Open source networking projects are transforming how service providers and enterprises develop, deploy, and scale their networks and next-generation services. The Open Network Automation Platform (ONAP) project orchestrates and manages physical and virtual network services to bring agility, higher customer satisfaction and lower costs. This course provides: The basics of Network Function Virtualization (NFV) An introduction to The Linux Foundation ONAP project The challenges ONAP solves Overview of the ONAP project’s architecture, subprojects and demos Is your organization embarking on anetwork transformation journey? Do youunderstand why open source software will play a critical role in this journey? Are you unclear how to manage and orchestrate network services for your SDN/NFV use case? If yes, this course is for you. This course is designed to provide a high-level understanding and business perspective of the ONAP project and a guide for navigating, participating, and benefiting from the ONAP community. The course is also meant for vendorswho wish to determine how to position or sell their products into the ONAP ecosystem.

Build on your existing knowledge of conditionals, loops, and functions by studying more about complex Python data structures, including strings, lists, dictionaries, and file input and output. Organized into five chapters, this course starts by covering the basics of data structures, then moves on to these four common data structures in Python: Strings let you perform far more complex reasoning with text. Lists let you process long lists of data, and even lists of lists of data for more complex reasoning. Dictionaries let you more clearly code for complex types of data, and even simulate some basic elements of object-oriented programming. File input and output brings your programs to life, allowing you to persist data across executions of the same program. By the end of this course, you'll be able to write even more complex programs in Python that process and persist complex data structures. For example, you'll be able to write an ongoing gradebook application that tracks and updates your average over time, a program to calculate the net force based on several force magnitudes and directions, or a program that can turn a string like this into a StRiNg LiKe tHiS. Structurally, the course is comprised of several parts. Instruction is delivered via a series of short (2-3 minute) videos. In between those videos, you'll complete both multiple choice questions and coding problems to demonstrate your knowledge of the material that was just covered.

How much would you like your smart home to know about you? Has your data been harvested and used for political advertising on social media? Would you be happy to be profiled by a predictive policing AI? As we create more data-driven technologies, those issues become increasingly urgent. We must begin to ask not only ‘what can we do?’, but also ‘what should we do?’ How should we design new technologies to make sure they are used for good, not bad purposes? The ‘good’, the ‘bad’, and the ‘should’ are a domain of ethics, and a basis for other important concepts such as justice, fairness, rights, respect. They further inform the law and what is legal. Finally, they are at the roots of an extremely important currency in the modern economy: trust. This story-driven course is taught by the leading experts in data science, AI, information law, science and technology studies, and responsible research and innovation, and informed by case studies supplied by the digital business frontrunners and tech companies. We will look at real-world controversies and ethical challenges to introduce and critically discuss the social, political, legal and ethical issues surrounding data-driven innovation, including those posed by big data, AI systems, and machine learning systems. We will drill down into case studies, structured around core concerns being raised by society, governments and industry, such as bias, fairness, rights, data re-use, data protection and data privacy, discrimination, transparency and accountability. Throughout the course, we will emphasise the importance of being mindful of the realities and complexities of making ethical decisions in a landscape of competing interests. We will engage with data-based contexts such as facial recognition, predictive policing, medical screening, smart homes and cities, banking, and AI, to explore their social implications and the tools required to minimise harm, promote fairness, and safeguard and increase human autonomy and well-being. We address cutting edge issues being grappled with by practitioners and new approaches emerging in industry and offer the opportunity for participants to develop and feedback solutions. Completing this course will help you understand the challenges we are facing, and inspire you to design, criticise, and develop better intelligent systems to shape our future.

As the Internet of Things (IoT) continues to grow so will the number of privacy and security concerns and issues. As a professional working in the field, it is essential to understand the potential security risks and how to best mitigate them. In this course, you will learn about security and privacy issues in IoT environments. We’ll explore the organizational risks posed by IoT networks, and the principles of IoT device vulnerabilities. We’ll also look at software and hardware IoT Applications for industry. With billions of devices tracking our every move, privacy is a critical issue. We will explore and discuss the social and commercial implications the IoT brings to society.

