tags: 42 AWS

42 SACE WEEK - New Students

This template is for SACE new students.

🎉 Welcome

Hi

Welcome to 42 Adelaide SACE CLOUD Week!!

By the time you have finished this week's activities, you will have completed more then 30 hours of AWS Cloud Training!

AWS Student Information

Get to know your AWS Team

Monica Moorfield: AWS Skills to Jobs Senior Program Manager

Arran Peterson: Solutions Architect

Dylan Thompson: Solutions Architect / IoT Specialist

Justin Brien: Associate Solutions Architect / IoT Specialist

Brent Sarver: AWS Skills to Jobs Senior Program Manager

AWS Week

Day	Time	Topic
Monday 3 July	9:30am - 1:15pm	AWS Masterclass
Tuesday 4 July	9:30am - 5:00pm	Internet of Things (IoT)
Wednesday 5 July	9:30am - 5:00pm	Working with Data Pt1
Thursday 6 July	9:30am - 5:00pm	Working with Data Pt2
Friday 7 July	9:30am - 5:00pm	Project

Checklist

Day1

We kick off the week with an AWS Masterclass whereby local AWS Solution Architects will take you through an overview of AWS as a company, and AWS Cloud concepts such as compute, analytics and security. More broadly, students will learn more about career pathways in technology and hear from a range of Amazonians. This is a half day session, so you can spend the afternoon working on your assignments.

The day will be delivered as follows:

Workshop	Start Time	Topic	Duration
One	9:30am	Intro to the week & the team	45m
Two	9:45am	Kahoot! Quiz	45m
Three	10:00am	AWS Cloud 101	1.5 hrs
-	11:30am	Break	15m
Four	11:45am	Cloud roles quiz	15m
Five	12:00pm	Cloud Careers Panel	1 hr
-	1:00pm	Day Ends	-

Day2

Get your hands dirty!

The Sustainability IoT Hackathon is a fun way for students to learn about technology. During this all day event, students will learn how to use technology for good and how to program Internet of Things (IoT) devices with a visual programming interface. This will give the students practical experience interacting with IoT devices, such as sensors, buttons and water pumps. The students will also learn how this technology can be used for sustainable agriculture.

What will the learners do?

• Learn about the United Nations Sustainability Development Goals and how technology can be used to achieve them
• Use collaborative programming methods to learn both hard and soft skills
• Program IoT devices with a visual programming language built on Python to allow beginners and experts to work together
• Gain confidence that they will be able to succeed in a technology role

Workshop: https://catalog.workshops.aws/build-a-builder/en-US

The day will be delivered as follows:

Workshop	Start Time	Topic	Duration
One	9:30am	Intro to IoT	1hr
-	10:30am	Break - (Divide into groups)	15m
Two	10:45am	Set up the Device and Let’s get familiar	2hr
-	12:45pm	Break	30m
Three	1:15pm	Get Moving - Motion Detector	30m
Four	1:45pm	Feel the temperature of the room	30m
-	2:15pm	Break	15m
Five	2:30pm	Are you thirsty? Measure the soil moisture of each of our plants	30m
Six	3:00pm	Pump it - Control a pump to supply plants with water	1hr
Seven	4:00pm	Challenge Lab: Put it all together. Automatically water your plant, visualize the state of you plant and turn the lights off when nobody is home	30m
Eight	4:30pm	Open House. Stay to ask questions, finish exploring your project	30m
-	5:00pm	Day Ends	-

All the sections in Self-watering Plant Part 1 and Part 2 can be done with the equipment available.

Day3

Work with data!

AWS Glue DataBrew is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalise data to prepare it for analytics and machine learning. You can choose from over 250 pre-built transformations to automate data preparation tasks, all without the need to write any code. You can automate filtering anomalies, converting data to standard formats, and correcting invalid values, and other tasks. After your data is ready, you can immediately use it for analytics and machine learning projects.

Workshop

Company ABC is an online pet product retailer. They have recently launched a new product line for pets. They were expecting it to be a great success but sales from some regions were below their expectation. Marketing research suggests that limited awareness about the product in those regions is the main reason for inadequate sales. To address this problem, they want to start a targeted mail campaign in those regions to increase awareness and product sales. The team needs a fast turnaround and they don’t have much time to write code and manage infrastructure.

Actions: The team needs to analyse and transform customer, product, and sales data to compare and contrast sales of two product lines and identify postcodes where specific product types need to be marketed. The students will perform these tasks and complete the following labs.

