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Towards Data Science

@TDataScience · We're Global 🌏 · joined 20 Oct 2016

The world's leading publication for data science and artificial intelligence professionals. Submit an Article ✍️ https://t.co/57pIMegK1o

251 128Followers
1 919Following
64 167Posts total
18.8KViews on collected posts

Derniers posts

"You will learn how to parameterize weight distributions, run variational inference, and extract actionable uncertainty bounds that reveal exactly when your model should and shouldn't be trusted." Tom Narock presents a hands-on introduction to Bayesian neural networks and their 1K views · 2 likes · 1 reposts · 1 replies Open on X →
We usually think of missing data as a problem that needs to be solved or an absence that needs to be filled. David Conneely makes a well-argued case for the intrinsic value of missingness and what it can reveal about the data we observe. https://t.co/5rMuyYMRne 3.4K views · 9 likes · 0 reposts · 0 replies Open on X →
From enum and array hallucinations to distributional collapse, Mostafa Ibrahim outlines the various ways that LLM structured outputs can look right on the surface while hiding serious, hard-to-catch data errors. https://t.co/sQ2LMVgRpm 3.5K views · 5 likes · 0 reposts · 0 replies Open on X →
An API that ingests 10,000–20,000 developer-community posts a day and turns them into keyword trends, sentiment, and sourced summaries — for about $30–40 a month. By Ida Silfverskiold https://t.co/xWgvJ9URQn 4.3K views · 7 likes · 0 reposts · 0 replies Open on X →
In a thorough, hands-on deep dive, @tahreemrasul1 presents a framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking. https://t.co/TIoZrRbFwJ 3.9K views · 6 likes · 0 reposts · 0 replies Open on X →
📢 CALLING ALL AUTHORS 📢 Share your data science insights and get published on Towards Data Science. Tap the link to learn more. https://t.co/ympeAXAkwj 2.8K views · 11 likes · 2 reposts · 0 replies Open on X →

Face aux comptes de taille comparable

5 posts des 90 derniers jours, à côté de la tranche de 100K–1M abonnés. portée ordinaire pour sa taille, réaction plus faible que la moyenne.

Vues médianes3 483ce compte5 899médiane pour 100K–1M
Portée, %1.39%ce compte1.59%médiane pour 100K–1M
Engagement, %0.16%ce compte1.04%médiane pour 100K–1M
IndicateurCe compteMédiane pour 100K–1MRapport
Vues médianes par post3 4835 8990.59×
Portée (vues ÷ abonnés)1.39%1.59%0.87×
Taux d'engagement0.16%1.04%0.16×

Autres comptes de cette tranche →   Comparer avec un autre compte →   Comment ces repères sont établis →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

2.8K23 Feb
3.9K12 Sep
4.3K
3.5K13 Sep
3.4K
1K

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.47%23 Feb
0.16%12 Sep
0.16%
0.14%13 Sep
0.27%
0.39%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.04%.

What the audience does

Likes55.6%40 in total
Reposts4.2%3 in total
Replies1.4%1 in total
Bookmarks38.9%28 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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