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Thomas Berry (1914-2009) was a historian of world religions and an early voice awakening moral sensibilities to the environmental crisis. He is known for articulating a “new story” of the universe that explores the implications of the evolutionary sciences and cultural traditions for creating a flourishing future. This course investigates Berry’s life and thought in relation to the Journey of the Universe project. It draws on his books, articles, and recorded lectures to examine such ideas as: the New Story, the Great Work, and the emerging Ecozoic era. The course explores Berry’s insights into cosmology as a context for locating the human in a dynamic unfolding universe and thus participating in the creative work of our times. In particular, we will examine Berry’s reflections on renewal and reform in the areas of ecology, economics, education, spirituality, and the arts. Course Rationale: Thomas Berry was an original, creative, and comprehensive thinker, especially regarding the critical nature of our global environmental crisis. His intellectual importance resides in his response to the ecological crisis by bringing together the humanities and science in an evolutionary narrative. In addition, he articulated the need for the moral participation of the world religions in addressing environmental issues. He came to this realization largely through his study of cosmologies embedded within religious traditions. Sensing the significance of these stories as “functional cosmologies” he explored the widespread influence that these stories transmitted through a tradition, for example, in rituals, ethics, and subsistence practices.

The Think like a CFO Specialization will help you learn the language of finance. You will gain a firm understanding in Accountability, Operational Finance and Corporate Finance. By the end of the specialization, you’ll know what questions to ask and how to fit the different pieces together in order to develop a diagnosis and action plan to resolve a company’s financing dilemmas. You will have gain the necessary skills to understand basic concepts and feel comfortable reading, interpreting and discussing financial statements for decision making and you will understand the hey financial issues related to companies, investors, and the interaction between them in the capital markets. When you complete the Specialization, you’ll be able to better understand both business financial problems to make better decisions and your own personal financial decisions

This course introduces several tools for processing business data to obtain actionable insight. The most important tool is the mind of the data analyst. Accordingly, in this course, you will explore what it means to have an analytic mindset. You will also practice identifying business problems that can be answered using data analytics. You will then be introduced to various software platforms to extract, transform, and load (ETL) data into tools for conducting exploratory data analytics (EDA). Specifically, you will practice using PowerBI, Alteryx, and RStudio to conduct the ETL and EDA processes. The learning outcomes for this course include: 1. Development of an analytic mindset for approaching business problems. 2. The ability to appraise the value of datasets for addressing business problems using summary statistics and data visualizations. 3. The ability to competently operate business analytic software applications for exploratory data analysis.

The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.

This Specialization introduces the U.S. federal tax system via conceptual and applied material. Learners will be able to apply basic principles to settings involving individuals, corporations, and other business entities, complete key components of major, individual U.S. federal tax returns, and identify tax-related strategies and implications of structuring transactions and organizations.

The Uncommon Sense Teaching Specialization is a foundational program that prepares you to teach based on leading-edge insights from neuroscience, cognitive psychology, and movie-making. Your instructors have real-world experience in teaching some of the world’s most popular courses, and they bring their background from academia and beyond, including perspectives that could be obtained only from decades of teaching at virtually every level, personal experience with neurodiversity, adventure from the Bering Sea to the South Pole Station in Antarctica, and much more. This specialization gives invaluable insight for: Teachers—K12, higher ed, adult learning, vocational trainers College students training to be teachers Professors and others who train teachers how to teach Industry trainers Coaches Tutors Parents, caregivers, and homeschoolers The Uncommon Sense Teaching Specialization provides a pathway for you to bring your teaching skillset to a higher level by helping you gain the knowledge and skills to level up your career or strengthen your teaching foundation—whoever you might be teaching. You can take either MOOC 0 or MOOC 1 first, and enjoy MOOC 2 after MOOC 1. Enjoy! Uncommon Sense Teaching Specialization MOOC 0: Teaching Online MOOC 1: Uncommon Sense Teaching MOOC 2: Uncommon Sense Teaching, Part 2

The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.

Dieser Kurs bietet einen analytischen Rahmen zur strukturierten Beurteilung von Schlüsselproblemen und gibt Ihnen Tools an die Hand, mit denen Sie die Unsicherheiten besser in den Griff bekommen, die in Geschäftsprozessen auftreten und diese komplizieren. Sie werden insbesondere in die Statistik eingeführt und in die Art und Weise, wie man Daten zusammenfasst. Sie erfahren etwas über Häufigkeitskonzepte, Normalverteilung, statistische Untersuchungen, Stichproben und Konfidenzintervalle. Sie werden zwar in einige Gebiete der didaktischen Wissenschaft eingeführt, der Schwerpunkt liegt jedoch auf der Anwendung von Methoden. Dies geschieht mittels Excel und Datensätzen aus vielen verschiedenen Fachrichtungen und ermöglicht Ihnen, etwas über die Anwendung von Statistik in sehr unterschiedlichen Situationen zu lernen. Schwerpunkte des Kurses sind nicht allein die Erklärung dieser Konzepte, sondern ebenfalls das Verständnis der Bedeutung erzielter Resultate. Wenn Sie diesen Kurs erfolgreich absolviert haben, werden Sie in der Lage sein: • umfangreiche Datensätze in grafischer, tabellarischer und numerischer Form zusammenzufassen. • zu verstehen, welche Bedeutung ordnungsgemäße Stichprobenverfahren haben und warum Sie sich auf Stichprobendaten verlassen können. • zu verstehen, warum Normalverteilung in so vielen Situationen angewandt werden kann. • Stichprobeninformationen zu verwenden, um mit einem gewissen Grad an Sicherheit im Hinblick auf die Genauigkeit der Schätzungen Rückschlüsse auf die Population zu ziehen. • Excel für statistische Analysen einzusetzen. Dieser Kurs ist Teil des von der University of Illinois angebotenen iMBA, einem flexiblen, voll anerkannten Online-MBA zu einem unglaublich günstigen Preis. Ausführliche Informationen erhalten Sie auf der Seite „Resources“ in diesem Kurs sowie unter onlinemba.illinois.edu.

