport Computational Thinking

61

Standards in this Framework

23

Standards Mapped

37%

Mapped to Course

Standard Lessons
1.1.1
Understand and recognize structured and unstructured data
1.1.2
Understand and recognize different types of data such as text, numeric, date/time, image, and audio
  1. 2.1 Spreadsheet Basics
  2. 2.2 Data Cleaning
  3. 2.3 Sort and Filter
  4. 2.4 Data Visualizations
  5. 2.5 Pivot Tables
  6. 2.6 Statistical Measures
1.1.3
Understand and recognize data encoding (ascii, binary, character mapping)
1.2.1
Recognize and apply Boolean and logical operators
1.2.2
Recognize and apply inductive reasoning
  1. 2.5 Pivot Tables
  2. 2.6 Statistical Measures
  3. 4.2 Final Project
1.2.3
Recognize ambiguity in a logical reasoning problem
1.2.4
Recognize and apply deductive reasoning
  1. 2.5 Pivot Tables
  2. 2.6 Statistical Measures
  3. 4.2 Final Project
1.3.1
Explain the purpose of algorithmic thinking
1.3.2
Understand the purpose of abstraction and model building
1.3.3
Understand the purpose and capabilities of automation
2.1.1
Identify the data needed to solve a problem
  1. 2.1 Spreadsheet Basics
  2. 2.2 Data Cleaning
  3. 2.3 Sort and Filter
  4. 2.4 Data Visualizations
  5. 2.5 Pivot Tables
  6. 2.6 Statistical Measures
  7. 4.2 Final Project
2.1.2
Assess relevance of existing data sets
  1. 4.2 Final Project
2.1.3
Determine the gap between existing data and data needs
  1. 4.2 Final Project
2.2.1
Understand validity
2.2.2
Understand reliability
2.2.3
Explain data cleaning in data sets
  1. 2.2 Data Cleaning
2.3.1
Collect relevant data using existing data sources
  1. 4.2 Final Project
2.3.2
Select appropriate tools to gather, analyze, and process data
  1. 2.1 Spreadsheet Basics
  2. 2.2 Data Cleaning
  3. 2.3 Sort and Filter
  4. 2.4 Data Visualizations
  5. 2.5 Pivot Tables
  6. 2.6 Statistical Measures
  7. 4.2 Final Project
2.3.3
Retrieve information from a data source, such as a list, a table, an infographic, etc.
  1. 4.2 Final Project
2.3.4
Choose a method for creating original data sets such as an observation or a survey
  1. 4.2 Final Project
2.3.5
Use input-validation methods
2.3.6
Explain the legal and ethical dimensions of data collection
  1. 3.1 Data Privacy
  2. 3.2 Big Data and Bias
3.1.1
Identify patterns in data
  1. 2.4 Data Visualizations
  2. 2.5 Pivot Tables
  3. 2.6 Statistical Measures
3.1.2
Organize data using models such as tables, charts, and graphs
  1. 2.4 Data Visualizations
  2. 2.5 Pivot Tables
  3. 2.6 Statistical Measures
3.1.3
Sort and filter data by relevant criteria
  1. 2.3 Sort and Filter
3.1.4
Identify similarities, differences, and subsets in a data set
  1. 2.3 Sort and Filter
  2. 2.4 Data Visualizations
  3. 2.5 Pivot Tables
  4. 2.6 Statistical Measures
3.1.5
Make predictions by examining patterns
  1. 2.3 Sort and Filter
  2. 2.4 Data Visualizations
  3. 2.5 Pivot Tables
  4. 2.6 Statistical Measures
3.2.1
Recognize an abstract representation, such as a model, variable, function, or procedure
3.2.2
Create an abstract model to understand complex systems or facilitate problem solving
3.2.3
Interpret a process flow diagram
4.1.1
Identify an appropriate problem statement based on information provided
  1. 1.2 The Data Science Life Cycle
4.1.2
Define the scope and limitations of a problem
  1. 1.2 The Data Science Life Cycle
4.1.3
Identify decision makers, collaborators, and target audience
  1. 4.1 Data Storytelling
  2. 4.2 Final Project
4.1.4
Break down a problem into component parts by using decomposition
4.2.1
Select a design process, such as iterative or incremental
4.2.2
Identify prerequisites for a solution
  1. 1.1 What is Data Science?
4.2.3
Identify the possible outcomes of a solution
4.2.4
Choose appropriate tools to develop a solution, such as flow charts, spreadsheets, pseudocode, surveys
  1. 2.1 Spreadsheet Basics
  2. 2.2 Data Cleaning
  3. 2.3 Sort and Filter
  4. 2.4 Data Visualizations
  5. 2.5 Pivot Tables
  6. 2.6 Statistical Measures
  7. 4.2 Final Project
5.1.1
Create a sequence of steps
5.1.2
Evaluate the outcome of a sequence of steps
5.1.3
Recognize when to combine steps into reusable procedures and functions
5.2.1
Recognize when to use iteration
5.2.2
Recognize when to use nested loops
5.2.3
Determine the outcome of an algorithm that uses iteration
5.2.4
Create an algorithm that uses iteration
5.3.1
Recognize when to use selection statements
5.3.2
Recognize when to use nesting in selection statements
5.3.3
Determine the outcome of an algorithm that uses selection statements
5.3.4
Create an algorithm that uses selection statements
5.4.1
Recognize when to use variables
5.4.2
Determine the outcome of an algorithm that uses variables
5.4.3
Create an algorithm that uses variables
6.1.1
Choose an effective medium for communicating a solution to a target audience
6.1.2
Create an original computational artifact to communicate a solution to a target audience
6.2.1
Interpret a design for a computational artifact
6.2.2
Critique and provide feedback on a design for a computational artifact
6.2.3
Incorporate collaborative feedback into a computational artifact
  1. 4.2 Final Project
6.3.1
Create a prototype to evaluate the effectiveness of an automated solution
6.3.2
Compare the efficiency of multiple possible solutions
6.3.3
Troubleshoot an automated solution
6.3.4
Use iterative testing to improve an automated solution