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Data Science Weekly – Issue 658
23 days ago
- Data Science Weekly newsletter #659 from June 26, 2026, curated by Hannah & Sebastian, covers Data Science, AI, ML, Data Visualization, and ML/Data Engineering topics with editor's picks, articles, and community polls.
- Editor's picks include an article on colors beyond digital display, using Bradley-Terry model for dog treat preferences, and generative AI creating optimized burgers for taste, sustainability, and nutrition.
- Notable articles discuss open-source ATS scoring resumes variably, underrated data science skills, AI's role in data science, vibe coding vs. engineering, regression coefficient issues, and tools like Rust tensor stacks, Typst graphics, geo-experiment benchmarks, and CUDA kernel processes.
Book on Probability and Statistics for Data Science with videos / code
21 days ago
- Materials for the book 'Probability and Statistics for Data Science' include a free preprint, 103 Python notebooks using 23 real-world datasets, 118 videos with slides, and solutions to 200 exercises.
- The book covers probability theory and statistics, focusing on topics like random variables, models, correlation, estimation, hypothesis testing, principal component analysis, and regression/classification methods, using real-world datasets to address challenges like overfitting and causal inference.
- Authored by Carlos Fernandez-Granda, an Associate Professor with 10 years of teaching experience, who provides learning advice and acknowledges contributors for typos and support from the National Science Foundation.
Mathematics of Data Science
9 days ago
- The book covers the mathematical foundations of data science, including topics like high-dimensional spaces, singular value decomposition, linear regression, graphs and networks, and deep learning.
- It addresses key techniques such as dimensionality reduction (both linear and nonlinear), optimization, classification, compressive sensing, and matrix recovery.
- The text also explores theoretical aspects like concentration of measure, Gaussian analysis, and matrix concentration inequalities, providing a comprehensive introduction to the mathematics behind data science.

data science

Data Science Weekly – Issue 658

Book on Probability and Statistics for Data Science with videos / code

Mathematics of Data Science