Standards Mapping

1.1.1

Demonstrate understanding of various career paths in computer science (e.g., software development, data science, cybersecurity, networking, robotics, computer engineering, and artificial intelligence) and their roles in different industries.

• 7.1 What is Data Science?
• 18.1 What is Data Science?

1.1.2

Practice professional communication skills through various activities (e.g., mock interviews, technical presentations, and collaborative coding projects) to prepare for real-world scenarios in computer science careers.

• 8.3 Responsible Data Science
• 8.4 Data Storytelling
• 8.5 Final Project
• 15.2 Resume and Interview Prep

1.1.3

Describe the importance of ethical considerations in computer science (e.g., data privacy, algorithmic bias, and social impact of technology) and how they apply to professional practice.

• 8.1 Data Privacy
• 8.2 Big Data and Bias
• 8.3 Responsible Data Science
• 9.1 Cyber Ethics and Laws

1.1.4

Evaluate the concept and importance of a professional online presence in computer science careers (e.g., portfolio websites, professional networking platforms, open-source contributions) and explore ways to showcase skills and projects.

• 15.1 Student Organizations

1.1.5

Identify key elements of a professional portfolio and explain the purpose of a portfolio (e.g., showcasing skills, documenting growth, and supporting college applications or job searches).

• 15.3 Portfolios

2.1.1

Recognize situations where leveraging computational approaches (e.g., data analysis, automation, or simulations) would be beneficial for solving real-world problems.

• 7.1 What is Data Science?
• 7.8 Pivot Tables
• 7.9 Statistical Measures
• 8.4 Data Storytelling
• 8.5 Final Project
• 10.7 Impact of the Internet
• 18.1 What is Data Science?

2.1.2

Apply the core computational thinking principles‚ abstraction (focusing on essential elements), decomposition (breaking problems into manageable parts), algorithm development (creating step-by-step solutions), and pattern recognition (identifying recurring elements), to designing effective problem-solving strategies.

• 3.4 Nested Control Structures
• 4.1 Functions
• 4.2 Functions and Parameters
• 4.4 Functions and Return Values
• 5.3 For Loops and Lists
• 16.10 Top Down Design
• 17.5 Top Down Design and Decomposition in Karel
• 18.3 Exploring Data Using Python

2.2.1

Use various data types (e.g., Booleans, characters, integers, floating points, and strings) appropriately within a program.

• 1.2 Variables and Types
• 1.3 User Input
• 1.4 Mathematical Operators
• 1.6 String Operators
• 2.5 Floating Point Numbers and Rounding
• 18.3 Exploring Data Using Python

2.2.2

Create and use variables to store and manage data within a program.

• 1.2 Variables and Types
• 18.3 Exploring Data Using Python

2.2.3

Construct expressions using arithmetic operators (e.g., +, -, *, /, and %) and numeric data types to perform calculations within a program.

• 1.4 Mathematical Operators
• 18.3 Exploring Data Using Python

2.2.4

Convert between different data types when necessary within a program (e.g., casting a string into an integer).

• 1.3 User Input
• 18.5 Series and Central Tendency
• 18.6 Measures of Spread

2.2.5

Perform operations that encode and decode data from one form into another form (e.g., binary to hexadecimal, numeric values to colored pixels, or numbers to ASCII/Unicode representations).

• 13.2 Bits, Text, and Numbers
• 13.3 Encoding Images in Binary

2.2.6

Implement data structures (e.g., arrays, lists, sets, and maps) to organize, store, manipulate, and perform operations on collections of data within a program.

• 5.2 Lists
• 5.3 For Loops and Lists
• 5.4 List Methods
• 18.3 Exploring Data Using Python
• 18.7 Pandas DataFrames

2.3.1

Analyze a program in terms of steps of execution and expected outcomes (e.g., storyboards, flowcharts, and pseudocode).

• 5.3 For Loops and Lists
• 8.4 Data Storytelling
• 17.14 Debugging Strategies
• 18.10 Mini-Project: Findings

2.3.2

Construct Boolean expressions using relational operators (e.g., <, >, <=, >=, ==, and !=) within a program.

• 2.3 Comparison Operators

2.3.3

Construct Boolean expressions using logical operators (e.g, AND, OR, and NOT) within a program.

• 2.4 Logical Operators

2.3.4

Create programs that implement selection control structures (e.g., if statements and switch statements) to make decisions and execute different code paths based on conditions.

• 2.2 If Statements

2.3.5

Create programs that implement iteration control structures (e.g., while loops and for loops) to repeat code blocks a specific number of times or until a condition is met.

• 3.1 While Loops
• 3.2 For Loops
• 18.9 Using Functions

2.3.6

Create subroutines (e.g., procedures and functions) to modularize code for reusability and organization within a program.

• 4.1 Functions
• 4.2 Functions and Parameters
• 4.4 Functions and Return Values
• 18.9 Using Functions

2.3.7

Debug errors (e.g., syntax, runtime, and logic) within a program to ensure program functionality.

