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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

Neueste Beiträge

"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 →

Im Vergleich zu Konten gleicher Größe

5 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 100K–1M Follower. übliche Reichweite für diese Größe, schwächere Reaktion als bei den meisten.

Medianaufrufe3 483dieses Konto5 899Median für 100K–1M
Reichweite, %1.39%dieses Konto1.59%Median für 100K–1M
Interaktion, %0.16%dieses Konto1.04%Median für 100K–1M
KennzahlDieses KontoMedian für 100K–1MVerhältnis
Medianaufrufe pro Beitrag3 4835 8990.59×
Reichweite (Aufrufe ÷ Follower)1.39%1.59%0.87×
Interaktionsrate0.16%1.04%0.16×

Weitere Konten dieser Größe →   Mit einem anderen Konto vergleichen →   Wie diese Vergleichswerte entstehen →

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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