The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
Coursera
Mastering Portfolio Analysis
This course starts with an introduction to the basics of portfolio analysis, setting a solid foundation for evaluating investment portfolios. It further navigates through a variety of portfolio management strategies, shedding light on their distinct approaches and relevance in diverse financial scenarios.
University of Pennsylvania
Health Care Innovation
In this course, you’ll learn the foundational economic theories behind health care innovation and how to optimize your own health care practice or organization. Designed to help you gain a practical understanding of innovation techniques, operations management, and value and quality in the health care setting, this course will help you apply these frameworks to assess health care practices and apply innovation while managing risk. You’ll also explore the best practices for evaluating one’s innovative practices, using real-life examples of success to see the concepts in action. By the end of this course, you’ll have honed your skills in optimizing health care operations, and be able to develop the right set of evaluations and questions to achieve best innovative practices within your organization.
EDUCBA
XHTML - Positional CSS for Layout
This course empowers learners to analyze, apply, and design modern web layouts using XHTML and CSS. Beginning with a critical examination of traditional layout techniques such as frames and tables, the course transitions into practical mastery of CSS float-based layouts, including multi-column designs and form alignment. Through progressive modules, learners will construct responsive layouts using fluid and Jello layout techniques, organize content using semantic HTML lists, and develop interactive navigation menus purely with CSS. The final module guides learners to demonstrate control over page composition using absolute, fixed, and relative positioning strategies, while leveraging z-index for layering and visual hierarchy. By the end of the course, learners will be able to implement professional-grade web layouts with precision, semantic structure, and responsive behavior—without relying on outdated techniques or JavaScript.
Coursera
Measure ML Impact & Business Value
Most ML initiatives stall between “great AUC” and “great business results.” This course closes that gap end to end. You’ll learn to translate model performance into money by building metric trees that link offline metrics to product KPIs and P&L outcomes. We’ll design defensible measurement plans with the right counterfactuals (A/B, holdouts, geo, diff-in-diff) and guardrails that prevent “wins” that hurt the business elsewhere. You’ll practice power and sample size, variance reduction (CUPED), and lift analysis with confidence intervals. Then we turn lift into ROI: incremental revenue or savings, operating costs, payback and NPV, plus sensitivity analysis to reflect uncertainty. We’ll finish with impact dashboards and an executive narrative that enable clear go/no-go and scale-up decisions. This course is for professionals involved in planning, evaluating, or implementing ML solutions — including Data Scientists, ML Engineers, Business Analysts, Product Managers, and Technology Leaders. It’s also suitable for anyone looking to better connect ML outcomes with business value. Learners should have a basic understanding of Machine Learning concepts and general business workflows, along with an interest in applying data-driven solutions. No advanced coding or mathematics is required. By the end of this course, you’ll consistently connect model metrics to financial outcomes and communicate impact in a way leaders trust—so teams ship fewer models and deliver more value.
Infosec
CISSP Domain 4: Communication and Network Security
This course focuses on Domain 4 of the CISSP exam, covering network security. It begins with an in-depth look at the OSI Reference Model. At each layer, we will discuss functionality, threats/vulnerabilities and common mitigation strategies. In addition, we will focus on firewalls, proxy servers and remote access solutions. It also covers the security services that cryptography can provide and examine common terms like initialization vectors, salts, hashing, algorithms and keys. We will then build on this foundation to explore symmetric, asymmetric and hybrid cryptography and look at its practical implementations.
Maven Analytics
Complete Visual Guide to Machine Learning
This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science. Instead of memorizing complex math or writing code, we'll use simple, visual examples and Excel-based models to break down foundational machine learning concepts and help you build an intuition for exactly how they work. PART 1: QA & Data Profiling In Part 1 we’ll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We’ll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation matrices. PART 2: Classification Modeling In Part 2 we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting. From there we'll review common classification models like K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization. PART 3: Regression & Forecasting In Part 3 we’ll introduce core building blocks like linear relationships and least squared error, and practice applying them to univariate, multivariate, and non-linear regression models. We'll review diagnostic metrics like R-squared, mean error, F-significance, and P-Values, then use time-series forecasting techniques to identify seasonality, predict nonlinear trends, and measure the impact of key business decisions using intervention analysis. PART 4: Unsupervised Learning In Part 4 we’ll explore the differences between supervised and unsupervised machine learning and introduce several common unsupervised techniques, including cluster analysis, association mining, outlier detection and dimensionality reduction. We'll break down each model in simple terms, from K-means and apriori to outlier detection, principal component analysis, and more. Throughout the course, we’ll introduce real-world scenarios and to solidify key concepts and simulate actual data science use cases. You’ll visualize Olympic athlete demographics and traffic accident rates, use regression to estimate property prices and predict product sales, apply clustering models to identify customer segments, and even measure the business impact of a new website design. If you're an analyst or aspiring data professional looking to build the foundation for a successful career in machine learning or data science, this is the course for you!
