This course is ideal for marketing beginners. You will learn to identify the ideal clients to target with your marketing, estimate your company's target market, and get exposed to some of the most brilliant thinkers in the field of marketing - all in a beginner-friendly way that is easy to understand.
This course is made especially for beginners because marketing can feel daunting in the beginning. There are so many buzz words and different marketing websites that it's easy to get lost and confused. That's why this course focuses on the core fundamentals.
Invest in your future! Enroll today!
This course equips learners with the skills to analyze dynamic relationships within systems, create causal loop diagrams, and simulate the behavior of interconnected variables. Participants gain insights into system behavior, enabling them to make informed decisions and design effective interventions in diverse contexts.
GenAI Summarization with Langchain: Summarize Text Documents
In this guided project, you will learn the art of text summarization using the well-known GenAI framework, Langchain, and transform it into a practical, real-world web application with Streamlit. During this hands-on experience, we will encapsulate vast volumes of text into concise and coherent summaries by harnessing the power of GPT models and prompt template resided within the Langchain framework.
You will familiarize yourself with Langchain's architecture, it's underlying components and how they can be integrated with a summarizer function. Additionally, you'll learn how to integrate Langchain-powered summarization capabilities into a user-friendly, interactive web app, making your summarization skills accessible to a broader audience. This course is aimed at Python developers who are looking to get started with text summarization using GenAI. Familiarity with Python programming language including skills in creating lists, arrays, and functions is essential. Some experience with LLM interactions and prompt engineering would be useful as would some prior use of Streamlit.
Transform your Asana workspace into a fortress of security and compliance excellence! This Short Course was created to help project management professionals accomplish secure collaboration governance in modern work environments. By completing this course, you'll master the critical skills needed to configure bulletproof access controls, audit permissions like a security expert, and maintain compliance standards that protect your organization's most sensitive data.
By the end of this course, you will be able to:
Configure role-based access (Admin, Member, Guest) across workspaces and external collaborators
Audit permission logs, correct deviations, and file comprehensive compliance reports
This course is unique because it bridges technical security implementation with practical compliance management, giving you both the hands-on skills and analytical framework needed for real-world governance challenges.
To be successful in this project, you should have a background in basic project management principles and Asana workspace navigation.
알고리즘의 영향력은 점차 커지고 있습니다. 머신 러닝은 방대한 데이터 세트를 기반으로 주요 의사결정을 내리기 시작함에 따라, 인간인 우리는 실제 생활에서의 한계점을 충분히 알고 있어야 합니다. 대출 승인, 교통 경로 재설정을 막론하고, 머신 러닝 모델에는 인간의 공유가치가 정확히 반영되어야 합니다. 본 강좌에서는 가장 기본적인 알고리즘부터 완전 자율 알고리즘에 이르기까지 알고리즘의 발전을 살펴보고, 보다 윤리적으로 건전한 알고리즘을 만드는 방법을 논의합니다.
Every leader has to manage change and navigate crises. The key to success is knowing how to guide your team through uncertainty and help them emerge stronger. In this course, you'll develop essential skills to build resilience, lead change initiatives, and navigate crisis situations with confidence. You'll learn research-backed strategies to address resistance, communicate effectively during uncertainty, and support teams through recovery. By developing your ability to adapt quickly and transform challenges into opportunities, you'll help your organization not just survive but thrive in volatile environments.
By the end of this course, learners will be able to lead teams successfully through change and crisis by building organizational resilience, executing strategic improvement initiatives, and applying compassionate leadership techniques to navigate uncertainty while maintaining high performance and team cohesion.
This sequence of four courses will propose a multi-disciplinary approach to the study of Chinese cultural history conceived of as a succession of modes of rationality (philosophical, bureaucratic, and economic). The focus will be on the moments of paradigm shift from one mode of rationality to another. For each of these moments, cultural facts and artifacts—thought, literature, ritual—will be examined in relationship to changing social, political, and economic systems.