Want to produce and record your own music? This course will help you do that by showing you how to apply new technologies to your own creative practice, using freeware and browser based apps. Music Technology Foundations draws on Adelaide’s world-class pioneering expertise in making electronic music, to provide a great foundation to a career in music and to enable any learner to use technology in creative ways. In this course, you’ll learn about the core principles of music technology, including sound, audio, MIDI, effects and sequencing.

Improvements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment. For example, individual data points collected from one gene expression study can easily number in the hundreds of thousands. These types of data sets are often referred to as ‘biological big data’ and require bioinformaticians to use statistical tools to gain meaningful information from them. In this course, part of the Bioinformatics MicroMasters program, you will learn about the R language and environment and how to use it to perform statistical analyses on biological big datasets. This course is part of the Bioinformatics MicroMaster’s program from UMGC. Upon completion of the program and receipt of the verified MicroMaster’s certificate, learners may then transition into the full UMGC Master’s Program in Biotechnology with a specialization in Bioinformatics without any application process or testing. See the MicroMasters program page for more.

In this course, you will learn what AI is and understand its applications and use cases and how it is transforming our lives. You will explore basic AI concepts including machine learning, deep learning, and neural networks as well as use cases and applications of AI. You will be exposed to concerns surrounding AI, including ethics, bias, jobs and the impacts on society. You will take a glimpse of the future with AI, get advice for starting an AI related career, and wrap up the course by demonstrating AI in action with a mini project. This AI for Everyone course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Experimentation is a key capability for any business to develop and master. Learn how to leverage data to build knowledge and apply this knowledge to improve business outcomes and create strategic advantages. This course is part of both the Digital Product Management and Digital Leadership MicroMasters programs. In it, you will learn to develop iterative business experiments using agile methods. This capability is central to digital businesses as it allows them to sustain competitive advantage through both incremental improvements as well as significant, disruptive innovations when opportunities and conditions warrant them. This course focuses on experimentation across the three layers of a digital business: (1) the capacity of the technical infrastructure to provide an iterative and operational process that uses experiments to gather data and develop knowledge (2) the ability to use agile methods and manage the knowledge interfaces among experts at the organizational layer to derive insight from data to create knowledge and ultimately drive improvements in products and processes. (3) the capability to use the technical and organizational infrastructures to drive experimentation at scale in order to deliver digital transformation.

JavaScript is the programming language of the World Wide Web. As a professional web software developer, you will not only need to know how to program in this simple yet powerful language, but you will need to understand the fundamentals of how data is exchanged on the World Wide Web (WWW) and what tools and frameworks are available to you for creating robust, interactive web applications. This course, part of the CS Essentials for Software Development Professional Certificate program, provides an introduction to modern web development using JavaScript. In addition to exploring the basics of web page creation using HTML and CSS, you will learn advanced web page layout and responsive design tools such as Bootstrap. You will also learn how browsers represent a web page data using the Document Object Model (DOM) and how to develop dynamic, interactive web pages using JavaScript in the browser. Beyond fundamental JavaScript syntax and advanced language features such as callbacks, events, and asynchronous programming, you will work with jQuery, which provides functionality for simplified DOM manipulation and event handling. This course will also introduce you to modern web frameworks and component-based libraries such as React.js for efficiently developing modular web page components, and D3.js for creating data-driven documents. We will also teach you how to represent and exchange data using JavaScript Object Notation (JSON), and how to access RESTful APIs on the web. Server-side JavaScript is becoming more prevalent in the industry, with web frameworks such as Node.js and Express making it simple to create and deploy complex, data-driven web applications. This course will prepare you to use such frameworks and show you how to integrate them with NoSQL databases such as MongoDB.

How do you optimally encode a text file? How do you find shortest paths in a map? How do you design a communication network? How do you route data in a network? What are the limits of efficient computation? This course, part of the Computer Science Essentials for Software Development Professional Certificate program, is an introduction to design and analysis of algorithms, and answers along the way these and many other interesting computational questions. You will learn about algorithms that operate on common data structures, for instance sorting and searching; advanced design and analysis techniques such as dynamic programming and greedy algorithms; advanced graph algorithms such as minimum spanning trees and shortest paths; NP-completeness theory; and approximation algorithms. After completing this course you will be able to design efficient and correct algorithms using sophisticated data structures for complex computational tasks.