1. Cross-Cutting KSAs

KSAC Description		Bloom's Taxonomy Level
Identify and describe basic file types and demonstrate fundamental file management.	skill	2
Demonstrate an understanding of cloud architecture and the capabilities of services such as AWS, Azure, IBM, Oracle and Google.	knowledge	2
Ability to install and configure software.	ability	3
Explain data security in terms of authentication, authorization, access and auditing.	knowledge	3
Understand OSI model and how it applies to an example.	knowledge	2
Identify and apply Transmission Control Protocol and Internet Protocol (TCP/IP), Internet Protocol Version 4 (IPv4), Internet Protocol Version 6 (IPv6) applications and services (e.g., rlogin, Simple Mail Transfer Protocol [SMTP], Telecommunications Network [Telnet], File Transfer Protocol [FTP], Domain Name System [DNS], Network File System [NFS], Voice over Internet Protocol [VoIP], Internet Control Message Protocol [ICMP]).	knowledge	2
Compare and contrast Internet connection types, network types and their features (e.g. T-Lines, fiber cables, microwaves, cellular, satellite) Layers 1 & 2	knowledge	2
Apply secure network Protocols (e.g., IPSec, SNMP, SSH, DNS, TLS, SSL, TCP/IP, FTPS, HTTPS, SCP, ICMP).	ability	2
Apply SQL data manipulation language such as Select (From), Insert, Update, Delete, JOIN (inner, outer, full, left, right), Where, Group By, Order By, etc.	ability	3
Demonstrate fundamental programming skills including the use of variables, loops, conditional branching, and program logic.	skill	3
Ability to normalize a database through 3rd normal form.	ability	3
Differentiate common data typologies, including structured vs. unstructured, numeric vs. text, root vs. derived.	knowledge	3
Explain and demonstrate how differences in data and desired outcomes impact the appropriateness of data analysis techniques (e.g., descriptive vs. diagnostic vs. predictive vs. prescriptive).	knowledge	2
Demonstrate knowledge of probability and standard statistical distributions.	knowledge	1
Demonstrate and explain the role and importance of model validation and accuracy metrics in analytics projects, hypothesis testing, and information retrieval.	knowledge	2
Perform basic data manipulation and exploration using appropriate tools and software, including use of key Excel functions.	skill	3
Create and edit simple data structures and storage, understanding how version control affects each.	skill	2
Explain the role of data visualization in discovery, communication, and decision-making.	knowledge	3
Evaluate data visualization options for proper application in various situations.	ability	4
Create effective static and interactive data visualizations or storytelling that employ analytics and visualization software and strategies for various audiences.	skill	3
Visualize data using various types of displays including tables, dashboards, graphs, maps, and trees.	skill	3
Distinguish between advanced visualizations and explain the advantages of each.	knowledge	3
Discuss techniques for creating advanced data visualizations.	knowledge	3
Apply the principles of color, composition, and hierarchy to design.	skill	3
Properly define a problem in context, use appropriate data, and deliver a compelling visualization to explain or answer a question.	ability	3
Understanding of ADA/508 compliance for accessibility.	knowledge	1
Identify how global legal, policy and/or ethical constraints might impact data analyses.	knowledge	2
Identify the established ethical and legal issues in data management facing organizations.	knowledge	2
Explain how ethical, compliance, and legal issues should/must be considered in data driven decision making.	knowledge	1
Explain the importance of personal privacy issues related to the collection and usage of data.	knowledge	2
Explain the important issues around data governance.	knowledge	2
Describe the fundamental cloud components (e.g., shared or dedicated processing, storage, memory, networking, hypervisor).	knowledge	2
Differentiate between public, private, and hybrid clouds.	knowledge	2
Identify common breaches and threats in the cloud environment.	knowledge	1
Instantiate a small computing environment in a cloud service.	ability	3
Explain the pros and cons of on-premises vs cloud-based analytics solutions.	knowledge	2
Understand how to set security configurations in a cloud environment.	knowledge	2
Understand the concept of opening/extending the network perimeter and the role of a cloud access security broker (CASB).	knowledge	2
Explain DNS traffic.	knowledge	2
Identify the layers of the OSI Model.	knowledge	2
Summarize the responsibilities of each layer of the OSI Model.	knowledge	2
Explain how the OSI Model is applied in networking.	knowledge	3
Configure IPv4 and IPv6 classful subnets.	skill	1
Compare public IP addresses and private IP addresses.	knowledge	2
Identify IPv4 address network ID (Class A, Class B, Class C).	knowledge	2
Interpret classless network ID (CIDR block notation).	knowledge	2
Explain domain naming conventions (UNC path, FQDN, host name).	knowledge	3
Compare Network Address Translation and Port Address Translation (NAT vs PAT).	knowledge	2
Draw a network diagram.	skill	3
Analyze the output from networking utilities (e.g. Netstat, Tracert, Traceroute, Ping IPConfig, IFConfig).	skill	3
Discuss network software integration (client software (e.g. Windows 10 or Ubuntu) and server software).	ability	3
Discuss network hardware integration (workstations, desktop, mobile devices).	knowledge	2
Communicate best practices for troubleshooting networking issues (layers 1-2 at HS level) (7-step model).	knowledge	3
Identify common coding errors that lead to insecure programs (e.g., buffer overflows, memory leaks, malicious code) and apply strategies for avoiding such errors.	skill	3
Apply the principles of least privilege, defensive programming, and fail-safe defaults.	ability	3
Write code with logging capabilities.	skill	2
Understand basics of securing web apps - SQL Injection and other input validation.	knowledge	2
Identify and differentiate structured vs unstructured data.	knowledge	2
Design, implement, test, and debug a program that uses each of the following fundamental programming constructs: basic computation, simple I/O, standard conditional and iterative structures.	skill	3
Write a program that uses file I/O to provide persistence across multiple executions.	skill	2
Write programs that use each of the following data structures: arrays, records/structs, strings, linked lists, stacks, queues, sets, and maps.	skill	3
Choose the appropriate data structure for modeling a given problem.	skill	3
Implement a divide-and-conquer algorithm for solving a problem.	skill	3
Implement a coherent abstract data type, with loose coupling between components and behaviors.	skill	3
Explain core statistical inference concepts (for example, deriving relevant hypotheses, evaluating the hypotheses, and prediction with uncertainty).	knowledge	2
Explain core probability concepts (e.g., random variables, key distributions, conditional probability, Bayes theorem).	knowledge	2
Identify and describe several SDLC models (e.g., waterfall, Agile).	knowledge	2
Provide rationale for selecting the appropriate sampling methodology.	skill	3
Present real world examples of data bias and the unintended consequences of using analytics, machine learning, and AI in making decisions.	knowledge	2
Discuss the importance of provenance, transparency, and explainability in data analysis and the ability to build trust.	knowledge	2
Explain the limitations and potential unintended effects of data analysis when such algorithms encounter new scenarios.	knowledge	2
Explain individual and data bias and the implications each has in data analysis.	knowledge	3
Describe the implications of data architecture on data processing such as data fabric.	knowledge	2