Course

Credit Type:
Course
ACE ID:
CBTC-0001
Version:
1
Organization's ID:
BUS500
Organization:
Calbright College
Location:
Online
Length:
Contact Hours: 30 Total Student Learning Hours: 60
Minimum Passing Score:
80
ACE Credit Recommendation Period:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 Data Structures I: Introduction
Description

Objective:

This is an introductory non-credit, competency-based course, in data analysis. Students will gain an understanding of basic terminology used in data analysis, how data is generated, and be able to organize and visualize data using spreadsheets and Structured Query Language (SQL). Students will also explore how data analytics relates to research and ethical practices. Students will begin preparing for the workplace by learning effective presentation and data storytelling techniques.

Learning Outcomes:

  • Identify and apply basic computing skills to work with different types of data structures, file formats, sources of data, and data repositories.
  • Apply basic math concepts to identify patterns, trends, and outliers within a data set.
  • Organize complex data using spreadsheets and basic SQL.
  • Analyze data sets to determine the domain context and evaluate the viability of the data for the associated project.
  • Use data storytelling and visualization to inform decision-making.

General Topics:

  • Terminology: Define web, programming, and database terminology used in data analysis and apply basic computing skills (i.e., Uploading, downloading, importing, exporting, and troubleshooting software). Spreadsheets: Apply data in spreadsheets to enter, format, clean, and prepare data for analysis. Basic SQL Queries: Apply basic SQL queries by manipulating and modifying them to change the visual output of data. Professional Presentation Skills: Present data findings using various data visualization techniques. Research: Explain how research projects are designed and supported through the incorporation of data. Data Validity: Evaluate data sets to determine the domain context and validity of the data set for a given project. Foundational Mathematical Principles: Identify patterns within a data set, including basic mathematical concepts such as mean, median, mode, linear, and nonlinear
Instruction & Assessment

Instructional Strategies:

  • ● Online Text-Based Lessons ● Interactive Activities ● External Videos ● Problem-based Learning/Scenarios ● Demonstration/Modeling ● Facilitated Discussions

Methods of Assessment:

  • Other
  • Performance Rubrics (Checklists)
  • Quizzes
  • Project-based learning scenarios
Supplemental Materials
Equivalencies

Other offerings from Calbright College

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