Bethel's 2024 Workshop for Interns

Basic information

• Instructor: Dr. Cathy Bareiss
• Location: Bethel University: Science Building 027
• Original Date/Time: Fridays from 3:00pm-5:00pm. Moved to online videos and courses/labs

Recorded videos

Resources

• Introduction to the cloud: Google Cloud Fundamentals: Core Infrastructure: Link to the entire course https://www.cloudskillsboost.google/course_templates/60
  • Introducing Google Cloud: an overview of IaaS and PaaS
  • Virtual Machines and Networks in the cloud: focus on the part about the compute engine
  • Storage in the cloud. This entire module is very good. It covers cloud storage and databases
  • Prompt Engineering: About LLM: https://www.cloudskillsboost.google/course_templates/60/video/487111 -> should work
• Analyzing and Visualizing Data the Google Way: https://www.cloudskillsboost.google/course_templates/480
• Exploring and Preparing your Data with BigQuery: https://www.cloudskillsboost.google/course_templates/14. This is a LONG course (24 hours) and includes a good introduction into SQL. It would be great for continuing work. The first two sections gives a good overview
• Google Cloud Big Data and Machine Learning Fundamentals: https://www.cloudskillsboost.google/course_templates/3 This is a good overview (5 hours) with videos and labs about Big Data and Machine Learning. It explains the concepts and does some demos.
• Baseline: Data, ML, and AI: https://www.cloudskillsboost.google/course_templates/619. This is just a series of labs showing how things can be done on Google Cloud.
• BigQuery Basics for Data Analysts: https://www.cloudskillsboost.google/course_templates/677 This is a series of labs to help you use BigQuery. Do the Big Data and Machine Learning Fundamentals first.

Original Schedule

• June 14
  1. Introduction to the Cloud
  2. Introduction to Python
• June 21
  1. Virtual Machines in the cloud
  2. Loops with Python (part 1)
• June 28
  1. Databases in the cloud
  2. Loops with Python (part 2)
• July 5
  1. Big Data in the cloud
  2. Methods with Python
• July 12
  1. Overview of AI
  2. Python and working with big data
• July 19
  1. Ethics of AI
  2. Project (part 1)
• July 26
  1. AI in the cloud
  2. Project (part 2)
• August 2
  1. Summary of what was learned
  2. Presentation of project