This second course in the XR for Everybody specialization looks at how to design new user experiences for XR technologies. The course provides learners with a design thinking mindset and equips them with a fundamental toolbox for creating XR applications using rapid prototyping. It starts with a broad overview of the complete XR design process, discussing different paths to becoming an XR creator, what is generally involved in making XR experiences, and how to best get started. The course then emphasizes design thinking, critiques, and ethics while introducing guidelines and user-centered techniques like design jams. The second half of the course introduces a variety of methods and tools for prototyping both physically and digitally including on the XR devices themselves through immersive authoring. The course has an honors track that guides learners through creating their own storyboard, physical prototype, and digital prototype of an XR experience. You will work on exercises that allow you to practice your design skills and also build XR technologies into your workflow by working with specific templates and new digital tools that allow you to preview physical prototypes on AR/VR devices.

This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.

En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.

This is the first course in the Learn English: Advanced Grammar and Punctuation specialty. In this course, you will review the verb tenses that you learned in beginning English classes and learn about a few tenses you may not know very well. In all, you'll cover all 12 of the English verb tenses and passive verb forms. Most importantly, you'll learn how to blend these tenses, which is what native speakers frequently do. This course is vital to your becoming a more fluent English speaker. Please note that the free version of this class gives you access to all of the instructional videos and handouts. The peer feedback and quizzes are only available in the paid version.

Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash

This is the first course in a Coursera Specialization track involving Web Application Architectures. This course will give you the basic background, terminology and fundamental concepts that you need to understand in order to build modern full stack web applications. A full stack web developer is familiar with each "layer" of the software technologies involved in a web application, including data modeling and database technologies, the web server environment and middleware components, network protocols, the user interface and basic visual design and user interaction concepts. In this course we will learn by doing. We will start by learning the major components of web application architectures, along with the fundamental design patterns and philosophies that are used to organize them. You will build and continually refine a fully functional full-stack web application as we progress through the modules in this course. Along the way you will be exposed to agile software development practices, numerous tools that software engineers are expected to know how to use, and a modern web application development framework. This course is also available in Spanish. To join the Spanish version, visit this page: https://www.coursera.org/learn/apliweb.

The global supply chain is a $50 trillion industry and is the foundation of our global economy. While information technology has improved the flow of goods globally over the last few decades, as the COVID-19 crisis revealed there is still critical work to do. Today’s supply chains are complex, with parties conducting their transactions through a Byzantine network of computer systems with disparate applications like e-mail, phone, and fax. There are invoices, letters of credit, bank guarantees, bills of lading, tax forms, receipts, and other paperwork moving through this complex labyrinth. Payments are made through a hodgepodge of intermediaries, and consumers and supply chain players alike struggle to get accurate information. Enter blockchain—the Internet of Value. For the first time in human history, individuals and organizations can manage and trade their assets digitally peer to peer. These assets can be digital like money, identity, and private information; or they can be physical assets represented by digital tokens. Parties to a transaction achieve trust not necessarily through an intermediary, but through cryptography and clever code. In this course, you will learn how blockchain can improve global supply chains by streamlining processes, reducing costs and fraud, improving supply-chain visibility, authenticating assets and their origins, and ultimately fuelling product innovation. Development of this course was made possible thanks to the generous support of FedEx. Dale Chrystie, Business Fellow & Blockchain Strategist at FedEx, appears in several of the course videos, bringing a real-world industry perspective to the course. The scripts for these videos have been written and/or reviewed by the Blockchain Research Institute and approved by INSEAD to ensure that this content is pedagogically sound, unbiased, and academically rigorous.

Global commerce has grown in complexity and magnitude over millennia, but its processes remain relatively unchanged. Enter blockchain—the Internet of Value. For the first time in human history, individuals and organizations can manage and trade their assets digitally, peer to peer. This four-course Specialization introduces you to the world of blockchain technology for global commerce—explaining what blockchain is, how it works, and why it is revolutionary. You will learn about various categories of digital assets and the ways they can be transacted on a blockchain. You will learn how blockchain is disrupting global supply chains, including how it will transform the practices of operations, logistics, procurement and purchasing, transportation, customs and border control, trade finance and insurance, manufacturing, and inventory management—making the whole network more adaptable and responsive to demand fluctuations and crisis. You will also learn about industry consortia and the idea of coopetition—a strategy of cooperating with competitors for defined purposes to cultivate a new economic ecosystem that benefits all participants. Development of this Specialization was made possible thanks to the generous support of FedEx. All content for this Specialization has been written and/or reviewed by the Blockchain Research Institute and approved by INSEAD to ensure it is pedagogically sound, unbiased, and academically rigorous.

