Learning Outcomes What you will know

You understand how to succeed in the HI-DSI Data Fellows program

• You understand the goals, structures, and procedures for success as a participant in HI-DSI Data Fellows program.
• Your computer and its software is adequate to support the experiences you will have in this program.
• You know how to successfully onboard as an HI-DSI Data Fellow.

Referencing modules: Introduction to HI-DSI Data Fellows

You understand how to work effectively in a group

You can develop complex software efficiently and effectively as part of a team, using collaboration tools including GitHub.

Referencing modules: Configuration management

You understand basic Python and Jupyter

• You understand the basic syntax of Python.
• You can install and use a Python interpreter on your computer.
• You can install and use Jupyter Notebooks on your computer.

Referencing modules: Python and Jupyter

You understand and can apply FAIR data management practices

• You understand basic FAIR (Findable, Accessible, Interoperable, Reusable) concepts.
• You recognize that engaging in data management early can help you and others understand your project.
• You recognize the barriers and risks in the adoption of Open Science practices.
• You understand intellectual property, licensing, and openness.
• You understand the components that uniquely identifies a research publication.
• You have learned how to identify a FAIR dataset.
• You understand what metadata is and how to distinguish between different types of metadata.
• You understand privacy, freedom, explainability, and fairness as they go into managing data ethics.
• You have (potentially) identified useful changes to your current habits regarding data management.

Referencing modules: FAIR Data Management, Security and Ethics

Assessed by: Help us assess this workshop

You can use configuration management tools and techniques effectively

• You understand the motivation for version control and configuration management
• You can use git commands to support version control.
• You can set up a GitHub account and use it to manage files.
• You can implement project enhancements by creating and merging branches.

Referencing modules: Configuration management

You can perform basic command line functions

• You have an account on Mana and can login to it.
• You can navigate the Mana linux environment using shell commands
• You can create, copy, and delete files.
• You can combine commands using pipes.
• You can loop over commands
• You can create simple shell scripts.
• You can use the find and grep commands to locate files containing strings

Referencing modules: Scientific Software Basics

Assessed by: Help us assess this workshop

You understand basic HPC concepts

• How to access Open OnDemand and use Jupyter Lab on Mana
• How to request GPUs and utilize them for deep learning tasks
• The performance difference between CPU and GPU for deep learning
• Different types of file system found on HPC clusters
• How correct file system choice affect deep learning performance

Referencing modules: High Performance Computing

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You understand the challenges and options in moving scientific data over the network

• Demonstrate understanding of advantages and disadvantages of various bulk data transfer tools.
• Understand appropriate solutions for a data transfer workflow, taking into account current computing, storage and network infrastructure, and end-point sites.
• Demonstrate the ability to identify, communicate, and mitigate potential bottlenecks in collaboration with campus cyberinfrastructure and network operators.
• Transfer data using Rclone.
• Transfer data using Globus.

Referencing modules: Data Movement

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You understand basic data wrangling concepts

• Understand how to use notebooks to clean and wrangle climatological data
• Understand how to combined code and documentation to allow for reproducible analyses

Referencing modules: Data Wrangling

Assessed by: Help us assess this workshop

You understand basic machine learning concepts

• How to build a machine learning classifier using Scikit-learn
• How to build a neural network model in Pytorch for timeseries
• How to use a pretrained large language model for NLP.

Referencing modules: Machine Learning Approaches in Climate Science

Assessed by: Help us assess this workshop

You understand basic data visualization concepts

• Understand the role of data visualization in the research enterprise
• Increase your awareness of what makes good and bad visualizations
• Know basic steps for creating effective visualizations
• Know about contemporary tools for creating visualizations

Referencing modules: Data Visualization

Assessed by: Help us assess this workshop

You understand smart data collection

• You understand basic concepts of the Tapis Streams API and Abaco.
• You can deploy and access a Jupyter Notebook using Mana.
• You can use the Streams API to mimic a real-world sensor network.

Referencing modules: Smart Data Collection for Sensor Networks

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You understand important concepts about creative thinking

• You know what science says about creativity.
• You know about the conditions necessary for creative thinking.
• You understand the impact of collaboration on creativity.
• You are aware of techniques for creative thinking.
• You have seen examples of creative thinking in practice.
• You have practiced creative thinking techniques.

Referencing modules: Creative Thinking

Assessed by: Help us assess this workshop