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  1. What does it really mean to “observe” a star? This course trains you to think like an observational astronomer, moving from intuition to first-principles reasoning about starlight and stellar matter. You will build a coherent picture of stellar physics by following real observational clues and turning data into explanations.

  2. Microservices Masterclass
    David Farley and CourseAI

    Microservices are a great approach for building software at scale. But although the ideas at the root of microservices may sound simple, this is not a simple approach. There are several big traps along the way, and it's important to avoid them...

  3. Astrophysics for the Rest of Us: Fundamentals
    AftRoU: Fundamentals
    Toshiya Ueta

    What does it really mean to “observe” the Universe? This course trains you to think like an observational astronomer, moving from intuition to first-principles reasoning about light and matter, plus interactions between them. You will build a coherent picture of astrophysics by following real observational clues and turning data into explanations.

  4. Using openGUTS for survival analysis
    Short course introducing you to TKTD analyses of survival data with the user-friendly openGUTS software
    Tjalling Jager and Roman Ashauer

    Learn the basics of the GUTS framework, and how to apply it to analyse toxicity data and make risk predictions for field situations. This short on-line course guides you through the user-friendly openGUTS software (open source and freely downloadable Windows executable).

  5. SysML v2 Certification Model Reader
    Preparation with Sample Questions
    Tim Weilkiens and Vince Molnár

    Attention: Read the note below!Boost your SysML v2 skills and pass the Model Reader Certification Exam with confidence. Prepare for the exam with sample questions from Tim Weilkiens and Vince Molnar. This Leanpub course focuses on what the exam expects. Detailed explanations are available in the companion “The SysML v2 Book”.

  6. Understanding data and statistics in the medical literature
    Jeffrey Leek, Lucy D'Agostino McGowan, and Elizabeth Matsui

    Whether a medical student reading their first journal article or a healthcare professional trying to use the latest research to improve patient care, this course will help you understand data and statistics in the medical literature in an efficient and conceptual manner.

  7. Creating Leanpub Courses
    Learn how to create Leanpub courses writing questions and answers in plain text files
    Len Epp

    Learn All The Things!

  8. Introduction to R for Social Researchers
    A Practical Course on Data Preparation, Visualisation, and Statistical Analysis
    Alexandru Cernat

    Learn how to use R confidently for real social research. This hands-on course guides you through data preparation, visualisation, and core statistical methods using practical, research-driven examples. Build a solid R workflow you can immediately apply to your own data.

  9. Choosing Genomics Tools
    Fred Hutch, Candace Savonen, and Carrie Wright

    There's a multitude of genomic data analysis tools and resources out there. How do you find what you need for your data types and questions? This course aims to give you the foundational information you need and equip you with knowledge of the genomics tools out there so you can make informed decisions about your research.

  10. Free Course Two
    Peter Armstrong
    No Description Available
  11. Free Course One
    Peter Armstrong
    No Description Available
  12. No Description Available
  13. DataTrail
    Data Trail

    DataTrail aims to equip individuals with the tools they need to enter the booming field of data science.

  14. Intro to workflows on Terra
    Terra's bulk analysis pipelining mode
    Allie Cliffe and Leyla Tarhan

    Learn to run workflows - bulk analyses that can be automated and scaled - on Terra.

  15. Machine Learning Made Simple
    8 Pillar Machine Learning Algorithms in Python
    Christian Mayer and Lukas Rieger

    This course gives you an intuitive understanding of the eight most important machine learning algorithms. It helps you get started using them in your own projects NOW -- in a single line of Python code. After finishing this course, you'll be able to select, understand, and implement the top 8 machine learning algorithms in your own projects.