With its roots in ancient cultures as early as 6,000 BC, wine has long been part of the human experience. Today, increasing worldwide interest in wine and its complexities and varieties has created an international market valued at $304 billion annually. For the learner who is just getting started in the study of wine, this course will help you take the first steps toward understanding the physiological process of wine tasting. The first module will explore types of wine and help you set up your own tasting. You’ll also begin to build a sensory vocabulary. In module 2 you will review classic wine types of the world and set up flights of specific wines. Later we’ll examine the various faults and defects that can appear in wine as well as techniques for spotting problems. In week 4 we’ll delve into the complex world of wine and food pairings, and its “seven deadly sins.” The course will culminate in a peer review project in which you will apply the knowledge and sensory techniques you have developed throughout the course to assess a specific wine.

By the end of the specialization, you will be able to: Lead others by influencing their performance and understanding through effective communication skills. Send and understand messages online, mitigating miscommunication and inappropriate emotional messages. Use different tactics to eliminate or avoid common biases in group settings. Demonstrate empathy while listening to others, leading and reflecting on oneself and one’s actions.

Start creating your world. A game world is not just a backdrop for your game—be it minimal or detailed, contained or part of a much bigger universe, it provides the context for your player. Ultimately, a game world should feel alive and wholly unique to any player who will experience it. In this course, we will explore game worlds in existing games and study the art and influences that inform their themes and styles. We will also investigate key components of environment and level design as well as strategies designers use to define gameplay or advance it. We’ll also look at navigation and the elements that make your world as real (or unreal) as you want it to be. A weekly challenge will prompt you to explore styles and inspirations for possible game worlds, and you’ll learn effective ways to communicate your ideas from concepts to presentation-worthy proofs of concept.

Engineering leaders must write extensively for their jobs, and their writing needs to be professional, reader-focused, and error-free. What leaders write in particular can affect productivity and the bottom line, so the written communication that leaders create must be clear and concise. In this course, you’ll learn essential writing skills that you can apply in your daily activities on the job as an engineering leader. You’ll learn key principles in • Approaching various engineering genres • Using the writing process to create quality documents • Making your writing structured • Making your writing clear and concise • Handling style, tone, and voice Selected materials courtesy of Communication Faculty at Rice University - all rights reserved.

iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.

El curso en línea abierto y masivo (Massive Open Online Course, MOOC) “¿Qué son los medios sociales?” está destinado a empresarios, ejecutivos y profesionales del marketing que quieren mejorar considerablemente sus capacidades para potenciar su estrategia en los medios sociales con métodos efectivos y eficaces. Con este curso práctico y útil, no solo aprenderás cómo usar los medios sociales para fortalecer tu identidad profesional,sino que también llevarás a la práctica lo que aprendiste. Este es el primero de los seis cursos del programa de especialización “Marketing en los medios sociales: Cómo obtener ganancias en un mundo digital”. Si bien se puede asistir de forma gratuita, los participantes que paguen el curso recibirán más recursos además de los básicos. Para el MOOC 1, el material incluye un video especial de Alessandro Acquisti sobre macrodatos y un conjunto de estudios realizados por IBM sobre estrategias de participación y de marketing en los medios sociales con obtención de ganancias netas. Diseñamos este curso para darte las herramientas, la información, los conocimientos y las habilidades para que logres potenciar tu organización de inmediato. Además, te ayudaremos a ponerte en contacto con expertos reconocidos en los medios sociales. Después de realizar el curso, tú y los miembros de tu organización podrán posicionarse, interactuar y establecer relaciones con los consumidores de mayor valor para ustedes. Actualmente, estamos atravesando un período de muchísimas innovaciones. Las nuevas tecnologías están cambiando la forma en la que las personas se relacionan entre sí y con las organizaciones que les interesan. Este curso te mostrará el camino que debes seguir para definir tu propia estrategia de marketing en los medios sociales con métodos efectivos y eficaces. Algunos de los profesores del MOOC 1 son: * Judy Ungar Franks (presidenta de The Marketing Democracy, Ltd., y profesora de Comunicación integrada de marketing en la Escuela Medill (Medill School) de la Universidad Northwestern (Northwestern University)

