Course Summary
Credit Type:
6-9 months (132 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 introduction to data analytics
Lower-Division Baccalaureate 3 introduction to SQL programming
Lower-Division Baccalaureate 3 introduction to Python programming
Upper-Division Baccalaureate 3 advanced topics in data analytics


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

The course objective is to gain the job-ready skills for an entry-level data analyst role by analyzing real-world datasets, creating interactive dashboards, and presenting reports to share findings to gain confidence and a portfolio to begin a career as an associate or junior data analyst. Students also build the foundation for other data disciplines such as data science or data engineering.

Learning Outcomes:

  • demonstrate proficiency in using spreadsheets and utilizing Excel to perform a variety of data analysis tasks like data wrangling and data mining
  • develop working knowledge of Python language for analyzing data using Python libraries like Pandas and Numpy, and invoke APIs and web services
  • describe data ecosystem and compose queries to access data in cloud databases using SQL and Python from Jupyter notebooks
  • create various charts and plots in Excel and work with IBM Cognos Analytics to build dashboards
  • visualize data using Python libraries like Matplotlib.

General Topics:

  • What is data analytics?
  • The data ecosystem
  • Gathering and wrangling data
  • Mining and visualizing data and communicating results
  • Career opportunities and data analysis in action
  • Introduction to data analysis using spreadsheets
  • Cleaning and wrangling data using spreadsheets
  • Analyzing data using spreadsheets
  • Visualizing data using spreadsheets
  • Creating visualizations and dashboards with spreadsheets
  • Creating visualizations and dashboards with Cognos Analytics
  • Python basics
  • Python data structures
  • Python programming fundamentals
  • Working with data in Python
  • APIs and data collection
  • Crowdsourcing short squeeze dashboard
  • Getting started with SQL
  • Introduction to relational databases and tables
  • Intermediate SQL
  • Accessing databases using Python
  • Bonus module: advanced SQL for data engineer
Instruction & Assessment

Instructional Strategies:

  • Discussion
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Performance Rubrics (Checklists)
  • Quizzes
  • peer-reviewed final project

Minimum Passing Score:

Supplemental Materials