Course

Course Summary
Credit Type:
Course
ACE ID:
GOOG-0011
Organization's ID:
N/A
Organization:
Coursera Inc.
Location:
Online
Length:
4.5 months (90 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 1 Introduction to SQL
Lower-Division Baccalaureate 3 Databse Management and Visualization
Upper-Division Baccalaureate 3 Cloud Computing
Upper-Division Baccalaureate 3 Cloud Data Analytics
Description

Objective:

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

The course objective is for learners to master cloud data foundations, build data ingestion and transformation skills, become a data visualization pro, drive data-driven decision making, and develop their cloud data analyst careers.

Learning Outcomes:

  • Identify and connect to data sources in a cloud-based Data warehouse (BigQuery) or a cloud-based Data Lake (GCS/Dataproc) within the company’s data landscape and determine what data to look at in order to analyze data to come to a data conclusion
  • Describe what are the components that make up a data lakehouse architecture
  • Identify the appropriate factors that should be analyzed for reporting the status of a business data request and the tools that should be used for analysis
  • Communicate with stakeholders and users to determine why the data is needed
  • Identify transformations and data wrangling (manipulation) activities that need to be performed on the data
  • Analyze the data through tools such as SQL, Python, and Looker to determine the most effective way to visually present the insights (Bar chart, Pie chart, etc.)
  • Use tools such as BigQuery, Google Cloud Storage, and Cloud SQL databases, using SQL, to perform exploratory analytics
  • Use Business Intelligence platforms such as Looker Studio and Looker Enterprise to create dashboards, and do exploratory self-service analytics
  • Explain the differences and pros/cons between a relational database structure vs a columnar database structure, and the definition of primary keys vs foreign keys in a table
  • Effectively communicate data results to stakeholders using storytelling methods (Empathy and SME)
  • Describe how data analytics functions within a cloud environment (cloud economics)
  • Explain how data is organized (structures) and how data components interact with one another
  • Explain the differences between dimensions and measures in a BI tool like Looker, and when you would use each one
  • List common tools used (Ex: Cloud SQL, BigQuery, Jupyter Notebooks) to analyze data that is contained within data warehouses / data lakes
  • Describe how to analyze data in the cloud using Python, R and Pandas
  • Use SQL tool to run queries that join multiple tables and data sources and perform aggregations on data warehouses such as BigQuery
  • Identify who are the key roles that interact with data, and explain how your role plays into the overall data analytics business process within the organization

General Topics:

  • Course 1. Introduction to Data Analytics in Google Cloud
  • Course 2. Data Management and Storage in the Cloud
  • Course 3. Data Transformation in the Cloud
  • Course 4. The Power of Storytelling: How to Visualize Data in the Cloud
  • Course 5. Put It All Together: Prepare for a Cloud Data Analyst Job
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Computer Based Training
  • Laboratory
  • Practical Exercises
  • Performance Rubrics (Checklists)

Methods of Assessment:

  • Quizzes

Minimum Passing Score:

80%
Supplemental Materials