Использование Excel широко распространено в отрасли. Это очень мощный инструмент анализа данных, и почти все крупные и малые предприятия используют Excel в своей повседневной работе. Это вводный курс по использованию Excel, который предоставляет знания о работе в Excel с целью ее использования для более продвинутых тем деловой статистики позже. Курс разработан с учетом двух типов учащихся — тех, у кого очень мало функциональных знаний о Excel, и тех, кто регулярно использует Excel, но на периферийном уровне и хочет развить свои навыки. Курс включает в себя базовые операции, такие как импорт данных в Excel с использованием различных форматов данных, организацию и управление данными, а также некоторые более продвинутые функции Excel. В целом, знакомство с функциями Excel проходит на базе простых для понимания примеров, которые демонстрируются таким образом, чтобы учащимся не составило труда понять и применить их. Для успешного прохождения курсов учащиеся должны иметь доступ к Microsoft Excel 2010 или более поздней версии для Windows. ________________________________________ НЕДЕЛЯ 1 Модуль 1: Введение в электронные таблицы В этом модуле вы познакомитесь с использованием электронных таблиц Excel и различными базовыми функциями данных Excel. Рассматриваемые темы включают: • Импорт данных в Excel с использованием различных форматов • Основные функции в Excel, арифметические и различные логические функции • Форматирование строк и столбцов • Использование формул в Excel и их копирование с использованием абсолютных и относительных ссылок ________________________________________ НЕДЕЛЯ 2 Модуль 2: Функции электронной таблицы для организации данных В этом модуле представлены различные функции Excel для организации и запроса данных. Участники знакомятся с функциями ЕСЛИ, ВПР и ГПР и вложенной функцией ЕСЛИ в Excel. Рассматриваемые темы включают: • Функция ЕСЛИ и вложенная функция ЕСЛИ • ВПР и ГПР • Функция СЛУЧМЕЖДУ ________________________________________ НЕДЕЛЯ 3 Модуль 3: Введение в фильтры, сводные таблицы и диаграммы В этом модуле представлены различные возможности фильтрации данных в Excel. Вы узнаете, как настраивать фильтры для избирательного доступа к данным. Также объясняется очень мощный инструмент обобщения данных, сводная таблица, и мы начинаем вводить функцию построения диаграмм в Excel. Рассматриваемые темы включают: • ВПР и рабочие листы • Фильтрация данных в Excel • Использование сводных таблиц с категориальными и числовыми данными • Введение в возможности построения диаграмм в Excel ________________________________________ НЕДЕЛЯ 4 Модуль 4: Расширенные возможности построения графиков и диаграмм В этом модуле рассматриваются различные продвинутые методы построения графиков и диаграмм, доступные в Excel. Начиная с различных линейных, столбчатых и круговых диаграмм, мы представляем сводные диаграммы, точечные графики и гистограммы. Вы научитесь понимать эти различные диаграммы и начнете строить их самостоятельно. Рассматриваемые темы включают: • Линейные, столбчатые и круговые диаграммы • Сводные диаграммы • Точечные графики • Гистограммы

Этот курс предоставляет аналитическую основу, позволяющую структурировано оценивать ключевые проблемы, а также инструменты, которые помогают управлять факторами неопределенности, затрагивающими и усложняющими бизнес-процессы. В частности, вы ознакомитесь с основами статистики и методами сведения данных, а также изучите такие понятия, как частота, нормальное распределение, статистическое исследование, выборка и доверительный интервал. Небольшая часть курса посвящена теоретическим основам предмета, но наша основная цель заключается в применении изучаемых методик. С помощью программы Excel и наборов данных из множества дисциплин, мы наглядно рассмотрим принципы использования статистики в различном контексте. На курсе вы не только ознакомитесь с указанными понятиями, но и научитесь анализировать полученные результаты. После успешного прохождения курса вы сможете: • Сводить большие наборы данных в графической, табличной и числовой форме. • Понимать, чем важна правильная выборка и почему ее результатам можно доверять. • Понимать, почему нормальное распределение можно использовать в множестве различных ситуаций. • Использовать данные выборки, чтобы делать выводы о генеральной совокупности с определенным уровнем достоверности оценки. • Использовать программу Excel для выполнения статистического анализа. Этот курс является частью программы iMBA Иллинойсского университета. Это полностью аккредитованная, представленная в удобной форме онлайн-программа MBA по доступной цене. Дополнительные сведения см. на странице "Ресурсы" этого курса или на сайте университета onlinemba.illinois.edu.

В этом курсе рассматривается, как цифровые инструменты, такие как Интернет, смартфоны и 3D-печать, преобразуют мир маркетинга, меняя роли и подходы как фирм, так и потребителей. Маркетинг в цифровом мире — это один из самых популярных курсов Coursera; у него более чем 250 000 слушателей, и он получил от Class Central звание одного из 50 лучших МООК всех времен (https://www.class-central.com/report/top-moocs/). Этот курс был разработан в 2015 году и обновлен в апреле 2020 года. Он входит в состав программы iMBA, предлагаемой университетом Иллинойса — гибкой, полностью аккредитованной программы онлайн-MBA по невероятно дешевой цене. За дополнительной информацией обращайтесь на страницу ресурсов в этом курсе и на сайт onlinemba.illinois.edu.

В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.

في هذه الدورة التدريبية، ستتعلم أسس القيادة اليومية واتخاذ القرارات الفردية والجماعية وإدارة التحفيز. تتمثل الأهداف في معرفة سبب الأهمية البالغة لمهارات القيادة لنجاح المنظمة ومعرفة كيفية ذلك، بجانب تعلّم أسس مهارات القيادة الفعّالة.\n\nيستفيد المتعلمون:\n•\tمعرفة سبب أهمية مهارات القيادة للنجاح المؤسسي وكيفية تطبيقها.\n•\tمعرفة كيفية استخدام مهارات القيادة للعمل بشكل أكثر فعاليّة مع الآخرين، والقدرة على تنظيم مجموعات للعمل معًا بشكل أكثر فعاليّة.\n•\tالقدرة على تطبيق أسس مهارات القيادة الفعّالة أثناء مواجهة المواقف اليومية التي يتعرض لها القادة.\n\nتُعد هذه الدورة التدريبية جزءًا من شهادة الماجستير الدولية في إدارة الأعمال iMBA التي تُقدمّها جامعة إلينوي، وهي عبارة عن شهادة ماجستير في إدارة الأعمال MBA عبر الإنترنت سلسة ومعتمدة بالكامل بسعر مُنافس ومُذهل. لمزيد من المعلومات، يُرجى الاطلاع على صفحة "الموارد" في هذه الدورة التدريبية وعلى onlinemba.illinois.edu.

