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Standards Mapping

for Arkansas Introduction to Computer Science

60

Standards in this Framework

53

Standards Mapped

88%

Mapped to Course

Standard Lessons
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.
  1. Data Science with Python
  2. 1.1 What is Data Science?
  3. Fundamentals of Cybersecurity
  4. 7.1 Communication is Key!
  5. 10.1 After Fundamentals of Cybersecurity
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.
  1. Data Science with Python
  2. 2.1 Data Science for Change
  3. Fundamentals of Cybersecurity
  4. 7.1 Communication is Key!
  5. 7.3 Building a Knowledge Base
  6. 7.4 Star in a Video!
  7. 16.3 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.
  1. Data Science with Python
  2. 2.2 Big Data and Bias
  3. Fundamentals of Cybersecurity
  4. 1.4 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.
  1. Fundamentals of Cybersecurity
  2. 16.1 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).
2.1.1
Recognize situations where leveraging computational approaches (e.g., data analysis, automation, or simulations) would be beneficial for solving real-world problems.
  1. Data Science with Python
  2. 1.1 What is Data Science?
  3. 2.1 Data Science for Change
  4. Fundamentals of Cybersecurity
  5. 5.9 Impact of the Internet
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.
  1. Data Science with Python
  2. 1.3 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. Data Science with Python
  2. 1.3 Exploring Data Using Python
2.2.2
Create and use variables to store and manage data within a program.
  1. Data Science with Python
  2. 1.3 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. Data Science with Python
  2. 1.3 Exploring Data Using Python
  3. 7.4 Mathematical Operators
2.2.4
Convert between different data types when necessary within a program (e.g., casting a string into an integer).
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).
  1. Fundamentals of Cybersecurity
  2. 5.2 Bits, Text, and Numbers
  3. 5.3 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.
  1. Data Science with Python
  2. 1.7 Pandas DataFrames
2.3.1
Analyze a program in terms of steps of execution and expected outcomes (e.g., storyboards, flowcharts, and pseudocode).
  1. Data Science with Python
  2. 1.10 Mini-Project: Findings
2.3.2
Construct Boolean expressions using relational operators (e.g., <, >, <=, >=, ==, and !=) within a program.
  1. Data Science with Python
  2. 2.4 Conditional Filtering
  3. 7.8 Comparison Operators
2.3.3
Construct Boolean expressions using logical operators (e.g, AND, OR, and NOT) within a program.
  1. Data Science with Python
  2. 2.4 Conditional Filtering
  3. 7.9 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.
  1. Data Science with Python
  2. 2.4 Conditional Filtering
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.
  1. Data Science with Python
  2. 1.9 Using Functions
2.3.6
Create subroutines (e.g., procedures and functions) to modularize code for reusability and organization within a program.
  1. Data Science with Python
  2. 1.9 Using Functions
  3. 7.14 Functions
2.3.7
Debug errors (e.g., syntax, runtime, and logic) within a program to ensure program functionality.
  1. Data Science with Python
  2. 2.5 Data Cleaning
2.4.1
Use the console for basic data input and output operations within a program.
  1. Data Science with Python
  2. 1.3 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.
  1. Data Science with Python
  2. 2.3 Importing and Filtering Data
  3. Fundamentals of Cybersecurity
  4. 4.4 Compatibility
2.4.3
Develop programs that perform file operations including reading data from, writing data to, and appending data to files.
  1. Data Science with Python
  2. 2.3 Importing and Filtering Data
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. Data Science with Python
  2. 1.3 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. Data Science with Python
  2. 1.4 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.
  1. Data Science with Python
  2. 1.6 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.
  1. Data Science with Python
  2. 1.2 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.
  1. Data Science with Python
  2. 1.2 Gathering Data
  3. 2.2 Big Data and Bias
3.1.4
Design basic data collection methods (e.g., short surveys, observation checklists, and simple experiments) appropriate for a specific purpose.
  1. Data Science with Python
  2. 4.5 Your Business Data
3.2.1
Identify and address data quality issues (e.g., missing values, inconsistencies, and outliers) to ensure the data is suitable for analysis.
  1. Data Science with Python
  2. 2.5 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).
