// 20 courses indexed
The course directory
Every course we track across 10+ providers, in one searchable index. Want a shortlist picked for your goals? Run a free skill gap analysis.
Software Development Practices
Welcome to the Software Development Practices course! In this course, you will explore techniques for eliciting and documenting software requirements, including the creation of use cases and user stories. You will learn to design software systems using visual modeling methods such as UML diagrams and Data Flow Diagrams (DFDs). Throughout this learning journey, you will acquire the skills to produce high-quality code, leverage libraries and APIs, and efficiently manage code with version control tools like Git and GitHub. You will also delve into contemporary deployment and DevOps strategies, en
All levels
GitHub: AI-Augmented Testing and Refactoring
Learn to accelerate your software development workflow by combining GitHub Copilot with test-driven development, system-wide refactoring, and infrastructure-as-code generation. This course teaches you to use AI assistance at every stage of code quality — from writing your first test to deploying containerized applications. You will start with AI-assisted test-driven development, using GitHub Copilot to generate test cases, mock dependencies, and evaluate test coverage with pytest. You will then move to system-wide refactoring, leveraging @workspace references to analyze cross-file dependencie
All levels
MLOps and responsible AI practices
This course equips you with the essential skills to take generative AI models from development to production. You will learn to implement robust MLOps practices on Azure, including automated CI/CD pipelines, version control, and full lifecycle management for your models. Simultaneously, you will dive into the critical principles of Responsible AI, using Microsoft’s framework to build fair, transparent, and ethical models that you can deploy with confidence.
All levels
GitHub: Evaluating and Integrating AI Models
Learn to evaluate, select, and integrate AI models using GitHub Models — a service that provides ready-to-use, off-the-shelf machine learning models directly within the GitHub platform. You will navigate the GitHub Models marketplace to compare models by provider, capability, and rate limits, then test them interactively using the built-in playground with system prompts and temperature controls. This course covers the practical skills needed to move from model evaluation to production integration. You will understand how rate limits work across different models, learn strategies for scaling b
All levels
Infrastructure Automation with Ansible
This course builds a strong foundation in infrastructure automation using Ansible, equipping you with the skills to replace manual server provisioning with consistent, repeatable, and scalable automation workflows. You’ll begin by understanding why infrastructure automation is essential in modern IT environments. Instead of relying on manual configuration and repetitive setup processes, you’ll learn how Infrastructure as Code (IaC) brings structure, reliability, and version control to server management. You’ll explore Ansible’s agentless architecture, core components such as inventories, modu
All levels
Introduction to Generative AI
This introductory course is designed for beginners with no prior knowledge of generative AI. You will start by gaining a high-level understanding of what generative AI is and how it works. Through interactive lessons and hands-on examples, you will learn fundamental skills like providing effective prompts and iteratively improving the generated outputs. As the course progresses, you will dive deeper into specific major generative AI models, including their unique capabilities and limitations. Finally,, you will get practical experience using leading systems like GitHub Copilot, DALL-E, and Ope
All levels
Tools for Data Science
In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what prog
All levels
Continuous Integration and Continuous Delivery (CI/CD)
A principle of DevOps is to replace manual processes with automation to improve efficiency, reduce human error, and accelerate software delivery. This requires automation that continuously integrates code changes and continuously delivers those changes to a production environment. This course introduces you to Continuous Integration and Continuous Delivery (CI/CD), an automated approach to software development. You’ll discover the benefits of CI/CD for creating a DevOps pipeline and explore popular CI/CD tools. You’ll examine the key features of CI, explore social coding, and the Git Feat
All levels
GitHub: Codespaces, Actions, and Ecosystem Tools
Learn to build cloud-based development environments with GitHub Codespaces, run GPU-accelerated AI workloads, use GitHub Copilot for AI-assisted coding, and automate CI/CD pipelines with GitHub Actions. This hands-on course walks you through launching Codespaces from repository templates, configuring dev containers for different machine types, and running NVIDIA GPU instances for machine learning tasks. You will use Whisper for speech-to-text transcription on GPU-enabled Codespaces and explore Hugging Face for model hosting, datasets, and fine-tuning pre-trained models. The course demonstrates
All levels
Foundations of DevOps and Git
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 meticulously designed course, you'll embark on a journey to understand the fundamental principles of DevOps and the crucial role Git plays in version control and continuous integration. The course begins with an introduction to DevOps, explaining its importance and how it revolutionizes the development and operations landscape. You'll
All levels
Kubernetes From Basics to Guru: DevOps and GitOps