تَعلّم التعليمات البرمجية في Java، وحسّن برمجتك ومهارات حل المشكلات. ستتعلم تصميم الخوارزميات وأيضًا تطوير البرامج وتصحيحها. استخدام فئات مخصصة مفتوحة المصدر، ستكتب برامج بإمكانها الوصول إلى الصور ومواقع الويب وأنواع أخرى من البيانات وتحولها. في نهاية الدورة ستنشئ برنامجًا يُحدد شهرة أسماء المواليد المختلفة في US بمرور الوقت من خلال تحليل ملفات القيمة المفصولة بفاصلة (CSV). بعد الانتهاء من الدورة ستتمكن من: 1. تحرير برنامج Java وتحويله برمجيًا وتشغيله، 2. استخدام العبارات الشرطية وحلقات التكرار في برنامج Java، 3. استخدام وثائق Java API في كتابة البرامج. 4. تصحيح برنامج Java باستخدام أسلوب علمي، 5. كتابة أسلوب Java لحل مشكلة معينة، 6. تطوير مجموعة من حالات الاختبار كجزء من تطوير البرنامج، 7. إنشاء فئة باستخدام عدة أساليب تعمل معًا لحل المشكلة، و 8. استخدام تقنيات تصميم فرق تسد لبرنامج يستخدم عدة أساليب.

تمتلك المؤسسات صغيرها وكبيرها كميات هائلة من البيانات عن اختيارات العملاء. ولكن هذا القدر الغزير من المعلومات لا يمكن استخلاص تفسيرات منه دائمًا لصنع قرارات أفضل. ويتمثل التحدي في معرفة طريقة تفسير البيانات مع الوضع بعين الاعتبار التوقع بتزايد استخدام المسوقين للتحليلات لأخذ قرارات مبررة عن علم. تسمح تحليلات السوق للمسوقين بقياس أداء التسويق وإدارته وتحليله لتعظيم فعاليته وتحسين العائد على الاستثمار. بالإضافة إلى التطبيقات الغنية عن الذكر المعنية بالمبيعات وجذب العملاء المحتملين، تقدم تحليلات التسويق رؤى عميقة حول تفضيلات العملاء واتجاهاتهم من أجل استخدامها بشكل أفضل في قرارات التسويق والأعمال المستقبلية. تمنحك هذه الدورة التدريبية التي طورتها كلية داردن لإدارة الأعمال (Darden School of Business) بجامعة فيرجينيا (University of Virginia)، الأدوات اللازمة لقياس أصول العلامة التجارية والعملاء، وفهم تحليل الانحدار، وتصميم التجارب على سبيل تقييم الحملات التسويقية وتحسينها. بعد إتمام هذه الدورة التدريبية، سيترسخ فهمك لطريقة استخدام تحليلات التسويق للتنبؤ بالنتائج وتخصيص الموارد بشكل منهجي. يمكنك متابعة منشوراتي على تويتر باسم @rajkumarvenk وعلى https://www.linkedin.com/in/education-marketing. مع جزيل الشكر، راج/Raj أستاذ إدارة الأعمال في كلية داردن

هدف هذا التخصص إلى إعداد الطلاب للدراسة الجامعية في جامعة ناطقة باللغة الإنجليزية. تزودك الدورة التدريبية بالمشاركة الكاملة والانخراط في دراستك، من خلال بناء الوعي والفهم للقيم الأساسية وتوقعات الثقافة الأكاديمية، وتزويدك بإستراتيجيات عملية لتطبيقها على دراساتك. في هذه الدورة التدريبية، ستتعلم كيفية تطوير مهارات محو الأمية المعلوماتية والرقمية لديك، لمساعدتك في تحقيق النجاح في دراساتك الجامعية. بعد الانتهاء من هذه الدورة التدريبية، ستكون قادراً على: 1. الوصول إلى المعلومات والبحث عنها بكفاءة وفعالية باستخدام مجموعة متنوعة من الأدوات الرقمية. 2. تقييم نقدي لمصداقية المصادر لسياق أكاديمي. 3. تصفية المعلومات، وإدارتها، وتنظيمها من مجموعة متنوعة من المصادر لاستخدامها في الدراسة الأكاديمية. 4. إظهار الوعي بالقضايا الأخلاقية المتعلقة بالنزاهة الأكاديمية المحيطة بالوصول إلى المعلومات واستخدامها. 5. فهم كيفية استخدام الأدوات الرقمية للإحالة والإسناد من أجل تجنب الانتحال. 6. فهم كيفية نشر المعلومات وإيصالها بطريقة مهنية، بما فيه إدارة الهوية الرقمية، وبناء شبكات للتعلم والبحث.

