Have you ever had the experience of opening up an old analysis you did in Jupyter and being completely unable to reproduce the results? Maybe you can't work out where you saved the data you used, or what version of a core dependency you had in your environment. Perhaps your Jupyter notebook is a complete mess and you can't decipher your own code. All you can do is make yourself a big cup of coffee and prepare for a rough week of trying to piece together what you must have done.
If this sounds familiar, you're not alone! Recent studies have found that the work in the vast majority of Jupyter notebooks cannot be reproduced. Being unable to rerun these notebooks means the assumptions and conditions under which the original results were produced can't be recreated, making it difficult to fully understand how data-based decisions or even pieces of intellectual property were made.
In this webinar, Dr. Jodie Burchell will explain some common pitfalls for reproducibility and how you can avoid them by creating reproducible analyses from the outset using Datalore.
Speaker:
Dr. Jodie Burchell is the Developer Advocate in Data Science at JetBrains and was previously Lead Data Scientist in audience generation at Verve Group Europe. After finishing her PhD in psychology and a postdoc in biostatistics, she has worked in a range of data science and machine learning roles across the fields of search improvement, recommendation systems, NLP, and programmatic advertising. She is also the author of two books, The Hitchhiker's Guide to Ggplot2 and The Hitchhiker's Guide to Plotnine, and writes a data science blog. …...more
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