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Standards Mapping

for Arkansas Computing with Data

61

Standards in this Framework

30

Standards Mapped

49%

Mapped to Course

Standard Lessons
1.1.1
Demonstrate effective communication skills in both technical and non-technical contexts (e.g., explaining data analysis results to peers, presenting insights to non-technical stakeholders).
  1. 4.1 Data Storytelling
  2. 4.2 Final Project
1.1.2
Demonstrate integrity in data science practices (e.g., citing data sources, ensuring data privacy and security).
  1. 3.1 Data Privacy
  2. 3.3 Responsible Data Science
1.1.3
Develop collaboration and teamwork skills through group data projects (e.g., pair programming, code reviews, collaborative data analysis).
  1. 1.2 The Data Science Life Cycle
  2. 4.2 Final Project
1.1.4
Identify and develop traits important for success in data science (e.g., problem-solving, attention to detail, continuous learning, and adaptability).
  1. 1.1 What is Data Science?
  2. 2.2 Data Cleaning
  3. 2.3 Sort and Filter
  4. 2.6 Statistical Measures
1.2.1
Research various roles within the data science field (e.g., data analyst, machine learning engineer, data engineer, business intelligence analyst).
  1. 1.1 What is Data Science?
  2. 5.1 What's Next?
1.2.2
Identify professional certifications relevant to different data science careers (e.g., Google Data Analytics Professional Certificate, IBM Data Science Professional Certificate).
1.2.3
Research routes to become a data scientist (e.g., university degree, bootcamps, online courses, internships in data-driven industries).
  1. 5.1 What's Next?
2.1.1
Explain the core concepts of data science (e.g., data collection, cleaning, analysis, and visualization).
  1. 1.1 What is Data Science?
  2. 1.2 The Data Science Life Cycle
  3. 2.1 Spreadsheet Basics
  4. 2.2 Data Cleaning
  5. 2.3 Sort and Filter
  6. 2.4 Data Visualizations
  7. 2.6 Statistical Measures
2.1.2
Describe real-world applications of data science in various fields (e.g., healthcare, finance, marketing, environmental science).
  1. 1.1 What is Data Science?
  2. 1.2 The Data Science Life Cycle
  3. 2.4 Data Visualizations
  4. 4.1 Data Storytelling
  5. 4.2 Final Project
2.2.1
Discuss the ethical considerations involved in data collection, storage, and use (e.g., data privacy, data security, informed consent).
  1. 3.1 Data Privacy
  2. 3.3 Responsible Data Science
2.2.2
Evaluate data sources for potential biases (e.g., selection, measurement, confirmation, and reporting) and identify strategies to mitigate their impact.
  1. 3.1 Data Privacy
  2. 3.3 Responsible Data Science
2.2.3
Explain the importance of peer-reviewed and replicated experimental data.
2.2.4
Evaluate the credibility of data sources (e.g., author expertise, source reputation, publication date, and objectivity).
  1. 3.1 Data Privacy
  2. 3.3 Responsible Data Science
2.3.1
Differentiate between quantitative (e.g., continuous and discrete) and qualitative (e.g., nominal and ordinal) data.
  1. 1.1 What is Data Science?
  2. 1.2 The Data Science Life Cycle
  3. 2.2 Data Cleaning
2.3.2
Recognize and classify different types of data (e.g., nominal, ordinal, interval, ratio).
2.3.3
Explain the concept and qualities of structured data (e.g., tabular data, JSON, csv).
3.1.1
Enter and format data within spreadsheets for business purposes using spreadsheet software.
3.1.2
Use formulas and functions (e.g., AVG, MIN, MAX, COUNT, and SUM) within a spreadsheet software to use logical reasoning to draw conclusions and apply mathematical problem-solving skills to create suitable formulas to solve problems.
3.1.3
Use spreadsheet software to aid with data analysis and reporting by creating visualizations (e.g., charts).
  1. 2.4 Data Visualizations
  2. 4.1 Data Storytelling
3.1.4
Use advanced functions (e.g., VLOOKUP, COUNTIF, and IFERROR), conditional formatting (e.g., IF, AND, OR, and BETWEEN), and filtering techniques to analyze and manipulate data using a spreadsheet software.
3.1.5
Use a text-based programming language to create and read simple spreadsheet files (e.g., csv) using file input and output operations.
3.1.6
Create programs using a text-based programming language that solve common business data operations (e.g., average, sum, count, max, min) in analyzing data in a spreadsheet.
3.2.1
Compare various types of databases (e.g., relational, NoSQL, and hierarchical).
3.2.2
Research different database management systems (e.g, MySQL, SQlite, Microsoft SQL Server, Access, and Oracle Database) to manage and interact with a database.
3.2.3
Use a database management system to create basic databases and perform basic operations (e.g., selecting, inserting, updating, and deleting data) on the database.
3.2.4
Use a text-based programming language to create basic databases and tables and perform basic operations (e.g., selecting, inserting, updating, and deleting data) on the database.
3.2.5
Create and execute statements using a declarative language (e.g., Structured Query Language) to create simple databases and perform basic operations (e.g., selecting, inserting, updating, and deleting data) on the database.
