The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
Packt
R Programming for Statistics and Data Science
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This in-depth course starts by walking you through the basics of R programming, from setting up the environment with R and RStudio to understanding its user interface. As you move through the early sections, you'll dive into foundational programming concepts like data types, functions, and vector operations, enabling you to build a solid base in R. You’ll also learn how to handle complex structures like matrices and data frames, making it easy to organize and manipulate data efficiently. As the course progresses, you’ll explore more advanced R capabilities, such as creating and modifying data frames, using the popular dplyr package, and working with relational, logical operators, and loops. The lessons on data manipulation and visualization offer hands-on experience in cleaning and presenting data, covering essential tools like ggplot2 for creating insightful graphs and charts. These skills will help you analyze data and make data-driven decisions more effectively. Finally, the course delves into statistics with exploratory data analysis, hypothesis testing, and linear regression modeling. By mastering these techniques, you'll gain the ability to analyze real-world data, draw meaningful insights, and make predictions. Whether you’re an aspiring data scientist or a statistician looking to hone your skills, this course provides everything you need to succeed in the data science field using R. This course is designed for aspiring data scientists, statisticians, and professionals looking to master R for data analysis. Basic knowledge of programming is beneficial, but not required.
Coursera
Optimizing Enterprise Testing with Functionize
Enterprise-Scale Testing with Functionize: AI Optimization and Integration is a hands-on, advanced-level course designed for test architects, SDETs, and DevOps professionals looking to scale automation in fast-paced CI/CD environments. As enterprise software systems grow in complexity, traditional test suites often struggle with fragility, flakiness, and slow feedback loops. This course teaches you how to design scalable, AI-powered automation architectures using Functionize—incorporating intelligent self-healing, orchestration strategies, and integration with GitHub Actions, Jenkins, and Azure DevOps. Through guided walkthroughs, hands-on labs, and expert-driven projects, you'll learn to shift from test execution to intelligent test architecture—ensuring your automation adapts, scales, and performs in real-world enterprise workflows.
Microsoft
Beyond the Basics: Expand Your Copilot for Sales Expertise
Designed for experienced Copilot users, this course comprehensively examines advanced features, integrations, and the future of AI in sales. You will learn how to fine-tune Copilot for specific use cases, leverage it for sales forecasting and pipeline management, and integrate it with a wider range of Microsoft and third-party tools. Additionally, the course explores emerging AI trends and ethical considerations, equipping participants with the knowledge to stay ahead of the curve and use AI responsibly in their sales strategies. Required Course Materials: A Copilot license is required to complete this course. If you don’t have a Microsoft 365 Personal or Family license, you can start a free 30-day trial using the link provided in the course.
Coursera
Python Scripting for DevOps
In this course, we are going to focus on the following learning objectives: 1. Work with core Python programming tools 2. Become comfortable reading and writing Python scripts By the end of this course, you will have a solid grasp of scripting in Python. You will learn the Pythonic way of many of the core programming concepts. You will be able to read and understand Python scripts in your daily line of work
Coursera
Advanced Supplier Quality, Risk, and Process Improvement
This advanced course builds skills for post-award contract compliance, supplier performance analytics, and structured risk mitigation. You will extract key commercial clauses into contract summary sheets using checklists, compare performance and spend against contract baselines to quantify variance impacts, and build probability-impact heat maps to prioritize supplier risk. The course also covers designing and implementing corrective actions and continuous-improvement cycles to resolve recurring issues and measure impact. This module is oriented toward professionals who review contracts, analyze supplier delivery and financial performance, and lead remediation or optimization activities to improve supply chain reliability and contractual value realization.
Imperial College London
Science Matters: Let's Talk About COVID-19
Welcome to ‘Science Matters: Let's Talk about COVID-19’, from the Jameel Institute at Imperial College London. The outbreak of the Novel Coronavirus Disease (COVID-19) is the most significant public health emergency of the 21st century so far. As the epidemic spreads, people around the world want to understand the science behind the most pressing questions: how many people have been infected? How dangerous is the virus? When will a vaccine be available? How can the epidemic be contained, and the damages mitigated? What is the economic impact? What’s the role of social media and local communities in the epidemic response? Researchers at the Jameel Institute and other research institutes at Imperial College London have been at the forefront of the response to the COVID-19 emergency, with clinical, epidemiological and social science analyses informing the outbreak response. They are generating robust empirical evidence that governments and international agencies are using around the world to plan their responses. On this course, you will hear directly from our world-class experts about the theory behind the analyses of COVID-19 and its spread, while learning how to interpret new information using core principles of public health, epidemiology, medicine, health economics, and social science. You will be able to watch regular situation reports about the state of the epidemic, provided by the researchers of J-IDEA and its director Professor Neil Ferguson. If you want to learn even more about these topics, a number of free MOOCs are available from Imperial College London. We also offer a fully online Global Master of Public Health for those of you who want to delve even deeper and join our professional community. Please note: This course was launched in February 2020 and we have continued to develop content as the COVID-19 situation progresses and new insights emerge. While we endeavour to include the most recent information, this is a fast-moving situation and information is constantly changing. This course is to be used for educational purposes only and is open to all and free of charge. The information in this course does not constitute clinical or other advice and must not be used for the purposes of providing any clinical or other advice. If you have any health concerns, please refer to your regional health authorities’ guidelines and consult a medical professional. Please note the views expressed by individuals in the course content do not necessarily reflect those of Imperial College London, the Jameel Institute and any other funding partners. Imperial College London, the Jameel Institute and any other funding partners shall not be liable, to the maximum extent permitted by law, for any loss suffered or for any other adverse or negative consequence arising directly or indirectly from your reliance on the information contained in this course.
