We understand that waiting for a cohort and guided curriculum might be too long to wait, so you are welcome to explore these materials on your own. The benefits of waiting and participating in our research include receiving a customized curriculum of materials and the peership and support of a community of learners like you. To express interest in participating in research, please fill out the DART interest form or go directly to our research screening form.
If you do access any of these modules, please fill out the module feedback survey at the end to let us know what you think.
| Training Course | Description | Estimated Time | Subject Category | Coding Language |
|---|---|---|---|---|
| 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 minutes | Learn to Code | Bash |
| Bash / Command Line 101 | This course will focus on accessing a command line program (or CLI, “command line interface”) and running shell scripts on your home computer and learning how to navigate your file system as well as editing and searching files. | 40 minutes | Learn to Code | Bash |
| Bash: Searching and Organizing Files | This module will teach you how to use the bash shell to search and organize your files. | 30 minutes | Learn to Code | Bash |
| Bash: Conditionals and Loops | This module teaches you how to iterate through “for” loops and write conditional statements in Bash. | 1 hour | Learn to Code | Bash |
| Bash: Reusable Scripts | This module will teach you how to create and use simple Bash scripts to make repetitive tasks as simple as possible. | 1 hour | Learn to Code | Bash |
| Reproducibility, Generalizability, and Reuse: How Technology Can Help | This module provides learners with an approachable introduction to the concepts and impact of research reproducibility, generalizability, and reuse of data, and how technical approaches can help make these goals more attainable. | 1 hour | Introduction to Data Science | none |
| Citizen Science | This is an overview of citizen science for biomedical researchers. | 45 minutes | Introduction to Data Science | none |
| Research Data Management Basics | Learn the basics about research data management. | 40 minutes | Data Management | none |
| Types of Data Storage Solutions | This course will focus on different data storage solutions available to an end user and the unique characteristics of each type. This course will also cover how each storage type impacts one’s access to data and computing capabilities. | 30 minutes | Data Management | none |
| 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 minutes | Data Visualization | none |
| 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 minutes | Statistics | none |
| Introduction to Null Hypothesis Significance Testing | This is an introduction to NHST for biomedical researchers. | 40 minutes | Statistics | none |
| Tidy Data | Tidy is a technical term in data analysis and describes an optimal way for organizing data that will be analyzed computationally. | 45 minutes | Data Cleaning | none |
| 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 minutes | Infrastructure and Technology | none |
| 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. | 15m | Data Management | none |
| Demystifying Geospatial Data | This module is a brief introduction to geospatial (location) data. | 15 minutes | Geospatial Data | none |
| Elements of Maps | This is a general overview of ways that geospatial data can be communicated visually using maps. | 45 minutes | Geospatial Data | none |
| 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 minutes | Geospatial Data | none |
| Intro to Version Control | An introduction to what version control systems do and why you might want to use one. | 10 min | Version Control | none |
| Setting Up Git for 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 | Version Control | Git |
| 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 | Version Control | Git |
| Creating a Git Repository | Create a new Git repository and get started with version control. | 1 hour | Version Control | Git |
| 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 minutes | Version Control | Git |
| 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. | 1 hour | Infrastructure and Technology | Bash |
| 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 minutes | Introduction to Data Science | none |
| Learning to Learn Data Science | Discover how learning data science is different than learning other subjects. | 20 minutes | Introduction to Data Science | none |
| Omics Orientation | This module provides a brief introduction to omics and its associated fields. | 15m | Omics | none |
| 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. | 30m | Omics | Bash |
| Genomics Tools and Methods: Quality Control | 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. | 40m | Omics | Bash |
| Demystifying Python | This module introduces the Python programming language, explores why Python is useful in research, and describes how to download Python and Jupyter. | 20m | Learn to Code | Python |
| Python Basics: Functions, Methods, and Variables | Learn the foundations of writing Python code, including the use of functions, methods, and variables. | 20 min | Learn to Code | Python |
| Python Basics: Lists and Dictionaries | Learn about collection objects, specifically lists and dictionaries, in Python. | 15 min | Learn to Code | Python |
| Python Basics: Loops and Conditionals | Learn how to use loops and conditional statements in Python. | 20 min | Learn to Code | Python |
| Transform Data with pandas | This is an introduction to transforming data using a Python library named pandas. | 1 hour | Data Transformation | Python |
| 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. | 1 hour | Data Visualization | Python |
| Python Practice | Use the basics of Python coding, data transformation, and data visualization to work with real data. | 1 hour | Coding Exercise | Python |
| R Basics: Introduction | Introduction to R and hands-on first steps for brand new beginners. | 1 hour | Learn to Code | R |
| R Basics: Transforming Data With dplyr | Learn how to transform (or wrangle) data using R’s dplyr package. |
1 hour | Data Transformation | R |
| R Basics: Visualizing Data With ggplot2 | Learn how to visualize data using R’s ggplot2 package. |
1 hour | Data Visualization | R |
| 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 | Data Cleaning | R |
| Summary Statistics in R | Learn to calculate summary statistics in R, and how to present them in a table for publication. | 30 min | Statistics | R |
| 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 | Data Visualization | R |
| 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. | 1 hour | Data Cleaning | R |
| R Practice | Use the basics of R coding, data transformation, and data visualization to work with real data. | 1 hour | Coding Exercise | R |
| 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 minutes | Learn to Code | SQL |
| Database Normalization | Learn about the concept of normalization and why it’s important for organizing complicated data in relational databases. | 40 minutes | Data Management | SQL |
| 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. | 1 hour | Learn to Code | SQL |
| SQL, Intermediate Level | Learn how to do intermediate SQL queries on single tables, by using code, hands-on. | 1 hour | Learn to Code | SQL |
| SQL Joins | Learn about SQL joins: what they accomplish, and how to write them. | 1 hour | Learn to Code | SQL |
| Demystifying Machine Learning | An approachable and practical introduction to machine learning for biomedical researchers. | 1 hour | Introduction to Data Science | none |
| 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 minutes | Introduction to Data Science | none |
| Demystifying Large Language Models | Learn about large language models (LLM) like ChatGPT. | 1 hour | Introduction to Data Science | none |
| Using the REDCap API | REDCap is a research data capture tool used by many researchers in basic, translational, and clinical research efforts. Learn how to use the REDCap API in this module. | 1 hour | Data Management | none |
| Demystifying Regular Expressions | Learn about pattern matching using regular expressions, or regex. | 30 minutes | Learn to Code | none |
| Regular Expressions Basics | Begin to use regular expressions, or regex, for simple pattern matching. | 60 minutes | Learn to Code | none |
| Regular Expressions: Groups | Use regular expressions, or regex, for complex pattern matching involving capturing and non-capturing groups. | 30 minutes | Learn to Code | none |
| Regular Expressions: Flags, Anchors, and Boundaries | Use flags, anchors, and boundaries in regular expressions, or regex, for complex pattern matching. | 45 minutes | Learn to Code | none |
| Regular Expressions: Lookaheads | Use regular expressions, or regex, for complex pattern matching involving lookaheads. | 30 minutes | Learn to Code | none |