学习者需要选择一个（由华为公司提供的）实战商业机会，或者自己工作中的例子，仔细地评估，并清晰地表述给同学、沃顿教职员以及相关行业合作伙伴高管。 学习目标： 分析一个现实商业机会的收益与风险 提出一套如何利用这个商机的策略 准确并清晰地与他人交流你的分析和方案。学习者需要提供一个战略计划，以幻灯片或者视频的形式，展示针对这个商机的”市场进入”战略。 学习者需要明确商机的意义，分析其主要收益和风险，并提供一个“市场进入”方案，包含以下关键方面： 市场营销：针对新的产品，设计一个以消费者中心的传播/市场营销计划。 金融/财务：针对可行的项目，设计一个金融模型（比如净现金流模型），包含对成本和收益的预测。 运营管理：针对新项目，构建一个生产以及（或者）服务计划。

Este curso irá aprimorar sua capacidade de influenciar pessoas em situações nas quais não é possível recorrer à autoridade formal. Você aprenderá formas eficazes de criar, desenvolver e manter uma base de poder em sua organização. Você aprenderá, também, táticas de influência com as quais estará apto a ser mais persuasivo e influente no trabalho com seus superiores, colegas e até mesmo subordinados. Além disso, você aprenderá a criar e manter relações de alta qualidade para aumentar ainda mais o seu poder informal e a capacidade de influenciar pessoas. Acima de tudo, você aprenderá a estabelecer a diferença entre influência e manipulação e proteger-se de influências indesejadas. As estratégias de influência estudadas neste curso o tornarão um líder, apresentador e tomador de decisões mais confiante e influente. Você será mais eficaz ao vender ideias de negócios aos seus superiores, influenciar clientes e formar alianças com partes interessadas. Com este curso, você não apenas obterá orientações estratégicas sobre como desenvolver e manter sua rede de influência e poder, mas também se equipará com táticas e estratégias específicas comprovadamente eficazes para conquistar o poder e influenciar pessoas.

En este curso, aprenderá a crear una visión compartida para su equipo y a comunicarla con eficacia a sus compañeros. También aprenderá a fijar metas y expectativas eficaces de un modo que le permita a su equipo alcanzar esa visión compartida. Por último, comprenderá las necesidades y los factores de desempeño más importantes en todas las culturas y aprenderá a alinear incentivos con conductas deseadas, para motivar a sus compañeros a alcanzar los objetivos del equipo. La motivación es un desafío esencial en las organizaciones modernas. Una encuesta reciente de Gallup reveló que solo el 13 % de los trabajadores de todo el mundo exhiben niveles altos de compromiso y motivación. Le mostraremos por qué estos problemas de motivación no solo se deben a miembros de equipo “desmotivados” o “de bajo desempeño”. Por el contrario, la motivación se basa en las condiciones laborales que creamos para nuestros compañeros de equipo, la forma en que estructuramos metas y objetivos, y la manera en que recompensamos el logro de esas metas. Este curso lo ayudará a diagnosticar y resolver problemas de motivación, para que pueda obtener el máximo desempeño de las personas.

In this course, you’ll learn the basic fundamentals of corporate finance. Based on the pre-term qualifying courses for Wharton MBA students, Professor Jessica Wachter has designed this course for learners who need a refresher in financial concepts, or for those who are learning about corporate finance for the first time. You’ll identify foundational concepts in corporate finance, such as NPV, Compound and Simple Interest, and Annuities versus Perpetuities. You’ll also learn how to apply the NPV framework to calculating fixed-income valuation and Equity, using hypothetical examples of corporate projects. By the end of this course, you’ll have honed your skills in calculating risk and returns to optimize investments, and be able to assess the right set of financial information to achieve better returns for your firm.

This course will improve your ability to influence people in situations where you cannot use formal authority. You will learn about effective ways to build, develop, and sustain a power base in your organization. You will also learn influence tactics that enable you to be more persuasive and influential in working with your superiors, peers, and even subordinates. In addition, you will learn how to build and maintain high-quality relationships to further maximize your informal power and ability to influence others. Importantly, you will distinguish between influence and manipulation and learn how to protect yourself from the unwanted influence of others. The influence strategies you learn in this course will make you a more confident and influential leader, presenter, and decision-maker. You will more effective in pitching business ideas to your superiors, influencing customers, and building coalitions across stakeholders. This course will not only give you strategic guidance on how to develop and maintain your network for influence and power, but we will also equip you with specific tactics and strategies that are proven to work for gaining power and influencing people.

Este curso mejorará su capacidad para influir en las personas en situaciones en las que no puede usar autoridad formal. Aprenderá formas efectivas de construir, desarrollar y mantener una base de poder en su organización. También aprenderá tácticas de influencia que le permitirán ser más persuasivo e influyente al trabajar con sus superiores, compañeros e incluso subordinados. Además, aprenderá cómo construir y mantener relaciones de alta calidad para maximizar aún más su poder informal y su capacidad de influir en los demás. Es importante destacar que distinguirá entre influencia y manipulación y aprenderá a protegerse de la influencia no deseada de los demás. Las estrategias de influencia que aprenda en este curso lo convertirán en un líder, presentador y encargado de tomar decisiones más seguro e influyente. Será más efectivo a la hora de presentar ideas comerciales a sus superiores, influir en los clientes y crear coaliciones entre las partes interesadas. Este curso no solo le brindará orientación estratégica sobre cómo desarrollar y mantener su red de contactos para la influencia y el poder, sino que también le brindaremos tácticas y estrategias específicas que han demostrado funcionar para obtener poder e influir en las personas.

In this course, you will learn how to build your team, improve teamwork and collaboration, and sustain team performance through continuous learning and improvement. Specifically, you will learn best practices for composing a team and aligning individual and team goals. You will also learn how to establish roles, build structures, and manage decision making so that your team excels. This course will also help you manage critical team processes such as conflict resolution and building trust that have a profound impact on your team’s performance. You will discuss some of the best ways to harness the productive potential of teams while mitigating the risks and traps of teamwork. In modern organization, most of work is done in teams, yet the results of teamwork are exceptionally mixed. Many teams are poorly designed and structured, fraught with dysfunctional conflict, experience coordination breakdowns and serious motivation challenges. As a result, many teams fail to realize their potential and frequently underperform even individuals working on similar tasks. After completing this course, you will acquire a set of tools and practices that enable you to effectively set up, run, evaluate, and continuously improve your team. Such insights will both make you a more effective team leader but also a standout contributor in team settings.