LearnKartS
Program Management Fundamentals
Ready to take your leadership talents to the next level? This Program Management Fundamentals course is the first step toward obtaining the globally recognized PMI-PgMP® certification and leading strategic initiatives with effect. This PgMP course, designed for experienced project or program managers, delves deeply into the key ideas, frameworks, and real-world applications of program management as defined by the Project Management Institute (PMI®). Learn how to align programs with company strategy, implement governance frameworks, manage interconnected projects, and provide long-term commercial value. You'll get a practical grasp of important concepts from the PMI® Exam Content Outline (ECO), such as stakeholder engagement, benefits management, and performance monitoring. Through captivating videos, scenario-based exams, and professional guidance, you'll not only gain the knowledge but also the confidence needed to manage complex program settings and prepare for the PgMP® certification exam. By the end, you'll be able to lead projects with clarity, structure, and a long-term view, instead of just managing them. Enroll now to take the next step toward getting recognized as a program management leader.
Coursera
Bewirb dein Kleinunternehmen auf Facebook mit Canva
Nach Abschluss dieses Projekts, kannst du dein Kleinunternehmen mit Hilfe der gratis Version von Canva auf Facebook bewerben. Du wirst in diesem angeleiteten Projekt lernen die Grafikdesign Plattform Canva zu nutzen, um Facebook Anzeigen, Facebook Posts und ein Facebook Titelbild zu erstellen. Egal, ob du eine neue Marke startest oder eine bestehende mithilfe deiner Social-Media-Strategie optimieren möchtest – visuelle Inhalte unterstützen dich bei der Etablierung deiner Online-Identität. Die web basierte Plattform Canva bietet alle nötigen Werkzeuge um visuell attraktive und organisierte Produkte alleine, oder als Team zu kreieren und zu teilen. Am Ende dieses Projekts wirst du gelernt haben, Grafikdesign Tools anzuwenden um kreative und attraktive Inhalte für Facebook zu erstellen und deine Marke oder dein Kleinunternehmen auf Facebook zu bewerben.
Whizlabs
Azure ML: Deploying, Managing, and Experimenting with Models
This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with essential skills for managing ML workflows within the Azure ML workspace. Participants will begin by understanding core workspace fundamentals, including environment setup, resource management, and key components for ML experimentation. The course progresses to advanced concepts such as optimizing compute resources, managing datasets effectively, and configuring high-performance ML pipelines. Learners will gain expertise in scaling ML workloads, fine-tuning data storage strategies, and applying best practices for secure and efficient model deployment. Additionally, the course covers advanced data and compute management techniques to enhance ML operations (MLOps) and ensure seamless integration with Azure services. This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios. Module 1: Experiment with Azure Machine Learning Module 2: Deploying, Consuming, Managing, and Evaluating Models with Azure Machine Learning By the end of this course, a learner will be able to Explore the process of registering, logging, and deploying MLflow models Understand and implement Responsible AI practices Understand the fundamentals of AutoML in Azure Learn about different machine learning algorithms and tasks Master how to interpret AutoML job results, ensuring success and optimizing model performance.
Rice University
Biases and Portfolio Selection
Investors tend to be their own worst enemies. In this third course, you will learn how to capitalize on understanding behavioral biases and irrational behavior in financial markets. You will start by learning about the various behavioral biases – mistakes that investors make and understand their reasons. You will learn how to recognize your own mistakes as well as others’ and understand how these mistakes can affect investment decisions and financial markets. You will also explore how different preferences and investment horizons impact the optimal asset allocation choice. After this course, you will be more effective in overcoming biases to do the wrong things at the wrong times and tailoring an investment strategy that is best suited on your or your client’s profile and investment needs.
University of Colorado Boulder
Equivalences, Abstraction, and Partial Order Reduction
This course introduces methods to utilize abstraction and partial order methods to reduce the complexity of their systems models. The equivalences introduced are based upon bisimulation and simulation relations. These concepts allow one to prove that a model is an abstraction (or simplification) of another model of the same system. Abstraction reduces the complexity of the system model while preserving the ability to correctly verify properties of the system. This course will also introduce the partial order method to further reduce model complexity during verification by enabling the state space exploration to not need to consider all possible interleavings of concurrent events. This approach often provides substantial reductions in the state space of the model being verified. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical and Computer Engineering (MS-ECE) 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 and Computer Engineering: https://www.coursera.org/degrees/msee-boulder
Coursera
RF/Microwave Design - RF Simulation Tools (ADS)
Modern RF systems—spanning wireless communications, radar, satellites, and emerging 5G/6G technologies—demand precise circuit design and validation. As frequencies rise, traditional circuit intuition becomes unreliable, making advanced simulation essential for predicting real-world behavior before hardware is built. This course equips learners with practical, industry-ready skills in RF and microwave design using Keysight ADS. Through guided demos and hands-on labs, you will master S-parameter analysis, transmission-line modeling, impedance matching, and electromagnetic (EM) simulation with Momentum. Real engineering scenarios show how coupling, parasitics, and layout effects influence performance, and how ADS enables accurate prediction and optimization. By the end of the course, you will confidently design RF schematics, evaluate matching networks, analyze antennas and filters, and refine layouts through EM-based validation. Whether you are building wireless front-end modules, optimizing radar components, or modeling high-frequency behavior for next-generation systems, this course provides the applied expertise needed to produce manufacturable, professional-grade RF designs.
