Topic
A/B Testing (9) ai (1) API (1) Artificial Intelligence (32) Automation (6) Automattic (20) best practices (11) Bias in AI (6) Books and Reading (6) Career and Professional Development (14) Causal Inference (1) Communication (15) Conferences and Events (5) data (1) Data Analytics (23) Data Discoverability (4) Data Engineering (12) Data Ethics (10) Data Lineage (1) Data Products (1) Data Science (55) Data Speaker Series (4) Data Visualization (23) Deep Learning (3) Distributed Work (8) Diversity and Inclusion (8) ecommerce (1) Elasticsearch (8) Experimentation (12) Information Retrieval (6) Learning and Education (6) Machine Learning (43) Marketing Analytics (7) Meetup (2) Metadata (1) Natural Language Processing (6) Network Analysis (8) OpenMetadata (1) 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 (1) Technology Trends (1) Time Series Analysis (3) Transparency in Data (2) WooCommerce (1) WordPress (23) WordPress.com (1) Work Life Balance (6)
This week, Boris Gorelik shares his thoughts on a NYU study of “The Persuasive Power of Data Visualization.”
This week, Boris, Demet, Charles, and Sirin offer pieces on data visualization, deep learning architecture, the “Dirtbag Left,” and an obituary for the late Hans Rosling, a man who “had a talent for using numbers to tell exciting stories” — largely about the world becoming a better place. Enjoy our recommendations below and remember to…
Welcome to the third part of our mini-series “Intro to Search.” In my previous posts, I’ve discussed the characteristics of great search results and what a search engine looks like from the inside. But, how do we know if our algorithms actually deliver relevant search results? The answer is, of course, by measurement! There is…
There’s nothing tastier than a set of links to devour! Check out what we’ve been reading recently and be sure to share your links to thought-provoking articles and discussions on topics in the field of data science.
The goal of data visualization is to transform numbers into insights. However, default data visualization output often disappoints. Sometimes, the graph shows irrelevant data or misses important aspects; sometimes, the graph lacks context; sometimes, it’s difficult to read. Often, data practitioners “feel” that something isn’t right with the graph, but cannot pinpoint the problem. In this…