Try out our self-paced learning modules!
Our modules are designed to be flexible and bite-sized, so you can use your time efficiently and pick just the topics you want to study. Each module has learning objectives and prerequisites clearly listed on the first page, to help you decide which ones you want to work on.
That said, many learners prefer to start with a curated list of suggestions, rather than browsing through the modules on their own. If you’re trying to figure out how to get started with DART, see if one of the suggested pathways below sounds like a good fit!
Pathway 1: Getting started with biomedical data science
This popular pathway is designed with new data scientists in mind. You might be early in your research career, or you might have years of experience but just trying out data science techniques for the first time.
This pathway provides a practical overview of what skills you’ll need to do reproducible, rigorous data science research in biomedical and health fields. We’ll touch on a lot of the hot topic techniques you may have heard about (what exactly are large language models?) and help you cut through the hype to figure out whether those are tools you want to invest time in learning.
If you’re at the point where you know you’re interested in biomedical data science but aren’t sure where to start, this is the pathway for you!
Getting started with biomedical data science: Modules
| Order | Module | Description | Estimated Time |
|---|---|---|---|
| 1 | Reproducibility, Generalizability, and Reuse | This module provides learners with an approachable introduction to the concepts and impact of research reproducibility, generalizability, and data reuse, and how technical approaches can help make these goals more attainable. | 60 min |
| 2 | How to Troubleshoot | Learning to use technical methods like coding and version control in your research inevitably means running into problems. Learn practical methods for troubleshooting and moving past error codes and other difficulties. | 30 min |
| 3 | Learning to Learn Data Science | Discover how learning data science is different than learning other subjects. | 20 min |
| 4 | Demystifying Geospatial Data | This module is a brief introduction to geospatial (location) data. | 15 min |
| 5 | Omics Orientation | This module provides a brief introduction to omics and its associated fields. | 15 min |
| 6 | Demystifying SQL | SQL is a relational database solution that has been around for decades. Learn more about this technology at a high level, without having to write code. | 40 min |
| 7 | Demystifying Machine Learning | An approachable and practical introduction to machine learning for biomedical researchers. | 60 min |
| 8 | Demystifying Large Language Models | Learn about large language models (LLM) like ChatGPT. | 60 min |
| 9 | Directories and File Paths | In this module, learners will explore what a directory is and how to describe the location of a file using its file path. | 15 min |
| 10 | Demystifying the Command Line Interface | Understand what the command line interface is and why it's useful! | 15 min |
| 11 | Demystifying Python | This module introduces the Python programming language, explores why Python is useful in research, and describes how to download Python and Jupyter. | 20 min |
| 12 | Demystifying Regular Expressions | Learn about pattern matching using regular expressions, or regex. | 30 min |
| 13 | Citizen Science | This is an overview of citizen science for biomedical researchers. | 45 min |
| 14 | Demystifying Containers | Containers can be a useful tool for reproducible workflows and collaboration. This module describes what containers are, why a researcher might want to use them, and what your options are for implementation. | 20 min |
| 15 | Intro to Version Control | An introduction to what version control systems do and why you might want to use one. | 15 min |
| 16 | Research Data Management Basics | Learn the basics about research data management. | 40 min |
Pathway 2: Focus on omics
This pathway is for people who want to start working with molecular data. It will bring you up to speed in the computing tools you’ll need to get started with genomics research, including using the command line, version control, and containerization. No computing background is assumed; we’ll start from the basics! Note that if you’re already actively working on genomics analysis, this material will likely be too basic for you.