Labs

• Profiling and Data Quality
• Transforming Data

https://catalog.us-east-1.prod.workshops.aws/workshops/6532bf37-3ad2-4844-bd26-d775a31ce1fa/en-US/30-howtostart/workshopstudio

The day will be delivered as follows:

Workshop	Start Time	Topic	Duration
One	9:30am	Intro to Glue Databrew	30m
Two	10:00am	Profiling and Data Quality	1hr
-	11:00am	Break	15m
Three	11:15am	Standard Transform	45m
-	12:00pm	Break	1hr
Four	1:00pm	Advanced Transform	1hr
-	2:00pm	Break	15m
Five	2:15pm	Project Work or Feature Engineering	3hr
-	5:00pm	Day Ends	-

How do I find data for my project?

AWS Data Exchange Easily find, subscribe to, and use third-party data in the cloud https://aws.amazon.com/data-exchange/

Day4

Amazon SageMaker Canvas expands access to machine learning (ML) by providing students with a visual interface that allows them to generate accurate ML predictions on their own—without requiring any ML experience or having to write a single line of code.

With Amazon SageMaker Canvas, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and quickly and more easily build ML models. You can then analyse models and generate accurate predictions with a few clicks.

Workshop

Self-paced lab that allows students to enable themselves or others into using no-code Machine Learning and Amazon SageMaker Canvas to solve real-world challenges based on publicly available datasets. While it is expected for anyone to be able to use SageMaker Canvas in complete autonomy.

Use Case Labs

• Prediction Machine Failure Types (Manufacturing)
• Supply chain delivery on-time (Transport & Logistics)
• Customer Churn (Marketing)
• Housing Pricing (Real Estate)
• Demand Forecasting (Retail)
• Loan Default Prediction (Financial Services)
• Diabetic Patient Readmission Prediction (Healthcare)

Technology Labs

• No-Code Computer Vision
• No-Code Natural Language Processing (Text Analysis)
• Customer Churn (Marketing)

https://catalog.workshops.aws/canvas-immersion-day

The day will be delivered as follows:

Workshop	Start Time	Topic	Duration
One	9:30am	Intro to Amazon SageMaker Canvas	30m
Two	10:00am	Set the scene for Lab3	15m
Three	10:15am	Predicting Machine Failure Types (Manufacturing)	90m
-	11:45am	Break	1hr
Four	12:45pm	Supply chain delivery on-time (Transportation and Logistic)	90m
-	2:15pm	Break	15m
Five	2:30pm	Technology Labs (Optional) or Project	2hr
-	4:30pm	Day Ends	-

Day5

How would you solve one of the 17 UN goals using data to draw your insights?

Outcome: Present your findings back to the group.

Assessment

You'll be assessed based on the successful completion of your project.

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Additional Reading

• IT skills training, certifications benefit both organizations and employees
• When cloud curiosity leads to a career transformation
• What's The Difference Between A Data Warehouse, Data Lake, And Data Mart?
• Learn About Data analytics

Reference Material

Data Engineering

AWS Glue DataBrew Blog Articles

• https://aws.amazon.com/blogs/machine-learning/category/analytics/aws-glue-databrew/

Machine Learning

Amazon SageMaker Canvas Blog Articles

• https://aws.amazon.com/blogs/machine-learning/tag/amazon-sagemaker-canvas/

SageMaker Canvas Advanced Metrics deep dive

• https://aws.amazon.com/blogs/machine-learning/is-your-model-good-a-deep-dive-into-amazon-sagemaker-canvas-advanced-metrics

SageMaker Built-in Algorithms used in Canvas

• https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

Building Custom Models in Canvas

• https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-custom-models.html

External Sources

These resources have been collated from student experience and aren't affiliated with AWS.

TBA

FAQ

Online Learning

What is AWS Educate?

• Cloud computing can open the door to exciting opportunities in technology or business. AWS Educate provides simple, barrier-free access to secure, authentic, self-graded labs in the AWS Console with just an email address. Learn, practice, and evaluate cloud skills in real time without creating an Amazon or AWS account. AWS Educate offers hundreds of hours of free, self-paced online resources designed specifically to help pre-professional learners develop in-demand cloud skills. You’re in control of your learning journey: follow personalized content recommendations to earn badges that demonstrate your skill mastery and search the job board to find employers looking to hire for in-demand roles.

What is AWS Skill Builder?

• AWS Skill Builder is a repository of over 700 training lessons to help you learn AWS and refine your knowledge of AWS services and improve your skills so you can put them into practice or apply the knowledge during the many AWS certifications.