Common Planner
K–12 Standards

Data Science II (11.08200) (2021)

Other Georgia CTAE sets

Demonstrate how Data Science can be used to impact school, work, and leisure timeDS2-1

  • 1.

    Demonstrates how students currently use data science in their lives, and how data science impacts their lives and possible career choicesDS2-1.1

  • 2.

    Identify and differentiate between different data governance standards and argue why governance is important.DS2-1.2

  • 3.

    Identify ethical issues in data science.DS2-1.3

  • 4.

    Identify and compare potential bias issues in data scienceDS2-1.4

Formulate Questions to Clarify the problem at hand and formulate 1 or more questions that can be answered with dataDS2-2

  • 1.

    Identify the objectives of data and information managementDS2-2.1

  • 2.

    Determine whether a problem involves categorical or quantitative dataDS2-2.2

  • 3.

    Frame a statistical question of interest in terms of measurable dataDS2-2.3

Design and implement a plan to collect appropriate data to answer the research questionDS2-3

  • 1.

    Describe the factors that must be considered in distributing data effectively and how a simple model can be used to obtain at least a first-cut distributionDS2-3.1

Analyze data by selecting appropriate graphical and numerical methodsDS2-4

  • 1.

    Implement advanced spreadsheet functions, automation, and dynamic reporting.DS2-4.1

  • 2.

    Utilize various tools (such as the ARIMA model) to analyze time series data.DS2-4.2

  • 3.

    Demonstrate the ability to take data and create a dashboard that provides insight to solve real world problems.DS2-4.3

  • 4.

    Use graphical and numerical displays to foster further investigation into question of interestDS2-4.4

Identify the general concepts of databases/data tools and how to utilize design thinking to produce solutions that are clean and thoughtful.DS2-5

  • 1.

    Identify and distinguish between variations of techniques (Artificial Intelligence, Machine Learning, Deep Learning, etc.)DS2-5.1

  • 2.

    Provide definitions of key terms and concepts that describe the database environmentDS2-5.2

  • 3.

    Describe and build the major components of the database environment and explain how these components interact with each otherDS2-5.3

  • 4.

    Provide a review of systems development methodologies, particularly the waterfall method and agile programming development and show how database development fits with these methodologies.DS2-5.4

  • 5.

    Generate Entity Relationship logical models to represent organization data and plan for database development and infrastructureDS2-5.5

  • 6.

    Assess end user data and information requirements and develop a logical model to fit those requirementsDS2-5.6

  • 7.

    Describe the concept of supertype/subtype relationships and recognize when to use these relationships in data modeling.DS2-5.7

  • 8.

    Describe the use of specialization (top-down perspective) and generalization (bottom-upper perspective) as complementary techniques for defining supertype/subtype relationships and understand relationship constraints when modelling.DS2-5.8

  • 9.

    Describe the position of logical database design within the overall database development processDS2-5.9

  • 10.

    Describe the relational model including the properties of relations, integrity constraints, and well-structured relations.DS2-5.10

  • 11.

    Describe the principles and detailed steps involved in mapping Enhanced Entity Relationship diagrams to relations.DS2-5.11

  • 12.

    Understand data normalization, functional dependency, and develop a fully normalized Entity Relationship Diagram. Evaluate the normality of a logical data model and correct any anomaliesDS2-5.12

Build a database based on designed model, identify implementation policies and procedures, and establish plans for testing/debugging a data science solution.DS2-6

  • 1.

    Describe a Database Management System Language (DMBS) like SQL and summarize its basic operators.DS2-6.1

  • 2.

    Illustrate data definition language (DDL) commands for creating tables and views as well as for modifying and dropping tables.DS2-6.2

  • 3.

    Formulate single table DMBS (SQL) queries.DS2-6.3

  • 4.

    Formulate DMBS (SQL) queries that use functions.DS2-6.4

  • 5.

    Show how to establish referential integrity using DMBS (SQL).DS2-6.5

  • 6.

    Use of the "group by" and "order by" clauses in DMBS (SQL) queries.DS2-6.6

  • 7.

    Demonstrate (DBMS) SQL capabilities such as multiple-table data retrieval (join and other operators such as difference, union, and intersection), explicit and implicit joining, and built-in functions.DS2-6.7

  • 8.

    Illustrate the differences between the joining and subquery approaches to manipulating multiple tables in DMBS (SQL)DS2-6.8

  • 9.

    Describe triggers and stored procedures and provide examples of how these might be usedDS2-6.9

  • 10.

    Generate and implement a testing plan for a data management solution implementationDS2-6.10

Deploy a data science solution in a production environment, follow implementation procedures, and develop a plan for long term maintenance.DS2-7

  • 1.

    Describe the differences between the processes of deployment and implementation of solutions.DS2-7.1

  • 2.

    Understand the components and key steps to a successful deployment.DS2-7.2

  • 3.

    Build and deploy a data management system solution implementation.DS2-7.3

  • 4.

    Generate and implement a maintenance plan for a data management solution implementationDS2-7.4

Analyze results by interpreting the information provided by the data and how its interpretation supports possible answers to question or problem being investigated.DS2-8

  • 1.

    Utilize visual reporting and statistic tools to perform, understand, and interpret statistic such as regression analysis, ANOVA, hypothesis testing, and sampling distributionsDS2-8.1

  • 2.

    Identify and express areas for further study or investigation based on resultsDS2-8.2

  • 3.

    Create a dashboard with appropriate high-level charts, such as heat plots, box and whiskers, etc. to express the data that is being analyzedDS2-8.3

Frequently asked questions

What grade levels do these standards cover?
Grade 9, Grade 10, Grade 11, and Grade 12
When were these standards adopted?
2021

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