Focus on omics: Modules
| Order | Module | Description | Estimated Time |
|---|---|---|---|
| 1 | Reproducibility, Generalizability, and Reuse | This module provides learners with an approachable introduction to the concepts and impact of research reproducibility, generalizability, and data reuse, and how technical approaches can help make these goals more attainable. | 60 min |
| 2 | How to Troubleshoot | Learning to use technical methods like coding and version control in your research inevitably means running into problems. Learn practical methods for troubleshooting and moving past error codes and other difficulties. | 30 min |
| 3 | Directories and File Paths | In this module, learners will explore what a directory is and how to describe the location of a file using its file path. | 15 min |
| 4 | Research Data Management Basics | Learn the basics about research data management. | 40 min |
| 5 | Demystifying the Command Line Interface | Understand what the command line interface is and why it's useful! | 15 min |
| 6 | Bash / Command Line 101 | This course teaches learners to navigate their computer, as well as view and edit files, from the command line using Bash. | 40 min |
| 7 | Bash: Searching and Organizing Files | This module will teach you how to use the bash shell to search and organize your files. | 30 min |
| 8 | Bash: Combining Commands | This module will teach you how to combine two or more commands in Bash to create more complicated pipelines in Bash. | 30 min |
| 9 | Bash: Conditionals and Loops | This module teaches you how to iterate through \"for\" loops and write conditional statements in Bash. | 60 min |
| 10 | Bash: Reusable Scripts | This module will teach you how to create and use simple Bash scripts to make repetitive tasks as simple as possible. | 60 min |
| 11 | Intro to Version Control | An introduction to what version control systems do and why you might want to use one. | 15 min |
| 12 | Setting Up Git on Mac and Linux | This module provides recommendations and examples to help new users configure git on their computer for the first time on a Mac or Linux computer. | 15 min |
| 13 | Setting Up Git on Windows | This module provides recommendations and examples to help new users configure Git on their Windows computer for the first time. | 25 min |
| 14 | Creating a Git Repository | Create a new Git repository and get started with version control. | 60 min |
| 15 | Exploring the History of your Git Repository | This module will teach you how to look at past versions of your work on Git and compare your project with previous versions. | 30 min |
| 15 | Omics Orientation | This module provides a brief introduction to omics and its associated fields. | 15 min |
| 16 | Genomics Tools and Methods: Computing Setup | This module walks you through setting up your own copy of a genomics analysis AMI (Amazon Machine Image) to run genomics analyses in the cloud. | 30 min |
| 17 | Genomics Tools and Methods: Quality Control | Get started with genomics! This module walks you through how to analyze FASTQ files to assess read quality, the first step in a common genomics workflow - identifying variants among sequencing samples taken from multiple individuals within a population (variant calling). | 40 min |
| 18 | Demystifying Containers | Containers can be a useful tool for reproducible workflows and collaboration. This module describes what containers are, why a researcher might want to use them, and what your options are for implementation. | 20 min |
| 19 | Getting Started with Docker for Research | This tutorial combines a hands-on interactive Docker tutorial published by Docker Inc with an academic article outlining best practices for using Docker for research. | 60 min |
Pathway 3: Big data, big questions
This pathway is for people primarily interested in analysis of the rich, complex data in the electronic health record (EHR) and other big databases. If you’re interested in social determinants of health, retrospective analysis of clinical data, or connecting data from multiple sources, this is the pathway for you! This pathway includes a gentle but thorough introduction to SQL, the programming language you’ll need to be able to work with databases, as well as information about working with geospatial data, text data, and more.
Big data, big questions: Modules
| Order | Module | Description | Estimated Time |
|---|---|---|---|
| 1 | Reproducibility, Generalizability, and Reuse | This module provides learners with an approachable introduction to the concepts and impact of research reproducibility, generalizability, and data reuse, and how technical approaches can help make these goals more attainable. | 60 min |
| 2 | Research Data Management Basics | Learn the basics about research data management. | 40 min |
| 3 | Demystifying SQL | SQL is a relational database solution that has been around for decades. Learn more about this technology at a high level, without having to write code. | 40 min |
| 4 | Database Normalization | Learn about the concept of normalization and why it's important for organizing complicated data in relational databases. | 40 min |
| 5 | SQL Basics | Structured Query Language, or SQL, is a relational database solution that has been around for decades. Learn how to do basic SQL queries on single tables, by using code, hands-on. | 60 min |
| 6 | SQL, Intermediate Level | Learn how to do intermediate SQL queries on single tables, by using code, hands-on. | 60 min |
| 7 | SQL Joins | Learn about SQL joins: what they accomplish, and how to write them. | 60 min |
| 8 | Demystifying Geospatial Data | This module is a brief introduction to geospatial (location) data. | 15 min |