Coursera
Data Cleaning in Snowflake: Techniques to Clean Messy Data
in 2006, the British mathematician Clive Humby coined the phrase "Data is the new Oil". This analogy has been proven correct as data powers entire industries nowadays but if left unrefined, is effectively worthless. This 2.5 hours-long guided project is designed for business analysts & data engineers eager to learn how to Clean Messy Data in Snowflake Data Platform. By the end of the project, you will -Be able to identify common data quality issues then use SQL String functions to remove unwanted characters and split rows into multiple columns. -Extract dates from Text fields then use SQL date functions for comparisons and calculations. -Identify and correct missing and duplicated data then answer business questions using SQL statements. To achieve these objectives, we will work on a real example from the field, you will play the role of a Data Analyst in the marketing department, who has been tasked with answering a business question, but the customer data they have received presents several data quality challenges. Note: To be successful in this project you need to have Snowflake beginner knowledge such as Creating a trial account, Databases, Tables, and Virtual Warehouses. If you are not familiar with Snowflake and want to learn the basics, start with my previous Guided Project: Snowflake for Beginners: Make your First Snowsight Dashboard which will give you basic knowledge about Snowflake and will teach you how to create your trial account.
Kennesaw State University
Leading Organizational Change
One of the most challenging problems facing leaders today is navigating change. This course presents a framework for managing change in your team, department, or organization. In this course, participants will understand the fundamentals of change, how to make change happen, and how to evaluate the outcomes. Additionally, the course content discusses creating a change management team and specifies individual roles and responsibilities to communicate the need for change successfully. While providing the best methods for managing change in leadership, processes, culture, and technology, the course also examines strategies for overcoming resistance to change.
Johns Hopkins University
The Persuasive Leader
This aims primarily at post-baccalaureate students interested in leadership theory. The course has four modules. Module 1 introduces students to agile leadership as (a) a logical sequel to adaptive and team leadership, and (b) the foundation of contemporary persuasive leadership. Topics include a working definition of agile leadership, the need for agile leadership, and characteristics of the agile leader. Module 2 answers the question, What is persuasive leadership? Topics include persuasive vs. coercive leadership, conversation as essential to persuasive leadership, types of persuasion, elements of persuasion, and principles of persuasion. Module 3 answers the question, Why persuasive leadership? Topics include setting the question, benefits to the organization, benefits, to the team, and benefits to the leader. Module 4 answers the question, Persuasive leadership: How? Topics include preparing the leader, preparing the team, engaging the team in a plan, executing the plan, and assessing and improving. To complete this course successfully students should be able to analyze college-level readings and audio/visual presentations into understandable parts, including premises and conclusions; synthesize the results of the analysis into coherent and accurate summaries; and evaluate the results for accuracy and practical applicability. This is one course in the Coursera specialization, Leadership: An Introduction. It examines current trends in leadership theory invoking several disciplines, including business, sociology, philosophy, history, and psychology. Upon successful completion of the course, students will be able to • Define persuasive leadership • Explain how adaptive leadership, team leadership, and agile leadership underlie persuasive leadership • Assess the value of persuasive leadership to contemporary organizational leadership • Apply techniques of persuasive leadership to organizational challenges
Packt
Foundations of Project Management
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Learn the essential skills and strategies to become an effective project manager with this comprehensive course. From mastering the PMI methodology to understanding key concepts such as guiding principles, performance domains, and ethical frameworks, this course sets you up for success in the CAPM exam and beyond. You’ll gain the tools needed to effectively plan, execute, and close projects in both predictive and adaptive environments. Dive into the fundamentals of project management, beginning with an overview of methodologies, planning strategies, and stakeholder engagement. Explore practical applications like schedule creation, risk planning, and communication management. With real-world insights into balancing constraints and addressing stakeholder needs, you'll gain a thorough understanding of managing projects from start to finish. In the later modules, you’ll develop advanced skills such as emotional intelligence, servant leadership, and the application of agile roles. Learn to differentiate the responsibilities of a project manager versus a sponsor, and understand the nuances of project initiation and closure. Each section is designed to reinforce your knowledge with practical tools and techniques to solve real-world project challenges. This course is ideal for aspiring project managers, business professionals, and anyone preparing for the CAPM certification. No prior experience is necessary, as the material is presented at a beginner to intermediate level, ensuring accessibility for all learners eager to develop project management expertise.