  1. Data Science with Python
  2. 1.5 Series and Central Tendency
3.2.3
Visually inspect and use exploratory analysis techniques to discern patterns, trends, and relationships within the data.
  1. Data Science with Python
  2. 3.7 Trends and Correlations
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.
  1. Data Science with Python
  2. 3.9 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.
  1. Data Science with Python
  2. 3.4 Line and Bar Charts
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.
  1. Data Science with Python
  2. 2.2 Big Data and Bias
  3. 4.6 Bias in Data Analytics
3.3.2
Develop clear and concise narratives that effectively communicate data insights to diverse audiences, leveraging storytelling techniques to enhance understanding and engagement.
  1. Data Science with Python
  2. 3.10 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.
  1. Data Science with Python
  2. 3.8 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.
  1. Data Science with Python
  2. 3.10 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.
  1. Data Science with Python
  2. 2.2 Big Data and Bias
  3. 4.7 Business Report
4.1.1
Define artificial intelligence (AI), identify its key subfields, and explain its benefits and potential drawbacks in real-world applications.
4.1.2
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.
4.1.3
Define the Internet of Things (IoT), recognize its real-world applications, and discuss its impact on data generation, automation, and interconnectedness.
  1. Fundamentals of Cybersecurity
  2. 1.6 Cybersecurity Essentials
4.1.4
Explain the concept of big data, its defining characteristics, and its impact on data analysis, decision-making, and various industries.
  1. Data Science with Python
  2. 2.2 Big Data and Bias
4.2.1
Define quantum computing, explaining its basic principles and potential to revolutionize computing power and cryptography.
  1. Fundamentals of Cybersecurity
  2. 2.1 Cryptography: Then, Now, and Future
4.2.2
Describe edge computing and its role in processing data closer to the source, discussing its advantages in reducing latency and enhancing privacy.
4.2.3
Explain the concept of extended reality (e.g., virtual reality, augmented reality, and mixed reality) and its potential applications across various industries.
4.2.4
Describe the capabilities of blockchain technology and assess its potential uses across different sectors (e.g., supply chain management, identity verification systems, and decentralized financial services).
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.
  1. Fundamentals of Cybersecurity
  2. 1.6 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.
  1. Fundamentals of Cybersecurity
  2. 1.7 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.
  1. Fundamentals of Cybersecurity
  2. 1.7 Common Cyber Attacks and Prevention
  3. 4.8 Application Security
  4. 8.3 Security in Coding
5.1.4
Analyze the impact of best practices (e.g., strong passwords and security awareness training) in maintaining a secure digital environment.
  1. Fundamentals of Cybersecurity
  2. 1.5 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.
  1. Fundamentals of Cybersecurity
  2. 6.1 Internal Components
  3. 6.3 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).
  1. Fundamentals of Cybersecurity
  2. 4.1 Operating Systems
  3. 4.6 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.
  1. Fundamentals of Cybersecurity
  2. 4.1 Operating Systems
  3. 4.3 Comparing Operating Systems
5.2.4
Summarize the concept of open-source software, its benefits, and its impact on software development and distribution.
  1. Fundamentals of Cybersecurity
  2. 4.7 Software Licenses
5.2.5
Explain the importance of software updates and patches in maintaining security and improving functionality.
  1. Fundamentals of Cybersecurity
  2. 4.8 Application Security
5.3.1
Define and explain the significance of common network concepts (e.g., IP address, topology, protocols, bandwidth, and latency).
  1. Fundamentals of Cybersecurity
  2. 5.4 What is the Internet?
  3. 5.5 IP Addresses
  4. 5.6 Routing and Packets
  5. 5.7 How Do Websites Work?
  6. 5.8 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.
  1. Fundamentals of Cybersecurity
  2. 6.5 Network Devices
5.3.3
Explain how basic network security principles (e.g., access control, encryption, and firewalls) are used to safeguard networks.
  1. Fundamentals of Cybersecurity
  2. 6.5 Network Devices
  3. 6.7 Network Options
  4. 6.10 Network Communication
  5. 6.11 Network Management
5.3.4
Analyze the relationship between computer systems, software, and networks in creating interconnected computing environments.
  1. Fundamentals of Cybersecurity
  2. 6.7 Network Options