Neste curso, você aprenderá como construir sua equipe, melhorar o trabalho em equipe e a colaboração e sustentar o desempenho da equipe por meio de aprendizado e melhoria contínuos. Especificamente, você aprenderá as melhores práticas para compor uma equipe e alinhar as metas individuais e da equipe. Você também aprenderá como estabelecer funções, criar estruturas e gerenciar a tomada de decisões para que sua equipe se sobressaia. Este curso também ajudará você a gerenciar processos críticos da equipe, como solucionar conflitos e construir confiança que têm um impacto profundo no desempenho da sua equipe. Você discutirá algumas das melhores formas de aproveitar o potencial produtivo das equipes, enquanto reduz os riscos e as armadilhas do trabalho em equipe. Na organização moderna, a maior parte do trabalho é feita em equipes, mas os resultados do trabalho em equipe são excepcionalmente mistos. Muitas equipes são mal projetadas e estruturadas, repletas de conflitos disfuncionais, problemas de coordenação de experiências e sérios desafios de motivação. Como resultado, muitas equipes não conseguem perceber o seu potencial e, frequentemente, têm baixo desempenho, até mesmo em indivíduos que trabalham em tarefas semelhantes. Depois de concluir este curso, você adquirirá um conjunto de ferramentas e práticas que permitirão que você configure, execute, avalie e melhore continuamente sua equipe de forma eficaz. Esses insights tornarão você um líder de equipe mais eficaz, e também um colaborador de destaque ao estabelecer equipes.

The course builds on my Introduction to Financial Accounting course, which you should complete first. In this course, you will learn how to read, understand, and analyze most of the information provided by companies in their financial statements. These skills will help you make more informed decisions using financial information.

What does it mean to truly change something? How does one persuade others to change? How do we reduce roadblocks to change? In this course, you’ll learn about the barriers to change and how to become more effective in inspiring change within others and your organization. Professor Jonah Berger of the Wharton School has designed this course to help you understand the REDUCE framework, and shows you how to develop your skills of persuasion and influence. By the end of this course, you’ll know both the strengths and weaknesses of certain strategies for removing barriers in change, plus you'll learn how to leverage those strategies to achieve change in both business and in life.

Ever wondered why some things become popular, and other don't? Why some products become hits while others flop? Why some ideas take off while others languish? What are the key ideas behind viral marketing? This course explains how things catch on and helps you apply these ideas to be more effective at marketing your ideas, brands, or products. You'll learn how to make ideas stick, how to increase your influence, how to generate more word of mouth, and how to use the power of social networks to spread information and influence. Drawing on principles from his best-selling book, "Contagious: Why Things Catch On," Professor Jonah Berger illustrates successful strategies for you to use buzz to create virality so that your campaigns become more shareable on social media and elsewhere. By the end of this course, you'll have a better understanding of how to craft contagious content, build stickier messages, and get any product, idea, or behavior to catch on.

ستتعلّم في هذه الدورة التدريبية أفضل الممارسات لاختيار المواهب وتوظيفها وتأهيلها. ستتعرّف أيضًا على الأساليب الرئيسية لقياس الأداء وتقييم موظفيك. بالإضافة إلى ذلك، ستتعلم كيفية تنمية قدرات الموهوبين وتدريبهم بحيث يمكنهم إدراك إمكاناتهم الكاملة في العمل. بشكل إجمالي، ستكتسب فهمًا شاملاً للدورة الكاملة لإدارة المواهب وإنشاء قناة إمداد مواهب قوية لفريقك ومؤسستك. تعد إدارة المواهب وتنمية قدراتهم واحدةً من أهم ثلاث قضايا تشغل عقول الرؤساء التنفيذيين حول العالم. في الواقع، يشير الرؤساء التنفيذيون إلى إدارة وتنمية قدرات مواهبهم القيادية باعتبارها المسألة الأكثر أهمية لنجاح أعمالهم في المستقبل، ولكن مؤسساتهم أقل قدرةً على معالجتها بفعالية. ستزودك هذه الدورة التدريبية برؤى وأطر عمل وأدوات لإدارة وتنمية قدرات المواهب في فرقك ومؤسساتك بفعالية.

This course describes the traits of Great Leaders who combine fierce resolve with personal humility. Indeed, they might be described more as “plow horses” as opposed to “show horses”. They see themselves as servants to the team and to the organization. They “hold the line” when faced with tough decisions and “do what must be done” when the time comes. Their leadership is based on solid ethical principles and they act with quiet, calm determination. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.

In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.