| 9 | Encoding Geospatial Data: Latitude and Longitude | This is an introduction to latitude and longitude and the importance of geocoding - encoding geospatial data in the coordinate system. | 15 min |
| 10 | The Elements of Maps | This is a general overview of ways that geospatial data can be communicated visually using maps. | 45 min |
| 11 | Demystifying Regular Expressions | Learn about pattern matching using regular expressions, or regex. | 30 min |
| 12 | Regular Expressions Basics | Begin to use regular expressions, or regex, for simple pattern matching. | 60 min |
| 13 | Regular Expressions: Groups | Use regular expressions, or regex, for complex pattern matching involving capturing and non-capturing groups. | 30 min |
| 14 | Regular Expressions: Flags, Anchors, and Boundaries | Use flags, anchors, and boundaries in regular expressions, or regex, for complex pattern matching. | 45 min |
| 15 | Regular Expressions: Lookaheads | Use regular expressions, or regex, for complex pattern matching involving lookaheads. | 30 min |
| 16 | Demystifying Large Language Models | Learn about large language models (LLM) like ChatGPT. | 60 min |
| 17 | Demystifying Machine Learning | An approachable and practical introduction to machine learning for biomedical researchers. | 60 min |
| 18 | Citizen Science | This is an overview of citizen science for biomedical researchers. | 45 min |
Pathway 4: Analysis in R
This pathway is focuses on the skills and techniques you’ll need to leverage the popular statistical programming language R. We’ll start from zero and walk you through everything you need to start analyzing data in R, including lots of opportunities for hands-on practice.
This is designed to be welcoming to folks with no coding experience, so if R will be your first programming language you’ll fit right in!
Analysis in R: Modules
| Order | Module | Description | Estimated Time |
|---|---|---|---|
| 1 | Reproducibility, Generalizability, and Reuse | This module provides learners with an approachable introduction to the concepts and impact of research reproducibility, generalizability, and data reuse, and how technical approaches can help make these goals more attainable. | 60 min |
| 2 | Tidy Data | Tidy is a technical term in data analysis and describes an optimal way for organizing data that will be analyzed computationally. | 45 min |
| 3 | How to Troubleshoot | Learning to use technical methods like coding and version control in your research inevitably means running into problems. Learn practical methods for troubleshooting and moving past error codes and other difficulties. | 30 min |
| 4 | R Basics: Introduction | Introduction to R and hands-on first steps for brand new beginners. | 60 min |
| 5 | R Basics: Visualizing Data With ggplot2 | Learn how to visualize data using R's ggplot2 package. |
60 min |
| 6 | R Basics: Transforming Data With dplyr | Learn how to transform (or wrangle) data using R's dplyr package. |
60 min |
| 7 | Directories and File Paths | In this module, learners will explore what a directory is and how to describe the location of a file using its file path. | 15 min |
| 8 | R Basics Practice | Use the basics of R coding, data transformation, and data visualization to work with real data. | 60 min |
| 9 | Reshaping Data in R: Long and Wide Data | A module that teaches how to reshape tabular data in R, concentrating on some typical shapes known as "long" and "wide" data. | 60 min |
| 10 | Missing Values in R | A practical demonstration of how missing values show up in R and how to deal with them. Note that this module does not cover statistical approaches for handling missing data, but instead focuses on the code you need to find, work with, and assign missing values in R. | 45 min |
| 11 | Summary Statistics in R | Learn to calculate summary statistics in R, and how to present them in a table for publication. | 30 min |
| 12 | Data Visualization in Open Source Software | Introduction to principles of data visualization and typical data visualization workflows using two common open source libraries: ggplot2 and seaborn. | 20 min |
| 13 | Data Visualization in ggplot2 | This module includes code and explanations for several popular data visualizations, using R's ggplot2 package. It also includes examples of how to modify ggplot2 plots to customize them for different uses (e.g. adhering to journal requirements for visualizations). | 60 min |
| 14 | Introduction to Null Hypothesis Significance Testing | This is an introduction to NHST for biomedical researchers. | 40 min |
| 15 | Statistical Tests in Open Source Software | This module provides an overview of the most commonly used kinds of statistical tests and links to code for running many of them in both R and python. | 20 min |
| 16 | R Practice | Use the basics of R coding, data transformation, and data visualization to work with real data. | 60 min |
| 17 | Demystifying Machine Learning | An approachable and practical introduction to machine learning for biomedical researchers. | 60 min |
| 18 | Understanding the Bias-Variance Tradeoff | The bias-variance tradeoff is a central issue in nearly all machine learning analyses. This module explains what the tradeoff is, why it matters for machine learning, and what you can do to manage it in your own analyses. | 20 min |
Pathway 5: Analysis in Python
Python is a powerful open source programming language with tons of great tools for data science. If you’re looking to learn Python to do things like clean and analyze data, and create data visualizations, this pathway is for you. We’ll start from zero and walk you through everything you need to start analyzing data in Python, including lots of opportunities for hands-on practice.
