Data Science around intermittent fasting and data science based Nutrition
I have experimented mainly with Daily fasts of 16 hours or 20 hours. As a data scientist, nutritionist and serious athlete, I also come with data. I have data from 2011-2019 of my body weight.
Datascience meets intermittent fasting
Weight gain by macronutrient and insulin response
A. High Carbohydrate foods appear to be the strongest correlation with weight gain.
B. Fat and Protein are essentially neutral
C. Total Calories and glycemic index are equally as correlated with weight gain.
One firm conclusion would be to avoid calorically dense, high carbohydrate foods that have a high glycemic index: i.e. French Fries.
- Glycemic index for 60+ foods
- https://en.wikipedia.org/wiki/Citric_acid_cycle
- https://www.health.harvard.edu/staying-healthy/eating-more-ultra-processed-foods-may-shorten-life-span
- https://en.wikipedia.org/wiki/Travelling_salesman_problem
- https://github.com/noahgift/or
- https://www.dietdoctor.com/intermittent-fasting
- https://github.com/noahgift/intermittent-fasting/blob/master/intermittent_fasting.ipynb
- https://www.nejm.org/doi/10.1056/NEJMra1905136
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3151731/
- FoodData Central USDA
His most recent books are:
- Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)
- Python for DevOps (O'Reilly, 2020).
His most recent video courses are:
- Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)
- AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)
- Python for Data Science Complete Video Course Video Training (Pearson, 2019)
- AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)
- Building A.I. Applications on Google Cloud Platform (Pearson, 2019)
- Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)
- Data Engineering with Python and AWS Lambda (Pearson, 2019)
His most recent online courses are: