Course Summary
Credit Type:
24 weeks (175 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 introduction to databases
Lower-Division Baccalaureate 3 introduction to SQL Programming
Lower-Division Baccalaureate 3 introduction to systems analysis
Lower-Division Baccalaureate 3 introduction to R programming
This course is recommended for a total of 12 semester hours at the lower division baccalaureate/ associate degree level.


This course is offered through Coursera, which is an ACE Authorized Instructional Platform.

The course objective is to prepare learners for a career in the high growth field of data analytics with professional training designed by Google and hosted on Coursera. Students will gain in-demand skills preparing them for an entry-level job.

Learning Outcomes:

  • gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job
  • learn key analytical skills (data cleaning, analysis, and visualization) and tools (spreadsheets, SQL, R programming, Tableau)
  • understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming
  • learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms

General Topics:

  • Introducing data analytics
  • All about analytical thinking
  • The wonderful world of data
  • Set up your toolbox
  • Endless career possibilities
  • Asking effective questions
  • Making data-driven decisions
  • More spreadsheet basics
  • Always remember the stakeholder
  • Data types and data structures
  • Bias, credibility, privacy, ethics, and access
  • Databases: where data lives
  • Organizing and protecting your data
  • Engaging in the data community
  • The importance of integrity
  • Sparkling-clean data
  • Cleaning data with SQL
  • Verify and report on your cleaning results
  • Adding data to your resume
  • Organizing data to begin analysis
  • Formatting and adjusting data
  • Aggregating data for analysis
  • Performing data calculations
  • Visualizing data
  • Creating data visualizations with Tableau
  • Crafting data stories
  • Developing presentations and slideshows
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Discussion
  • Laboratory
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Quizzes

Minimum Passing Score:

Supplemental Materials