The world runs computers. From small to large businesses, from the CEO down to level 1 support staff, everyone uses computers. This course is designed to give you a practical perspective on computer security. This course approaches computer security in a way that anyone can understand. Ever wonder how your bank website is secure when you connect to it? Wonder how other business owners secure their network? Wonder how large data breaches happen? This is practical computer security. It will help you answer the question – what should I focus on?

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you familiarize yourself with Google Cloud Billing reports, which provides built-in cost reporting for GCP within the Google Cloud Console. You view Billing reports from a live billing account, understand current and forecasted GCP costs, and then analyze costs using report filters to identify cost drivers and trends

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will learn how to train and deploy an object detector using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the SSD Object Detection algorithm from Sagemaker to create, train and deploy a model that will be able to localize faces of dogs and cats from the popular IIIT-Oxford Pets Dataset. Since this is a practical, project-based course, we will not dive in the theory behind deep learning based SSD or Object Detection, but will focus purely on training and deploying a model with Sagemaker. You will also need to have some experience with Amazon Web Services (AWS). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Explore cloud computing basics without installing anything! This course is designed for semi-technical and business learners, providing a solid foundation of cloud computing basics. Learners will build an understanding of how infrastructure as a service (IaaS) works as well. The modules in this course cover cloud computing basics, considerations for IaaS adoption, techniques for IaaS success and growth, as well as provide an exploratory IaaS experience for learners. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and suggested exploration examples, building a deeper understanding by doing. You'll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling answer blocks) to small, approachable exercises that take minutes instead of hours.

This mini-course is intended to apply foundational Python skills by implementing different techniques to collect and work with data. Assume the role of a Data Engineer and extract data from multiple file formats, transform it into specific datatypes, and then load it into a single source for analysis. Continue with the course and test your knowledge by implementing webscraping and extracting data with APIs all with the help of multiple hands-on labs. After completing this course you will have acquired the confidence to begin collecting large datasets from multiple sources and transform them into one primary source, or begin web scraping to gain valuable business insights all with the use of Python. PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to build a reusable pipeline that reads data from Cloud Storage, performs data quality checks, and writes to Cloud Storage.

In this course we will introduce you to Google Meet and Google Chat, Google’s video conference and chat software included with Google Workspace. You will learn how to create and manage video conference meetings using Google Meet. You will also explore how to use Google Chat for simple one-to-one and small group conversations, and how Chat rooms are used in Google Chat to better collaborate with others. You will step into the Google Meet environment so that you become familiar with the different ways you can open Google Meet and add people to a video conference. We will also look at how to join a meeting from different sources such as a calendar event or meeting link. You will learn how Google Meet can help you better communicate, exchange ideas, and share resources with your team wherever they are. We will also discuss how to customize the Google Meet environment to fit your needs. We will talk about managing participants and how to effectively use chat messages during a video conference. We will explore the different ways to share resources, such as via calendar invites or attachments. With Google Chat, you can use direct messaging to have a private conversation with a colleague or a small group of people. You will learn more about Google Chat’s unique features and how Google Chat can enhance your organization's ability to communicate and collaborate. Direct messages to a person or small group may be appropriate for impromptu conversations, but a chat room may be better suited for long-term conversations, particularly with a group that changes, or a large project team. You will explore the use of Chat rooms in Google Chat. We will look at some of the ways you can use Google Chat to effectively collaborate with others. We will look at how you can upload and share files with others from Google Chat.

This is a self-paced lab that takes place in the Google Cloud console. Learn how to build a BI dashboard with Data Studio as the front end, powered by BigQuery on the back end

Learn how to work with web technologies and persistent data on Android applications even after you close or restart an app. There is a focus on web communication and developer tools and you will discover how Kotlin applications communicate over the web. You’ll learn how data formats and web protocols work in relation to Kotlin apps. Furthermore, you will practice applying asynchronous programming techniques using Kotlin. Learn the core functionality and uses of the SQLite database management system (DBMS). Learn about web clients and databases by adding connections from your app to other languages to access custom built web application programming interfaces (APIs) and database management systems.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab you will create and use Terraform modules to organize your cloud configuration.