Topic
A/B Testing (9) API (1) Artificial Intelligence (31) Automation (6) Automattic (20) best practices (10) Bias in AI (6) Books and Reading (6) Career and Professional Development (14) Causal Inference (1) Communication (14) Conferences and Events (5) Data Analytics (23) Data Discoverability (4) Data Engineering (12) Data Ethics (10) Data Products (1) Data Science (53) Data Speaker Series (4) Data Visualization (23) Deep Learning (3) Distributed Work (8) Diversity and Inclusion (8) Elasticsearch (8) Experimentation (12) Information Retrieval (6) Learning and Education (6) Machine Learning (43) Marketing Analytics (7) Meetup (2) Natural Language Processing (6) Network Analysis (8) Open Source (7) Productivity (10) Python (8) Remote Work (21) Scientific Communication (10) Semantic Search (2) Social Media Analytics (1) Software Engineering (4) Surveys and Research Methods (1) Tech Industry (14) Technology Trends (1) Time Series Analysis (2) Transparency in Data (2) WordPress (21) WordPress.com (1) Work Life Balance (6)
This week, Boris and Charles bring you two new resources for data reading and pose some questions for discussion on how you approach scientific literature, and you detect and deal with bias inherent in your applications. Looking forward to your comments!