Explores the growing importance of writing vs. coding for senior tech roles, featuring insights from engineers and data scientists on communication and leadership.
Explains the theory behind linear regression models, focusing on interpretability and use cases in fields like lending and medicine.
An interview with an Amazon Applied Scientist describing the daily work, challenges, and projects involved in building ML systems like book recommendations.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A guide to testing machine learning code and systems, covering pre-train and post-train tests, evaluation, and implementation with a DecisionTree example.
Explains how regularly reading academic papers improves data science skills, offering practical advice on selection and application.
Article discusses the 'expert beginner' trap in tech, where narrow success halts learning, and advocates for maintaining a beginner's mindset.
The Dask team shares insights on running successful virtual community tutorials, including benefits for learners and maintainers, and practical logistics.
Explains the importance of post-project follow-up in data science, focusing on code cleanup, Jupyter notebook version control issues, and documentation.
Introduces the 'data exploration calculus', a theoretical model capturing the unique programming patterns used by data scientists and journalists for exploratory data analysis.
A psychology graduate shares his unconventional journey into data science, detailing his career transition and lessons learned to help others.
Highlights key themes from rstudio::conf 2020, including putting R in production, R Markdown advancements, parallel processing, and tidyverse programming.
Explores how applying design thinking principles can improve data science projects by focusing on user needs and storytelling.
A developer shares their experience participating in the free F# mentorship program, both as a mentee and a mentor, and encourages others to join.
Announces version 3.37 of the R 'survey' package, detailing new features for statistical analysis with complex survey data.
A summary of a meetup talk on advanced recommender systems, exploring techniques beyond baselines using graph and NLP methods.
A researcher reviews their 2019 scientific work, focusing on computational statistics for brain imaging and data science.
A professor details the curriculum and practical challenges of teaching an undergraduate 'Data Science Practice' course, covering data prep, predictive models, and tools like R and keras.
A review of Janelle Shane's AI humor book, discussing neural network limitations and the real-world impact of class imbalance in machine learning.
A data scientist's journey from dogmatic Bayesianism to a pragmatic, 'secular' use of Bayesian tools without requiring belief in the model's literal existence.