• 1.1 Printing in Python
• 1.2 Variables and Types
• 1.6 String Operators
• 17.14 Debugging Strategies
• 19.3 Data Cleaning

2.4.1

Use the console for basic data input and output operations within a program.

• 1.1 Printing in Python
• 1.3 User Input
• 18.3 Exploring Data Using Python

2.4.2

Explain the structure and purpose of different file types (e.g., txt, csv, bmp, and json) used for data storage.

• 6.1 What is File I/O
• 7.3 Spreadsheet Basics
• 12.3 Compatibility

2.4.3

Develop programs that perform file operations including reading data from, writing data to, and appending data to files.

• 6.1 What is File I/O
• 6.3 Reading Line from File
• 6.5 Writing to File

2.5.1

Implement consistent formatting and naming conventions within the code (e.g., indentation, spacing, variable names) to improve code readability and maintainability.

• 1.2 Variables and Types
• 1.7 Comments
• 2.2 If Statements
• 3.1 While Loops
• 3.2 For Loops
• 4.1 Functions
• 17.6 Commenting Your Code
• 18.3 Exploring Data Using Python

2.5.2

Craft clear and concise comments within the code to explain the purpose of different code sections, algorithms used, and non-obvious logic.

• 1.7 Comments
• 17.6 Commenting Your Code
• 18.4 Modules, Packages & Libraries

3.1.1

Categorize data into different types (e.g., quantitative - continuous and discrete, qualitative - nominal and ordinal) and understand the distinction between them.

• 7.1 What is Data Science?
• 18.1 What is Data Science?
• 18.6 Measures of Spread

3.1.2

Identify potential sources of data (e.g., sensors, surveys, databases, and web scraping) based on the type of data needed.

• 8.3 Responsible Data Science
• 8.5 Final Project
• 18.2 Gathering Data

3.1.3

Explain the advantages and disadvantages of different data collection methods (e.g., surveys for user opinions, experiments for cause-effect relationships, and observational studies for naturalistic data) considering factors like accuracy, cost, and time.

• 8.1 Data Privacy
• 8.2 Big Data and Bias
• 8.3 Responsible Data Science
• 18.2 Gathering Data

3.1.4

Design basic data collection methods (e.g., short surveys, observation checklists, and simple experiments) appropriate for a specific purpose.

• 7.2 The Data Science Life Cycle
• 8.2 Big Data and Bias
• 8.3 Responsible Data Science
• 8.4 Data Storytelling
• 8.5 Final Project

3.2.1

Identify and address data quality issues (e.g., missing values, inconsistencies, and outliers) to ensure the data is suitable for analysis.

• 7.4 Data Cleaning
• 19.3 Data Cleaning

3.2.2

Analyze data sets utilizing appropriate descriptive statistics (e.g., mean, median, quartiles, and range) and visualizations (e.g., histograms and box plots).

• 7.6 Data Visualizations
• 7.8 Pivot Tables
• 7.9 Statistical Measures
• 18.5 Series and Central Tendency
• 18.6 Measures of Spread
• 19.7 Explore Univariate Data

3.2.3

Visually inspect and use exploratory analysis techniques to discern patterns, trends, and relationships within the data.

• 7.6 Data Visualizations
• 7.8 Pivot Tables
• 7.9 Statistical Measures
• 19.7 Explore Univariate Data
• 19.8 Trends and Correlations
• 19.9 Linear Regression
• 19.10 Explore Bivariate Data

3.2.4

Develop programs that perform data analysis techniques (e.g., finding correlations and comparison of means) appropriate for the data and question at hand.

• 7.7 Correlations
• 7.8 Pivot Tables
• 7.9 Statistical Measures
• 19.8 Trends and Correlations
• 19.10 Explore Bivariate Data

3.2.5

Create programs to generate suitable visualizations (e.g., bar charts for comparisons, line charts for trends, scatter plots for relationships, and pie charts for part-to-whole) for the data and question at hand.

• 7.6 Data Visualizations
• 7.7 Correlations
• 8.4 Data Storytelling
• 8.5 Final Project
• 19.5 Line and Bar Charts
• 19.7 Explore Univariate Data
• 19.10 Explore Bivariate Data

3.3.1

Recognize and explain how potential biases (e.g., confirmation, selection, and reporting) within a data source could influence the analysis and resulting insights.

• 8.1 Data Privacy
• 8.2 Big Data and Bias
• 8.3 Responsible Data Science

3.3.2

Develop clear and concise narratives that effectively communicate data insights to diverse audiences, leveraging storytelling techniques to enhance understanding and engagement.

• 8.4 Data Storytelling
• 19.11 Telling Your Story

3.3.3

Demonstrate a clear understanding of the difference between correlation and causation, ensuring conclusions accurately reflect the relationships identified in the data analysis.

