Course Summary
Credit Type:
44 weeks (88 hours in total)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 Introduction to Data Analytics
Lower-Division Baccalaureate 3 Introduction to Databases and SQL Programming
Upper-Division Baccalaureate 3 R Programming Fundamentals
Upper-Division Baccalaureate 3 Advanced Data Analytics
Upper-Division Baccalaureate 3 Data Visualization


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

The course objective is for students to develop the job-ready skills, tools, and portfolios for an entry-level data analyst or data scientist position. Through these eight online modules, learners will dive into the role of a data analyst or data scientist and develop the essential skills needed to work with a range of data sources and apply powerful tools, including Excel, Cognos Analytics, and the R programming language, towards becoming a data-driven practitioner.

By the end of this Professional Certificate, students will be able to explain the data analyst and data scientist roles, work with Excel spreadsheets and utilize them for data analysis to create charts and plots, utilize Cognos Analytics to create interactive dashboards and with relational databases and query data using SQL statements. Learners also use the R programming language to complete the entire data analysis process - including data preparation, statistical analysis, data visualization, predictive modeling, creating interactive data applications, communicating data findings, and preparing a report for stakeholders.

Learning Outcomes:

  • Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, and creating charts
  • Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny
  • Complete the data analysis process, including data preparation, statistical analysis, predictive modeling, using R, R Studio, and Jupyter
  • Create relational databases and tables, query data, sort, filter and aggregate result sets using SQL and R from JupyterLab

General Topics:

  • What is Data Analytics
  • The Data Ecosystem
  • Gathering and Wrangling Data
  • Mining & Visualizing Data and Communicating Results
  • Career Opportunities and Data Analysis in Action
  • Introduction to Data Analysis Using Spreadsheets
  • Getting Started with Using Excel Speadsheets
  • Cleaning & Wrangling Data Using Spreadsheets
  • Analyzing Data Using Spreadsheets
  • Final Project
  • Visualizing Data Using Spreadsheets
  • Creating Visualizations and Dashboards with Spreadsheets
  • Creating Visualizations and Dashboards with Cognos Analytics
  • Final Project
  • R Basics
  • Common Data Structures
  • R Programming Fundamentals
  • Working with Data
  • Final Project
  • Getting Started with SQL
  • Introduction to Relational Databases and Tables
  • Intermediate SQL
  • Getting Started with Databases using R
  • Working with Database Objects using R
  • Course Project
  • Introduction to Data Analysis with R
  • Data Wrangling
  • Exploratory Data Analysis
  • Model Development in R
  • Model Evaluation
  • Project
  • Introduction to Data Visualization
  • Basic Plots, Maps, and Customization
  • Dashboards
  • Final Assignment
  • Capstone Overview and Data Collection
  • Data Wrangling
  • Performing Exploratory Data Analysis with SQL, Tidyverse & ggplot2
  • Predictive Analysis
  • Building a R Shiny Dashboard App
  • Present Your Data-Driven Insights
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Computer Based Training
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Other
  • Quizzes
  • Peer review graded projects with rubrics

Minimum Passing Score:

Supplemental Materials