A curated list of five interesting Python tutorials covering music generation, computer vision, data science, and popular modules.
A guide for academics with math/physics backgrounds transitioning into data science, covering skills, learning paths, and practical advice.
A curated list of resources for beginners to learn Python specifically for data science, including tutorials, courses, and books.
A guide on creating a custom Docker container for R data science work, including installing packages and visualizing data.
A summary of a talk on achieving top 3% in a Kaggle competition, covering validation, feature engineering, and ensemble techniques.
A guide for Python/R users transitioning to Java, focusing on the necessity of IDEs like Eclipse or IntelliJ for productive development.
Interview with data scientist Jeroen Janssens about his background, work on data science at the command line, and his Data Science Toolbox project.
Announcing EuroSciPy 2015, the European conference on Python for scientific computing, with calls for papers, talks, and tutorials.
Exploring the IBash Notebook, a Bash kernel for Jupyter, and its potential as a data science environment with inline image support.
Author's 2014 review: writing a data science book from scratch in Python and preparing for/starting a software engineering job at Google.
A review of the book 'Data Science at the Command Line', highlighting its approach to data manipulation and analysis using command-line tools.
An interactive exploration using IPython to simulate and understand the mathematical model behind 'The Hipster Effect' paper on conformity and non-conformity.
A response to a critique of the author's introductory series on Frequentist vs. Bayesian statistics, focusing on audience and the role of decision theory.
A reflection on the challenges of data science in academia, discussing the 'brain drain' of data skills and the need for systemic change.
A report on the 2014 scikit-learn developer sprint in Paris, covering participants, venues, achievements, and sponsors.
Explores how personas, data science, and k-means clustering can be used together to analyze user data and gain actionable business insights.
A guide to using the Unix command-line for efficient data science workflows, including data processing, exploration, and modeling.
A guide to setting up a remote IPython Notebook server on Amazon EC2 for data science and analytics.
Explores how the demand for big data skills in industry is draining talent from academic science, threatening research.
A guide to seven essential command-line tools (jq, csvkit, Rio, etc.) for data scientists to obtain, scrub, explore, and model data.
