Announcing the 2021 Ihaka Lectures featuring local experts on distributed computing, machine learning for child welfare, and applied math for COVID-19 response.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A podcast transcript discussing the importance of writing for career growth in tech, covering motivation, process, and Amazon's writing culture.
A data scientist's 2020 review, focusing on machine learning projects for healthcare, including mining COVID-19 EHR data and brain signal analysis.
A data visualization designer reflects on their 2020 freelance work, challenge contributions, and personal projects using R, ggplot2, and plotly.
A personal review of 2020 focusing on the growth of the #TidyTuesday data visualization project in the R community, with code and analysis.
A data scientist's 2020 reflection on moving to Amazon, building ML systems, and establishing a weekly writing habit for learning and sharing knowledge.
An interview with lead data scientist Alexey Grigorev on his career transition from software engineering to data science, his advice, and his work at OLX.
Explains the differences between Applied Scientist, Research Scientist, and ML Engineer roles in data science and machine learning.
Discusses the pitfalls of overengineering machine learning models for business, contrasting Kaggle's optimization goals with real-world value creation.
Analyzes the challenges of using data science and scientific advice for Covid-19 policy, comparing it to the gap between scientists and policymakers.
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.
