# Project Brief - Research Data Science ## Self-paced online learning course materials :::info ### Project background The Turing's Research Engineering Group (REG) have been delivering two live courses over the last few years, which are in high demand within the Turing community and beyond. They are currently only run once a year, and to a limited number of participants due to the resource-intensive nature of planning and delivering a live course. Previous participants and instructors have also identified a number of areas for improvement. **1. Introduction to Research Data Science** https://alan-turing-institute.github.io/rds-course/ https://github.com/alan-turing-institute/rds-course **2. Research Software Engineering with Python** https://alan-turing-institute.github.io/rse-course/html/index.html https://github.com/alan-turing-institute/rse-course ### Objectives Beginning with 1. Introduction to Research Data Science, we aim to: - Review and improve existing materials - Revise or create materials to ensure they are suitable for self-paced online learning - Create instructor guidance so the materials can also be taught in a live/blended learning setting ::: ### Milestones This will be a multi-stage project. Output from Milestone 1 will be tested and evaluated, and then we will proceed to Milestones 2-3 if applicable. **Milestone 1: Sample** Using the information provided (1 module), deliverables are as follows: - A detailed plan for the revised module - Materials for one lesson from that module plan (Depending on outcomes from Milestone 1) **Milestone 2: Full course plan** Deliverables: - Review of existing materials - Detailed plan/outline of revised course **Milestone 3: Complete course materials** Deliverables: - All course materials as per plan from Milestone 2, which should include: - Learning content - Video content - Teacher/instructor guidance - Coding activities with sample solutions - Any datasets or supplementary resources Milestone 3 will be carried out according to agile methodology, and materials will be released module by module. ### Target audience - PhD-level students who need to work on collaborative data science projects - Early career professionals from a range of backgrounds looking to break into data science ### Requirements - **License**: all Turing courses must be released under a CC BY 4.0 license, and contributors acknowledged. Any third party content used within the course must have appropriate permissions. - **Modularisation**: Content should be modularised - sections should be as independent as possible, so that users do not need to complete the course in a certain order & also offer users the option of only taking modules of interest - **Videos** must not exceed 15 minutes each - **Interactions**: Learners must have opportunities to check their understanding and interact with the content in the absence of live instructor support, e.g. through quizzes, coding activities or other tasks - **Practical applications**: Modules/blocks should include a practical application of concepts, ideally domain agnostic but with possibility to add/apply to specific use cases - **Platform:** Core materials must be built on the Turing’s Online Learning Platform (Moodle) - Where required activities are not compatible with Moodle (i.e. certain coding activities) they may be built externally and linked or embedded - **Longevity**: Material should be as future-proof as possible (particularly the videos) ### Budget ... ### Invoicing/payment schedule The supplier may invoice: - Upon completion of Phase 1 - Upon completeion of Phase 2 - Upon delivery of each module within Phase 3 The Turing's standard terms are payment within 30 days of receipt of invoice.