• 7.2 The Data Science Life Cycle
• 7.6 Data Visualizations
• 7.7 Correlations
• 7.8 Pivot Tables
• 7.9 Statistical Measures
• 19.8 Trends and Correlations
• 19.9 Linear Regression

3.3.4

Develop sound inferences from the data to support informed decision-making, avoiding overstated or misleading implications based on the findings.

• 7.2 The Data Science Life Cycle
• 7.3 Spreadsheet Basics
• 7.4 Data Cleaning
• 7.5 Sort and Filter
• 7.6 Data Visualizations
• 7.8 Pivot Tables
• 7.9 Statistical Measures
• 19.11 Telling Your Story

3.3.5

Present both positive and negative findings in a comprehensive and unbiased way, ensuring accurate data representation for the audience.

• 8.2 Big Data and Bias
• 8.3 Responsible Data Science
• 8.5 Final Project
• 19.11 Telling Your Story

4.1.1

Define artificial intelligence and the major categories of AI (e.g., narrow AI, general AI, and superintelligence).

• 10.2 Human & Artificial Intelligence
• 10.3 Generative vs. Predictive AI

4.1.2

Evaluate the ethical challenges inherent in AI systems and formulate principles for its responsible use.

• 10.4 Project: AI in My Life
• 11.4 Ethics and AI
• 11.5 Project: Ethics Case Study

4.1.3

Describe how AI models are trained and the role data plays in the training process.

• 11.1 How Are AI Models Trained?
• 11.3 Bias in Training

4.1.4

Explain how AI impacts various industries (e.g., healthcare, finance, transportation, and manufacturing) and everyday life (e.g., navigation, content recommendation, and communication tools).

• 10.4 Project: AI in My Life
• 11.2 AI Models in Industry

4.2.1

Investigate emerging technologies (e.g., quantum computing, edge computing, extended reality, and blockchain) and their potential future impact on computer science and society.

• 10.5 Cloud Computing
• 10.6 Edge Computing
• 10.8 Blockchain Technology

4.2.2

Explain cloud computing, its service models (e.g., Infrastructure as a Service, Platform as a Service, and Software as a Service), and discuss how it has transformed the IT landscape, including its advantages and challenges.

• 10.5 Cloud Computing

4.2.3

Define the Internet of Things (IoT), recognize its real-world applications, and discuss its impact on data generation, automation, and interconnectedness.

• 9.3 Cybersecurity Essentials

4.2.4

Explain the concept of big data, its defining characteristics, and its impact on data analysis, decision-making, and various industries.

• 8.2 Big Data and Bias

5.1.1

Analyze the core principles for establishing comprehensive information security strategies (e.g., confidentiality, integrity, availability, authentication, and non-repudiation) when using data and information systems.

• 9.3 Cybersecurity Essentials

5.1.2

Describe common cyber threats (e.g., malware, phishing, and ransomware) and how these threats can compromise data and information systems.

• 9.4 Common Cyber Attacks and Prevention

5.1.3

Describe essential cybersecurity practices (e.g., data encryption, secure coding, and user authentication) and technologies (e.g., firewalls and multi-factor authentication) implemented to safeguard information.

• 9.4 Common Cyber Attacks and Prevention
• 12.6 Application Security
• 14.5 Network Options

5.1.4

Analyze the impact of best practices (e.g., strong passwords and security awareness training) in maintaining a secure digital environment.

• 9.2 Personal Data Security

5.2.1

Identify and explain the purpose of key hardware components (e.g., CPU, RAM, storage, and I/O devices) within various computing platforms (e.g., desktops, laptops, and mobile devices) and how they work together to enable the overall system operation.

• 14.1 Internal Components
• 14.2 Peripheral Devices

5.2.2

Define software and distinguish between system software (e.g., operating systems, device drivers) and application software (e.g., word processors, web browsers, games).

• 12.1 Operating Systems
• 12.4 Software and Applications

5.2.3

Explain the basic functions of an operating system (e.g., managing hardware resources, providing user interface, handling file systems) and its importance in computer operations.

• 12.1 Operating Systems
• 12.2 Comparing Operating Systems

5.2.4

Summarize the concept of open-source software, its benefits, and its impact on software development and distribution.

• 12.5 Software Licenses

5.2.5

Explain the importance of software updates and patches in maintaining security and improving functionality.

• 12.6 Application Security

5.3.1

Define and explain the significance of common network concepts (e.g., IP address, topology, protocols, bandwidth, and latency).

• 13.4 What is the Internet?
• 13.5 IP Addresses
• 13.6 Routing and Packets
• 13.7 How Do Websites Work?
• 13.8 OSI Model

5.3.2

Identify and describe the functionalities of common network hardware (e.g., modems, routers, switches, firewalls, and servers) used in networks.

• 14.4 Network Devices

5.3.3

Explain how basic network security principles (e.g., access control, encryption, and firewalls) are used to safeguard networks.

• 14.4 Network Devices
• 14.5 Network Options

5.3.4

Analyze the relationship between computer systems, software, and networks in creating interconnected computing environments.

• 14.5 Network Options