Coursera
Medical Office Operations and Patient Flow
Master the essential operational skills that keep medical offices running smoothly and patients satisfied. This Short Course was created to help health administration professionals accomplish efficient, compliant, and patient-centered medical office management. By completing this course, you'll be able to confidently execute daily opening and closing procedures, maintain optimal patient flow through examination rooms, implement effective scheduling and triage protocols, and manage seamless patient check-in and check-out processes that you can apply immediately in your workplace. By the end of this course, you will be able to: • Apply operational policies to complete daily opening and closing checklists • Recall the steps for preparing examination rooms and maintaining patient flow • Explain standard protocols for patient scheduling, triage, and follow-up • Apply check-in and check-out procedures to ensure accurate client intake and documentation This course is unique because it combines fundamental administrative procedures with patient flow optimization strategies, providing you with both the operational foundation and the practical skills to enhance patient satisfaction while maintaining compliance and efficiency. To be successful in this course, you should have a background in basic healthcare administration concepts and familiarity with medical office environments.
University of Michigan
人、テクノロジー、そしてモビリティの未来
「人、テクノロジー、そしてモビリティの未来」コースへようこそ！このコースでは、モビリティ分野で現在進行中の主要な技術革新のいくつかを一般の人向けに紹介します。受講生には、さまざまな社会科学の概念を適用して、これらの技術がもたらす可能性のある社会的影響を理解するよう求めます。このコースは、あらゆるバックグラウンドの受講生に適しており、工学または社会科学の事前のトレーニングは必要ありません。 このコースの内容は、 Siemensとのパートナーシップを通じて作成された多数のインタビューや講義に基づいています。このコースにはインタラクティブな360ビデオ体験も含まれており、受講生は自動運転車の主なコンポーネントについて学習したり、自動運転車に同乗したり、現在と未来のスマートシティで使用される新しいセンシング技術について学ぶことができます。 このコースでは、より安全かつクリーンで、より公平な未来のモビリティを思い描くことを受講生に求めます。また、電化とオートメーションという、進化する2つのモビリティ関連技術分野について、最近の技術進歩と社会経済や政策への影響について考察します。
Meta
Fundamentals of Social Media Advertising
This course takes a deep dive into paid advertising on social media. Learn how to start advertising on platforms like Facebook and Instagram by developing effective ads. Learn how to work with design teams by capturing the essence of your ad campaign in a creative brief, and understand how privacy policies may affect your ads. Complete the course with a project where you will produce a creative brief with assets you would deliver to a design team for your ad campaign. You’ll also create your first social media ad. By the end of this course you will be able to: • Determine why and when to invest in paid advertising on social media • Understand the anatomy of a social media ad and how they differ from organic posts • Evaluate on which platforms to run social media ad campaigns and what makes an ad effective • Craft compelling and effective visuals and copy for social media ads • Learn how to collaborate effectively with others through creative briefs • Build foundational understanding for data, data-based advertising and privacy protection • Build an ad directly from your Facebook Business Page and your Instagram Business Account • Use Instagram Stories Ads effectively to connect with customers • Write a creative brief and create a social media ad This course is intended for people who want to learn how to create and manage ads on social media. Learners don't need marketing experience, but they have basic internet navigation skills and are eager to participate and connect in social media. Having a Facebook or Instagram account helps and ideally learners have already completed course 1 (Introduction to Social Media Marketing) and 2 (Social Media Management) in this program.
Packt
Regression Analysis for Statistics & Machine Learning in R
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 course delves into regression analysis using R, covering key concepts, software tools, and differences between statistical analysis and machine learning. - You'll learn data reading, cleaning, exploratory data analysis, and ordinary least squares (OLS) regression modeling, including theory, implementation, and result interpretation. - You'll tackle multicollinearity with techniques like principal component regression and LASSO regression, and cover variable and model selection for performance evaluation. - You'll handle OLS violations through data transformations and robust regression, and explore generalized linear models (GLMs) for logistic regression and count data analysis. - Advanced sections include non-linear and non-parametric techniques such as polynomial regression, GAMs, regression trees, and random forests. Ideal for statisticians, data analysts, and machine learning practitioners with basic R knowledge, this course blends theory with hands-on practice to enhance your regression analysis skills.