These are chat archives for FreeCodeCamp/DataScience
discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
A PeerJ (biomedical science journal) collection on Practical Data Science for Stats.
The "Practical Data Science for Stats" Collection contains preprints focusing on the practical side of data science workflows and statistical analysis. Curated by Jennifer Bryan and Hadley Wickham.
There are many aspects of day-to-day analytical work that are almost absent from the conventional statistics literature and curriculum. And yet these activities account for a considerable share of the time and effort of data analysts and applied statisticians.
The goal of this collection is to increase the visibility and adoption of modern data analytical workflows.
We aim to facilitate the transfer of tools and frameworks
- between industry and academia
- between software engineering and Stats/CS
- across different domains
def least_squared_error(x, y, thetta): error_sum = 0 m, n = x.shape for j in range(0, m): for i in range(1, n): error_sum += (thetta.T[i] * x[i] - y[i]) ** 2 return (error_sum) / (2 * m)