Course

Course Summary
Credit Type:
Course
ACE ID:
GOOG-0011
Organization's ID:
N/A
Organization:
Google
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:

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:

  • Describe how data analytics functions within a cloud environment (cloud economics)
  • 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
  • Explain how data is organized (structures) and how data components interact with one another
  • 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.)
  • List common tools used (Ex: Cloud SQL, BigQuery, Jupyter Notebooks) to analyze data that is contained within data warehouses / data lakes
  • Use tools such as BigQuery, Google Cloud Storage, and Cloud SQL databases, using SQL, to perform exploratory analytics
  • Describe how to analyze data in the cloud using Python, R and Pandas
  • Use Business Intelligence platforms such as Looker Studio and Looker Enterprise to create dashboards, and do exploratory self-service analytics
  • Explain the differences between dimensions and measures in a BI tool like Looker, and when you would use each one
  • Use SQL tool to run queries that join multiple tables and data sources and perform aggregations on data warehouses such as BigQuery
  • 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
  • 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
  • Effectively communicate data results to stakeholders using storytelling methods (Empathy and SME)

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