This is the sixth course in the IBM AI Enterprise Workflow Certification specialization.   You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.     This course focuses on models in production at a hypothetical streaming media company.  There is an introduction to IBM Watson Machine Learning.  You will build your own API in a Docker container and learn how to manage containers with Kubernetes.  The course also introduces  several other tools in the IBM ecosystem designed to help deploy or maintain models in production.  The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow.   By the end of this course you will be able to: 1.  Use Docker to deploy a flask application 2.  Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3.  Discuss basic Kubernetes terminology 4.  Deploy a scalable web application on Kubernetes  5.  Discuss the different feedback loops in AI workflow 6.  Discuss the use of unit testing in the context of model production 7.  Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.   What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

This is the fifth course in the IBM AI Enterprise Workflow Certification specialization.   You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises.  Apache Spark is a very commonly used framework for running machine learning models.  Best practices for using Spark will be covered in this course.  Best practices for data manipulation, model training, and model tuning will also be covered.  The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies.   By the end of this course you will be able to: 1.  Use Apache Spark's RDDs, dataframes, and a pipeline 2.  Employ spark-submit scripts to interface with Spark environments 3.  Explain how collaborative filtering and content-based filtering work 4.  Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5.  Analyze hyperparameters in machine learning models on Apache Spark 6.  Deploy machine learning algorithms using the Apache Spark machine learning interface 7.  Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

This is the fourth course in the IBM AI Enterprise Workflow Certification specialization.    You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.  Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company.  The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge.  The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks.  Out-of-the-box Watson models for natural language understanding and visual recognition will be used.  There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines.   By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.   What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

Being a successful leader in complex technical organization requires being ultimately accountable for your team’s performance and meeting commitments to all your stakeholders. This second Leading Technical Organizations course, "Accountability and Employee Engagement", explores how organizational leaders use different decision-making processes for different situations and that they are ultimately accountable for all results. You will also learn how a company’s culture affects strategy, risk and meeting stakeholder commitments. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

The goal of this third course in the Project Management Specialization examines the philosophy and process of managing projects using Agile project management. Students in this course will learn the Agile philosophy and process including the Scrum framework, sprints, and user stories. Upon completion of this course, you will be able to distinguish between predictive and agile project management methodologies and understand the benefits of delivering value early in an engineering project. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Logo image courtesy of Alex Lion, available on Unsplash at https://unsplash.com/photos/qOvxHjOrx4k

This course introduces students to marketing analytics through a wide range of analytical tools and approaches. We will discuss causal analysis, survey analysis using regression, textual analysis (sentiment analysis), and network analysis. This course aims to provide the foundation required to make better marketing decisions by analyzing multiple types of data related to customer satisfaction.

Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization.  You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites.  Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning.  A hypothetical streaming media company will be introduced as your new client.  You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects.  You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking.  Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks.   By the end of this course you should be able to: 1.  Know the advantages of carrying out data science using a structured process 2.  Describe how the stages of design thinking correspond to the AI enterprise workflow 3.  Discuss several strategies used to prioritize business opportunities 4.  Explain where data science and data engineering have the most overlap in the AI workflow 5.  Explain the purpose of testing in data ingestion  6.  Describe the use case for sparse matrices as a target destination for data ingestion  7.  Know the initial steps that can be taken towards automation of data ingestion pipelines   Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.   What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.

The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst. Your job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand. Please remember that this is a Capstone, and has a degree of difficulty/ambiguity higher than the previous four courses. The aim being to mimic a real life application as close as possible.

When is it good business practice to invest for social good? What are the most innovative and effective business strategies for developing positive social impact around the world? Designed by renowned Wharton professors Katherine Klein and Chris Geczy to help individuals, organizations, and investors bring about societal change, this course introduces the fundamentals of impact investing, and developing a business strategy that drives social impact. You’ll learn how to become a leader who cultivates purpose and inspires change, measure societal impact through evidence-based models, and invest in ventures effectively and meaningfully. By the end of this course, you’ll have a deep understanding of the realities of leading an organization with purpose, and be able to build successful strategies that bring impactful change to the world.

In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.

In the cloud networking course, we will see what the network needs to do to enable cloud computing. We will explore current practice by talking to leading industry experts, as well as looking into interesting new research that might shape the cloud network’s future. This course will allow us to explore in-depth the challenges for cloud networking—how do we build a network infrastructure that provides the agility to deploy virtual networks on a shared infrastructure, that enables both efficient transfer of big data and low latency communication, and that enables applications to be federated across countries and continents? Examining how these objectives are met will set the stage for the rest of the course. This course places an emphasis on both operations and design rationale—i.e., how things work and why they were designed this way. We're excited to start the course with you and take a look inside what has become the critical communications infrastructure for many applications today.

This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.

In this course you will learn how to use key finance principles to understand and measure business success and to identify and promote true value creation. You will learn how to use accounting information to form key financial ratios to measure a company’s financial health and to manage a company's short-term and long-term liquidity needs. You will also learn how to use valuation techniques to make sound business investment and acquisition decisions. Finally, you will learn how to incorporate risk and uncertainty into investment decisions and how to evaluate the performance of existing investments. Upon successful completion of this course you will be able to: • Understand how companies make investment decisions that create value for shareholders • Use accounting statements to measure the financial health of a company • Forecast and manage a company’s short- and long-term liquidity needs • Measure the contribution of a new project or acquisition to shareholder value • Incorporate risk into investment decisions using the appropriate discount rates • Evaluate the performance of a company or divisions of a company This course was previously entitled Financial Evaluation and Strategy: Corporate Finance. The course received an average rating of 4.7 out of 5 based on 177 reviews over the period of September 2015 through August 2016. A detailed breakdown of ratings and reviews received for the prior version of the course, which is identical in content to the current course, is provided in the course orientation page. This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.