This curriculum was designed for high school students with no prior coding experience who are interested in learning Python programming for data science. However, this course material would be useful for anyone interested in teaching or learning basic programming for data analysis.

The curriculum features short lessons to deliver course material in “bite sized” chunks, followed by practices to solidify the learners' understanding. Pre-recorded videos of lessons enable effective virtual learning and flipped classroom approaches.

The learning objectives of this curriculum are:

1. Write code in Python with correct syntax and following best practices.
2. Implement fundamental programming concepts when presented with a programmatic problem set.
3. Apply data analysis to real world data to answer scientific questions.
4. Create informative summary statistics and data visualizations in Python.
5. These skills provide a solid foundation for basic data analysis in Python. Participation in our program exposes students to the many ways coding and data science can be impactful across many disciplines.

Our curriculum design consists of 27 lessons broken up into 5 modules that cover Jupyter notebook setup, Python coding fundamentals, use of essential data science packages including pandas and numpy, basic statistical analyses, and plotting using seaborn and matplotlib. Each lesson consists of a lesson notebook, used for teaching the concept via live coding, and a practice notebook containing similar exercises for the student to complete on their own following the lesson. Each lesson builds on those before it, beginning with relevant content reminders from the previous lessons and ending with a concise summary of the skills presented within.

The past decade has seen rapid growth in conversations around and progress towards fostering a more transparent, open, and cumulative science. Best practices are being codified and established across fields relevant to gerontology from cancer science to psychological science. Many of the areas currently under development are of particular relevance to gerontologists such as best practices in balancing open science with participant confidentiality or best practices for preregistering archival, longitudinal data analysis. The present panel showcases one of the particular strengths of the open science movement - the contribution that early career researchers are making to these ongoing conversations on best practices. Early career researchers have the opportunity to blend their expertise with technology, their knowledge of their disciplines, and their vision for the future in shaping these conversations. In this panel, three early career researchers share their insights. Pfund presents an introduction to preregistration and the value of preregistration from the perspective of “growing up” within the open science movement. Seaman discusses efforts in and tools for transparency and reproducibility in neuroimaging of aging research. Ludwig introduces the idea of registered reports as a particularly useful form of publication for researchers who use longitudinal methods and/or those who work with hard-to-access samples. The symposium will include time for the audience to engage the panel in questions and discussion about current efforts in and future directions for transparent, open, and cumulative science efforts in gerontology.