This is designed to be welcoming to folks with no coding experience, so if Python will be your first programming language you’ll fit right in!
Analysis in Python: Modules
| Order | Module | Description | Estimated Time |
|---|---|---|---|
| 1 | Reproducibility, Generalizability, and Reuse | This module provides learners with an approachable introduction to the concepts and impact of research reproducibility, generalizability, and data reuse, and how technical approaches can help make these goals more attainable. | 60 min |
| 2 | Tidy Data | Tidy is a technical term in data analysis and describes an optimal way for organizing data that will be analyzed computationally. | 45 min |
| 3 | How to Troubleshoot | Learning to use technical methods like coding and version control in your research inevitably means running into problems. Learn practical methods for troubleshooting and moving past error codes and other difficulties. | 30 min |
| 4 | Learning to Learn Data Science | Discover how learning data science is different than learning other subjects. | 20 min |
| 5 | Directories and File Paths | In this module, learners will explore what a directory is and how to describe the location of a file using its file path. | 15 min |
| 6 | Demystifying the Command Line Interface | Understand what the command line interface is and why it's useful! | 15 min |
| 7 | Demystifying Python | This module introduces the Python programming language, explores why Python is useful in research, and describes how to download Python and Jupyter. | 20 min |
| 8 | Python Basics: Functions, Methods, and Variables | Learn the foundations of writing Python code, including the use of functions, methods, and variables. | 20 min |
| 9 | Python Basics: Lists and Dictionaries | Learn about collection objects, specifically lists and dictionaries, in Python. | 15 min |
| 10 | Python Basics: Loops and Conditionals | Learn how to use loops and conditional statements in Python. | 20 min |
| 11 | Python Basics: Exercise | Practice the skills acquired in the Python Basics sequence by working through an exercise. | 30 min |
| 12 | Transform Data with pandas | This is an introduction to transforming data using a Python library named pandas. | 60 min |
| 13 | Tidy Data | Tidy is a technical term in data analysis and describes an optimal way for organizing data that will be analyzed computationally. | 45 min |
| 14 | Data Visualization in Open Source Software | Introduction to principles of data vizualization and typical data vizualization workflows using two common open source libraries: ggplot2 and seaborn. | 20 min |
| 15 | Data Visualization in seaborn | This module includes code and explanations for several popular data visualizations using python's seaborn library. It also includes examples of how to modify seaborn plots to customize them for different uses. | 60 min |
| 16 | Introduction to Null Hypothesis Significance Testing | This is an introduction to NHST for biomedical researchers. | 40 min |
| 17 | Statistical Tests in Open Source Software | This module provides an overview of the most commonly used kinds of statistical tests and links to code for running many of them in both R and python. | 20 min |
| 18 | Python Practice | Use the basics of Python coding, data transformation, and data visualization to work with real data. | 60 min |
| 19 | Demystifying Machine Learning | An approachable and practical introduction to machine learning for biomedical researchers. | 60 min |
| 20 | Understanding the Bias-Variance Tradeoff | The bias-variance tradeoff is a central issue in nearly all machine learning analyses. This module explains what the tradeoff is, why it matters for machine learning, and what you can do to manage it in your own analyses. | 20 min |
Looking for something else?
The suggested pathways above are just that – suggestions! You can work through DART modules in whatever order you like. All of our learning modules are freely available online.
We’re also building a self-service tool to help you find the modules most relevant to you. Test out our prototype module discovery application, and please leave feedback to help us improve!