This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use Terraform to create secure, private, site-to-site connections between Google Cloud and Amazon Web Services (AWS) using virtual private networks (VPNs).

By the end of this project, you will be able to create an accurate customer problem statement that diagrams the problem that your brand or product will solve for the customer. The power of creating a customer problem statement is that it becomes business intelligence that can maximize business opportunities by solving user experience problems. It does this by expanding upon the knowledge of the customer’s user experience by empathizing with the customer and the challenges or needs they must meet as a part of their normal life journey. In your project you will understand the benefits and use cases for customer problem statements while developing your own customer problem statement geared toward solving user experience or UX problems. To do this, you will gain hands-on experience applying design thinking, user experience knowledge, and context from the customer journey to build a visualization of a customer problem statement in the Miro online visual collaboration platform for teamwork. 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.

By the end of this project, you’ll be comfortable using the pen tool to create all kinds of lines and shapes, from basic to complex in Adobe Illustrator. To master the pen tool, you’ll create multiple vector leaves—one with sharp corners and one with curving paths.

In this 2-hour long project-based course, you will learn how to administer automated checks, understand the test results, and conduct manual accessibility tests to evaluate whether a site is perceivable, operable, understandable, and robust. By the end of the project, you will have generated an accessibility report by running industry-leading automated tests and basic manual tests. 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.

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams

Internet of things (IoT) has become a significant component of urban life, giving rise to “smart cities.” These smart cities aim to transform present-day urban conglomerates into citizen-friendly and environmentally sustainable living spaces. The digital infrastructure of smart cities generates a huge amount of data that could help us better understand operations and other significant aspects of city life. In this course, you will become aware of various data mining and machine learning techniques and the various dataset on which they can be applied. You will learn how to implement data mining in Python and interpret the results to extract actionable knowledge. The course includes hands-on experiments using various real-life datasets to enable you to experiment on your domain-related novel datasets. You will use Python 3 programming language to read and preprocess the data and then implement various data mining tasks on the cleaned data to obtain desired results. Subsequently, you will visualize the results for the most efficient description.

This course provides hands-on experience with technology-based productivity tools, as well as foundational knowledge and understanding of system design and development. The course is designed to integrate concepts of hardware, software, and the Internet. This course also provides an overview of data security, data privacy, and ways to increase productivity and efficiency. Students will also investigate technology career paths and some of the various certifications available in the industry.

In this 1-hour long project-based course, you will learn how to read all sorts of data and import them into R, including CSV files, Excel files, data from other statistical software, the web and from relational databases. 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.

In this project-based course, you will learn about Markov chains and use them to build a probabilistic model of an entire book’s text. This will be done from first principles, without libraries. Markov chains are a simple but fundamental approach to modeling stochastic processes, with many practical applications. By the end of this project, you will have generated a random new text based on the book you modeled, using code you wrote in Python.

By the end of this project, you will learn how to use Canva to create 3 different templates for social media stories contents aimed to engage and enlarge your niche. Stories are currently among the strongest tools on social media to enhance public engagement.Whether you are an influencer, entrepreneur or a brand you will eventually face online presence, social media and consequently public engagement. Public engagement includes marketing strategies that directly engages consumers or followers to participate in a brand experience. It comes from the concept that consumers/followers should be actively involved in the production and co-creation of marketing programs, developing a relationship with the brand. The same approach is valid for influencers, entrepreneurs as well as for companies.Social networks are largely use in engagement marketing because they provide the optimal way for people to interact with brands and create a two-way dialogue between each other. Some of these platforms have also created specific types of online presences for companies. One of the tools that is currently spreading in most of the social media are stories. These tools are widely used to entertain, engage and develop public on social media.This guided project is for young entrepreneurs, content creators, influencers, students, graphic designers, who want to explore Canva in visual contents creation aimed to develop public engagement.No previous experience needed. Familiarity with different social media platforms recommended.