3.2.6
Create programs using a text-based programming language to transfer data from a data source (e.g., csv, spreadsheets, and online data repositories) into a database.
4.1.1
Discuss various sampling techniques (e.g., random sampling, stratified sampling, cluster sampling).
4.1.2
Develop and implement a text-based algorithm (e.g., random number generator) for sampling from a population to ensure data quality.
4.1.3
Compute the number of events in a sample space using combinatorics (i.e, fundamental principle of counting, permutations, and combinations).
4.1.4
Define probability in both language and mathematics using terms of related vocabulary (e.g., trial, sample space , event, outcome, complement).
4.1.5
Develop and implement simple simulations (e.g., coin flips, dice rolls, card draws) using a programming language to generate and analyze simulated data.
4.1.6
Leverage web scraping libraries (e.g., Beautiful Soup, Selenium) to retrieve data from the Internet.
4.1.7
Create programs that leverage libraries (e.g., Requests, urllib) to connect and retrieve data from public APIs (e.g., NOAA climate data, NASA Open APIs).
4.2.1
Assess data quality for consistency, completeness, and accuracy.
  1. 2.2 Data Cleaning
4.2.2
Identify and handle missing values (e.g., imputation techniques, deletion).
  1. 2.2 Data Cleaning
4.2.3
Identify outliers using statistical methods (e.g., z-score, interquartile range).
4.2.4
Perform various methods of addressing outliers (e.g., removal, transformation, imputation).
4.2.5
Normalize and standardize data (e.g., min-max scaling, z-score normalization, decimal scaling).
4.3.1
Manipulate existing data to create relevant features including combining columns, extracting information from text, and datetime transformations.
  1. 2.3 Sort and Filter
4.3.2
Aggregate data and convert into different levels of granularity (e.g., daily to monthly, individual to group).
  1. 2.3 Sort and Filter
  2. 2.5 Pivot Tables
5.1.1
Calculate and interpret descriptive statistics (i.e., mean, median, mode, standard deviation, percentiles).
  1. 2.6 Statistical Measures
5.1.2
Analyze the distribution of data using visualizations (e.g., histograms, scatter plots, box plots, box and whisker plots).
  1. 2.4 Data Visualizations
  2. 4.1 Data Storytelling
5.1.3
Assess the linear relationship between variables using correlation analysis.
5.1.4
Perform and interpret simple linear regression analysis.
5.1.5
Calculate various probabilities (i.e., simple events, compound events, using Addition Rule, using Multiplication Rule, and conditional).
5.1.6
Discuss and apply the concepts of related events (e.g., independent and dependent events, with and without replacement, mutually exclusive).
5.2.1
Leverage data visualization libraries (e.g., Matplotlib, Seaborn) 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).
5.2.2
Interpret insights from visualizations to identify trends, outliers or patterns in the data.
  1. 2.4 Data Visualizations
  2. 4.1 Data Storytelling
5.2.3
Create related visualizations depicting probability (e.g., Venn Diagram, Two-Way Table).
6.1.1
Discuss the importance of visualizations to communicate important information to various audiences.
  1. 2.4 Data Visualizations
  2. 4.1 Data Storytelling
  3. 4.2 Final Project
6.1.2
Leverage data visualization tools and libraries (e.g., Seaborn, Plotly, Basemap) to create infographics that support key findings and insights.
  1. 2.4 Data Visualizations
6.2.1
Create an effective narrative from data analysis.
  1. 4.1 Data Storytelling
6.2.2
Communicate the relevance of findings using basic storytelling techniques (e.g., using analogies, providing context, highlighting key trends).
  1. 4.1 Data Storytelling
  2. 4.2 Final Project
6.2.3
Identify and communicate potential limitations and uncertainties in the data analysis.
  1. 3.1 Data Privacy
  2. 3.3 Responsible Data Science
  3. 4.1 Data Storytelling
6.3.1
Identify a computational problem (e.g., prediction, classification, or regression of data from a scientific, industry-focused, or logistics data set) and create a plan for a student-centered project.
  1. 1.2 The Data Science Life Cycle
  2. 4.1 Data Storytelling
  3. 4.2 Final Project
6.3.2
Acquire relevant data (e.g., sampling, sensor data, online datasets, web scraping) using appropriate methods and manage it effectively as part of a student-centered project.
  1. 2.2 Data Cleaning
  2. 2.3 Sort and Filter
  3. 2.4 Data Visualizations
  4. 2.6 Statistical Measures
6.3.3
Identify and address data quality issues to prepare data as part of a student-centered project.
  1. 2.2 Data Cleaning
  2. 2.3 Sort and Filter
  3. 2.6 Statistical Measures
  4. 3.1 Data Privacy
  5. 3.3 Responsible Data Science
6.3.4
Extract meaningful insights from the data using statistical methods and visualization techniques as part of a student-centered project.
  1. 2.3 Sort and Filter
  2. 2.4 Data Visualizations
  3. 2.6 Statistical Measures
6.3.5
Communicate clearly and effectively through presentations and written reports to both technical and non-technical audiences at the conclusion of a student-centered project.
  1. 4.1 Data Storytelling
  2. 4.2 Final Project