The first two courses will cover the periods of the Warring States (481-256 BCE) and the Period of Division (220-589 CE), with a brief excursion into the Han (206 BCE-220 CE). The Warring States laid the social and cultural foundations for the emergence of the imperial mode of rationality; the Period of Division saw the Buddhist “conquest” of China and the emergence of a rationality defined by the opposition of the Three Teachings to shamanism, that is, of a clear contrast between elite and popular culture.
The third and fourth courses will focus on the emergence of modern China in the Song-Yuan (960-1368) and of today’s China 1850 to the present. We will see how the modern attack on religion, redefined as "superstition", led not only to religious reform movements but also to a society in which science and the nation became the primary value systems promoted by the state.
The courses are listed below:
A Critical Cultural History of China - Early China I: Intellectual Change in the Warring States and Han (481 BCE-220 CE)
A Critical Cultural History of China - Early China II: Religious Transformation in the Period of Division (220-589 CE)
A Critical Cultural History of China - Modern China I: Religion and Thought in the Song, Jin, and Yuan (960-1368)
A Critical Cultural History of China - Modern China II: Structuring Values (1850-2015)
This course explores the technological infrastructure that powers modern smart stadiums and connected venues. Learners will examine the digital architecture required to support large scale, data driven stadium operations, from high capacity connectivity networks to integrated platform ecosystems. The course begins with the foundations of digital venue design, including 5G and Wi Fi 6 deployment, edge computing, and the integration of technology partners into stadium infrastructure planning.
Participants will then explore how real time data systems transform stadium operations. Topics include IoT architecture, sensor networks, command centers, and decision intelligence platforms that allow venues to monitor crowd flows, optimize fan experiences, and manage operations through live data. Learners will understand how modern venues operate as intelligent environments where thousands of connected devices continuously generate operational insights.
The course also focuses on access control, digital ticketing, and security technologies that enable safe and seamless event management. Concepts such as crowd intelligence, automated incident response, and AI assisted security systems will be explored.
Finally, learners will examine the role of energy systems, connectivity infrastructure, and sustainability technologies in smart venues. By the end of the course, participants will understand the technical foundations that enable stadiums to operate as highly connected, efficient, and sustainable digital infrastructures.
In this course you will be introduced to two methods of writing SQL queries that retrieve data from two or more tables. Since one of the functions of a database is to store data in an organized format, many databases are made up of multiple tables. Often, the data output required from the database is made up of data from more than one table. For example, the data that populates a student transcript might come from the Student, Course, and Section tables. While the Student table may provide the student’s name, the name and number of the course might come from the Course table and the specific grade for that course may come from yet another table. While writing SQL queries in SQLiteStudio, you'll learn the SQL syntax required to join tables together as you develop an understanding of how the relationships among tables come into play.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
「生成 AI: 現在の状況を知る」は、生成 AI リーダー学習プログラムの 3 つ目のコースです。生成 AI は、私たちの働き方や、私たちを取り巻く世界との関わり方を変えています。リーダーは、実際のビジネス成果に結びつけるために、生成 AI の力をどのように活用できるでしょうか？このコースでは、生成 AI ソリューションの構築におけるさまざまなレイヤ、Google Cloud のサービス、ソリューションを選択する際に考慮すべき要素について学びます。
This course discusses how companies create new products that customers want while achieving their financial objectives. Learners begin by defining the product strategies necessary to ensure a company’s long-term growth. Learners then explore the different product development processes used by high-tech businesses today, such as Stage-Gate and Lean/Agile techniques. Learners conclude by illustrating the tools to build the high-performance teams that take the development process from concept through product launch.
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.
Build, Train and Deploy ML Models with Keras on Google Cloud - Français
Ce cours porte sur la création de modèles de ML à l'aide de TensorFlow et Keras, l'amélioration de la précision des modèles de ML et l'écriture de modèles de ML pour une utilisation évolutive.
