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Standards Mapping

for Certiport Computational Thinking

61

Standards in this Framework

39

Standards Mapped

63%

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. 1.3 Exploring Data Using Python
  2. 1.4 Modules, Packages & Libraries
  3. 1.5 Series and Central Tendency
  4. 1.6 Measures of Spread
  5. 2.3 Importing and Filtering Data
1.1.3
Understand and recognize data encoding (ascii, binary, character mapping)
1.2.1
Recognize and apply Boolean and logical operators
  1. 7.9 Logical Operators
1.2.2
Recognize and apply inductive reasoning
  1. 7.9 Logical Operators
1.2.3
Recognize ambiguity in a logical reasoning problem
1.2.4
Recognize and apply deductive reasoning
  1. 7.8 Comparison Operators
  2. 7.9 Logical Operators
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. 4.2 Quality Datasets
  2. 4.3 Aggregating Data
  3. 4.4 Combining Datasets
2.1.2
Assess relevance of existing data sets
  1. 4.2 Quality Datasets
  2. 4.3 Aggregating Data
  3. 4.4 Combining Datasets
2.1.3
Determine the gap between existing data and data needs
  1. 4.2 Quality Datasets
  2. 4.3 Aggregating Data
  3. 4.4 Combining Datasets
2.2.1
Understand validity
2.2.2
Understand reliability
2.2.3
Explain data cleaning in data sets
  1. 2.5 Data Cleaning
2.3.1
Collect relevant data using existing data sources
  1. 4.2 Quality Datasets
  2. 4.3 Aggregating Data
  3. 4.4 Combining Datasets
2.3.2
Select appropriate tools to gather, analyze, and process data
  1. 4.2 Quality Datasets
  2. 4.3 Aggregating Data
  3. 4.4 Combining Datasets
2.3.3
Retrieve information from a data source, such as a list, a table, an infographic, etc.
  1. 4.2 Quality Datasets
  2. 4.3 Aggregating Data
  3. 4.4 Combining Datasets
2.3.4
Choose a method for creating original data sets such as an observation or a survey
  1. 4.4 Combining Datasets
2.3.5
Use input-validation methods
2.3.6
Explain the legal and ethical dimensions of data collection
  1. 2.2 Big Data and Bias
3.1.1
Identify patterns in data
  1. 3.7 Trends and Correlations
  2. 3.10 Telling Your Story
  3. 4.7 Business Report
3.1.2
Organize data using models such as tables, charts, and graphs
  1. 2.7 Interpret and Present
  2. 3.3 Data Visualizations
  3. 4.7 Business Report
3.1.3
Sort and filter data by relevant criteria
  1. 2.3 Importing and Filtering Data
  2. 2.4 Conditional Filtering
3.1.4
Identify similarities, differences, and subsets in a data set
  1. 4.2 Quality Datasets
  2. 4.3 Aggregating Data
  3. 4.4 Combining Datasets
3.1.5
Make predictions by examining patterns
  1. 2.7 Interpret and Present
  2. 4.7 Business Report
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
  1. 4.7 Business Report
3.2.3
Interpret a process flow diagram
4.1.1
Identify an appropriate problem statement based on information provided
  1. 2.1 Data Science for Change
  2. 2.7 Interpret and Present
  3. 4.1 Data Science for Business
  4. 4.7 Business Report
4.1.2
Define the scope and limitations of a problem
  1. 2.1 Data Science for Change
  2. 2.7 Interpret and Present
  3. 4.1 Data Science for Business
  4. 4.7 Business Report
4.1.3
Identify decision makers, collaborators, and target audience
  1. 2.1 Data Science for Change
  2. 2.7 Interpret and Present
  3. 4.1 Data Science for Business
  4. 4.7 Business Report
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
  1. 7.11 For Loops
  2. 7.12 Break and Continue
4.2.2
Identify prerequisites for a solution
  1. 1.1 What is Data Science?
  2. 1.2 Gathering Data
4.2.3
Identify the possible outcomes of a solution
  1. 2.1 Data Science for Change
  2. 2.7 Interpret and Present
  3. 4.1 Data Science for Business
  4. 4.7 Business Report
4.2.4
Choose appropriate tools to develop a solution, such as flow charts, spreadsheets, pseudocode, surveys
  1. 2.1 Data Science for Change
  2. 2.7 Interpret and Present
  3. 4.1 Data Science for Business
  4. 4.7 Business Report
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
  1. 7.14 Functions
5.2.1
Recognize when to use iteration
  1. 7.11 For Loops
  2. 7.12 Break and Continue
5.2.2
Recognize when to use nested loops
5.2.3
Determine the outcome of an algorithm that uses iteration
  1. 7.11 For Loops
  2. 7.12 Break and Continue
5.2.4
Create an algorithm that uses iteration
  1. 7.11 For Loops
  2. 7.12 Break and Continue
5.3.1
Recognize when to use selection statements
  1. 7.8 Comparison Operators
  2. 7.9 Logical Operators
5.3.2
Recognize when to use nesting in selection statements
5.3.3
Determine the outcome of an algorithm that uses selection statements
  1. 7.8 Comparison Operators
  2. 7.9 Logical Operators
5.3.4
Create an algorithm that uses selection statements
  1. 2.4 Conditional Filtering
  2. 7.8 Comparison Operators
  3. 7.9 Logical Operators
5.4.1
Recognize when to use variables
  1. 1.3 Exploring Data Using Python
  2. 1.7 Pandas DataFrames
  3. 1.8 Selecting Columns
5.4.2
Determine the outcome of an algorithm that uses variables
  1. 1.3 Exploring Data Using Python
  2. 1.7 Pandas DataFrames
  3. 1.8 Selecting Columns
5.4.3
Create an algorithm that uses variables
  1. 1.3 Exploring Data Using Python
  2. 1.7 Pandas DataFrames
  3. 1.8 Selecting Columns
6.1.1
Choose an effective medium for communicating a solution to a target audience
  1. 2.7 Interpret and Present
  2. 3.3 Data Visualizations
  3. 3.10 Telling Your Story
  4. 4.7 Business Report
6.1.2
Create an original computational artifact to communicate a solution to a target audience
  1. 1.10 Mini-Project: Findings
  2. 2.7 Interpret and Present
  3. 4.7 Business Report
6.2.1
Interpret a design for a computational artifact
  1. 1.10 Mini-Project: Findings
  2. 2.7 Interpret and Present
  3. 4.7 Business Report
6.2.2
Critique and provide feedback on a design for a computational artifact
6.2.3
Incorporate collaborative feedback into a computational artifact
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