This course delves into the transformative methodologies of DevOps and GitOps, equipping you with the skills to streamline software development and deployment processes. Through engaging lessons, you'll explore the origins and evolution of DevOps, understand the intricacies of Continuous Integration and Continuous Deployment (CI/CD), and gain hands-on experience with Git, the cornerstone of version control. Additionally, you'll learn to create efficient pipelines to automate workflows, enhancing productivity and collaboration within your team. By the end of this course, you'll be proficient in
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Git Version Control Mastery – 2026 Guide
This course 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. With hands-on exercises and guided learning, you'll build practical Git skills that will enhance your development workflow. In this course, you'll gain comprehensive knowledge of Git, the essential version control tool for modern development. The content takes you through all aspects of Git, from installation and configuration to advanced workflows and best practices, en
All levels
AWS Elastic Beanstalk & CI/CD for Cloud Projects
Build practical cloud deployment and DevOps skills with AWS Elastic Beanstalk and CI/CD. You’ll begin by configuring a development environment and build files, then use Git and AWS CodeCommit for version control. You’ll automate reliable deployments with AWS CodePipeline and configure notifications to support a complete CI/CD workflow. Next, you’ll explore the core AWS Elastic Beanstalk workflow and learn to create, configure, deploy, and manage applications through its simplified PaaS framework. You’ll also apply deployment strategies, customize scaling, set policies, and integrate databases
All levels
First Steps in Python with Jupyter Notebooks
In this course, you'll set up a powerful development environment, master essential Python syntax, and learn to leverage GitHub for seamless collaboration. By the module's end, you'll be equipped with the same foundational skills used by industry pros, including cutting-edge GenAI applications. Get ready to transform from a coding novice to a confident data explorer. Upon completion, you'll be able to: • Explain the role of Python in data science and how GitHub integrates into a modern data workflow. • Describe Python’s core syntax, data types, control structures, and functions. • Set up and
All levels
Excel Modeling: Organize, Document & Retrieve
Excel Modeling: Organize, Document & Retrieve is a course for financial professionals ready to move beyond formulas and master the strategic architecture of model governance. It provides a system for control for those who have wasted hours hunting for a file, struggled to decipher a colleague’s logic, or feared the catastrophic risk of a version control error. The course is tailored for practitioners with established proficiency in building multi-sheet workbooks and applying complex formulas (e.g., lookups, logical functions) and named ranges. It looks beyond the spreadsheet, drawing lessons
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Data Warehousing & Pipeline Development in Microsoft Fabric
This course teaches you how to build and manage data warehouses and pipelines using Microsoft Fabric. You will get hands-on experience by creating data warehouses, designing schemas, and developing ETL/ELT pipelines with Data Factory. The course covers the use of T-SQL for data transformations and incorporates version control for data workflows, ensuring you can manage production environments confidently. By the end of this course, you will be able to orchestrate data pipelines, implement security, and handle data-to-insights workflows from ingestion through security and deployment.
All levels
Using Git for Distributed Development
Course 4 focuses on using git for open source software distributed development. Git is a revision control system which arose out of the Linux kernel development community. Git was designed to help developers spread across the globe working on complicated projects coordinate their work in a rational way and keep track of everything they were doing. Overtime, Git has grown to be used by millions of projects today. This course gives you the opportunity to learn how to use git efficiently and make contributions in a relatively short amount of time. It will cover: How to install git How to initial
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Automate your DevOps pipelines with GitHub Actions
This course 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 course, you will dive into GitHub Actions to automate your DevOps workflows, improving efficiency and streamlining development processes. You’ll explore essential tools for building, deploying, and managing cloud-based applications, containers, and infrastructure, allowing you to automate complex tasks with ease. Whether you’re a developer or IT professional, thi
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Introduction to Git and GitHub
In this course, you’ll learn how to keep track of the different versions of your code and configuration files using a popular version control system (VCS) called Git. We'll also go through how to set up an account with a service called GitHub so that you can create your very own remote repositories to store your code and configuration. Throughout this course, you'll learn about Git's core functionality so you can understand how and why it’s used in organizations. We’ll look into both basic and more advanced features, like branches and merging. We'll demonstrate how having a working knowledge
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Machine Learning with Databricks and MLflow
This course teaches you to build, track, and deploy machine learning models on the Databricks platform using MLflow. You start with the reproducibility crisis in ML — understanding why untracked experiments, scattered notebooks, and missing version control create production failures — and learn how MLflow solves these problems with structured experiment tracking, model versioning, and artifact management. You then explore MLflow's architecture in depth: the Tracking layer for logging parameters, metrics, and artifacts; the Model Registry for governance and stage gates; and the Projects
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