Packt
Advanced Automation Frameworks and Continuous Integration
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this advanced course, you'll journey through automation frameworks and continuous integration, starting with Python logging infrastructure, mastering message logging, custom log formats, and logger utilities. You'll delve into the unittest framework, writing test cases, implementing setup and teardown methods, and running tests from the terminal. Next, explore Pytest, learning fixtures, test orders, and HTML test reports. The course's three-part modules focus on building robust frameworks with Selenium WebDriver and Python, covering logging, test result verification, and dynamic elements handling. Practice exercises reinforce these concepts. Additionally, learn data-driven testing, complete test suite execution, and version control with Git and GitHub. You'll also set up and secure Jenkins, manage plugins, and build remote projects, streamlining your CI/CD pipelines. By the end, you'll be adept in automation frameworks and continuous integration, ready to tackle complex projects confidently. This course targets software developers, QA engineers, and automation testers with basic Python knowledge. Familiarity with Selenium WebDriver and basic testing concepts is recommended.
Google Cloud
Google Cloud Compute and Scalability for AWS Professionals
This is the second course of a four-course series for cloud architects and engineers with existing AWS knowledge. It aims to compare Google Cloud and AWS solutions and guide professionals on their use. This course focuses on compute resources and load balancing in Google Cloud. The learner will apply the knowledge of using virtual machines and load balancers in AWS to explore the similarities and differences with configuring and managing compute resources and load balancers in Google Cloud. Learners will get hands-on practice building and managing Google Cloud resources.
The State University of New York
Student Success: Foundations of Self-Management
This course walks learners through the behavioral concepts presented in Professor Roma’s book, “Student Success: Foundations of Self-Management”, (SUNY Press, 2023). It is organized around an individual behavioral responsibility framework called the 5C Elements of Self-Management. Whether you are a student or an experienced working adult, the 5C Elements of Self-Management are the same: • Communication, which conveys appropriateness, • Choice, which conveys judgment, • Caring, which conveys concern for others, • Commitment, which conveys duty, and • Coping, which conveys fortitude. We all convey these elements of self-management to one extent or another. This course introduces you to these elements and the thirty-eight sub-variables that underpin the framework, explains why they are important to you, and describes how to identify and assess them in yourself and others.
Packt
Natural Language Processing - Probability Models in Python
This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into Natural Language Processing (NLP) using probability models in Python! This course covers essential topics like Markov models, text classification, article spinning, and cipher decryption. You will build practical skills by applying theoretical knowledge through coding exercises, enabling you to tackle real-world NLP problems with probability models. Begin by understanding the foundations of Markov models, including the Markov property and probability smoothing techniques. You will learn how to build and code text classifiers and language models, exploring the application of these models in text prediction. With hands-on coding exercises, you will master implementing these models in Python. Next, you will delve into article spinning using n-grams, enhancing your ability to generate diverse and meaningful content. Finally, you’ll explore the complexities of cipher decryption, applying probability models and genetic algorithms to crack encrypted messages. Throughout the course, you'll solidify your understanding by coding and testing various models. This course is perfect for learners interested in NLP, machine learning, and Python programming. No prior experience in probability modeling is required, though familiarity with Python basics is beneficial. Ideal for learners looking to strengthen their NLP and data science skills.
Fractal Analytics
Data Frameworks for Generative AI
Modern GenAI (LLMs, RAG, agentic AI) succeeds or fails on the quality, structure, and governance of the data behind it. In this course, you’ll learn how structured and unstructured data drive GenAI applications, and how to design comprehensive data frameworks, taxonomies, and governance practices that reduce hallucinations, improve relevance, and make AI outcomes reliable. You’ll examine LLM limitations, connect them to data quality and metadata strategy, and implement taxonomy led architectures that future proof enterprise AI. Through case studies, practice assignments, and guided dialogues, you’ll develop the skills to design, validate, and operationalize GenAI ready data foundations for real products and platforms. By the end, you’ll be able to create enterprise grade data frameworks that deliver consistent, ethical, and high performing results.
Carnegie Mellon University
Statistical Thermodynamics: Molecules to Machines
Modern engineering research focuses on designing new materials and processes at the molecular level. Statistical thermodynamics provides the formalism for understanding how molecular interactions lead to the observed collective behavior at the macroscale. This course will develop a molecular-level understanding of key thermodynamic quantities like heat, work, free energy and entropy. These concepts will be applied in understanding several important engineering and biological applications.