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Towards Data Science engagement report

@TDataScience - 251K followers on X

Measured over 72 original posts from a 30-day window, last computed on September 26, 2026.

Engagement

Bottom quarter for its size
Per follower
0.003%
of 251K followers
Per impression
0.221%
3.8K views on a typical post
Reach
1.53%
of its followers see a post
Typical post
8
interactions (median)
Saved
0.156%
6 bookmarks on a typical post
Posting rate
2.7/day
active 33% of days
Peak time
11:00 UTC
Wednesday

A typical post picks up 8 interactions against 251K followers, an engagement rate of 0.003%. Measured over 72 original posts, its engagement rate beats 14% of 15,519 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 3.8K times each, and 0.221% of those impressions turn into an interaction. That is about 1.53% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.7 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 11:00 UTC, and Wednesday is the busiest day of the week. Of the 72 posts sampled, 100% link out. The account's strongest tracked post pulled 46 interactions, about 5.8x its own typical post.

Measured over 72 original posts from a 30-day window, last computed on September 26, 2026.

Compared with accounts its own size

Towards Data Science's engagement rate beats 14% of the tracked X accounts closest to it in follower count (15,519 accounts, accounts of similar size (decile 9 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 14% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.003%, Towards Data Science sits above the 10th percentile of the 156,711 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.022%.

p100.003%
p250.022%
p50 (median)0.127%
p750.604%
p902.32%
p9983.5%
Engagement rate as a share of followers, across the 156,711 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 26,082 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.003%
25th percentile0.022%
50th percentile0.127%
75th percentile0.604%
90th percentile2.32%
99th percentile83.5%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 11:00 UTC, and Wednesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 11:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 11:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Wednesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Wednesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video0% of posts+111%+108% to +115%34K
Outbound link100% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 0% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 100% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
  • Its average post runs 228 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Sep 23, 20265.8x their median

    How does graph engineering work in the context of agentic AI? @nhu_hoang offers a lucid and practical guide to an emerging topic. https://t.co/Cep4eZLqcM

    388006.3K viewsView on X
  • Sep 25, 20263.0x their median

    "I compared Jev with Qwen on 3,080 bank messages, looking not only at which model made the right decision, but also at speed, confidence, and what happened when the available answers did not quite fit the input." @nhu_hoang shares a detailed comparison of LLMs with new "System One" model, Jev. https://t.co/zv525nxvta

    164223.3K viewsView on X
  • Sep 24, 20262.5x their median

    "What interests me the most about Group Relative Policy Optimization, or GRPO as most of us know it, is how little feedback the basic setup needs." @BenjaminNweke11 explains how GRPO trains small language models with verifiable rewards. https://t.co/XT6JCOJZPe

    182004.5K viewsView on X
  • Sep 5, 20262.5x their median

    Choosing between LangChain and LangGraph depends on the complexity of the workflow you need to build. @snr14 explains where each framework fits and when one makes more sense than the other. https://t.co/L4oUL8cUjI

    163105.9K viewsView on X
  • Sep 6, 20262.4x their median

    "Point a normal LLM server at a live robot camera and it will happily flood the VRAM, blow through the control-loop deadlines, and choke on a camera that’s faster than its own brain." Anubhab Banerjee explores the notion of LLMs "forgetting" the right things. https://t.co/puNQiSWTVd

    124214.9K viewsView on X
  • Sep 25, 20262.3x their median

    "I had to figure out a way to make my coding and subscription plans last longer while impacting quality minimally." @EivindKjos shares actionable tips for getting the most out of your AI coding assistants without depleting the credits you purchased in a day. https://t.co/zHoFPEK3UJ

    134103.9K viewsView on X
  • Sep 24, 20262.1x their median

    A scheduled ETL pipeline that pulls the day's top 30 new Python repos from the GitHub Search API, cleans them with pandas, and loads them into SQLite. By Ibrahim Salami https://t.co/VNC98dQEMQ

    161004.7K viewsView on X
  • Sep 7, 20262.1x their median

    Delegating a handful of tasks to Claude Code is great. What about 100+ tasks? @EivindKjos explains what it takes to scale your coding-agent effectiveness. https://t.co/uDvZXZhZOB

    122304.5K viewsView on X
  • Sep 6, 20262.1x their median

    Are vector stores a fallback or a foundation? Which is better, a deterministic dispatcher or an autonomous agent? Kezhan Shi outlines 10 essential positions around enterprise RAG systems. https://t.co/2FiI1rdRpQ

    124105.8K viewsView on X
  • Sep 5, 20262.1x their median

    Running an LLM locally solves the privacy problem, but not the integration problem. Shuai Guo breaks down how structured outputs make local models easier to connect to real-world workflows. https://t.co/v1y1BmjJSp

    134005.0K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

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Reading these numbers

A typical post picks up 8 interactions against 251K followers, an engagement rate of 0.003%. Measured over 72 original posts, its engagement rate beats 14% of 15,519 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 3.8K times each, and 0.221% of those impressions turn into an interaction. That is about 1.53% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.7 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 11:00 UTC, and Wednesday is the busiest day of the week. Of the 72 posts sampled, 100% link out. The account's strongest tracked post pulled 46 interactions, about 5.8x its own typical post.

What is Towards Data Science's engagement rate on X?
Towards Data Science (@TDataScience) has an engagement rate of 0.003%, based on the median interactions across 72 original posts from the last 30 days against 251,431 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.003%, Towards Data Science sits above the 10th percentile of the 156,711 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @TDataScience have real engagement?
Its engagement rate beats 14% of the tracked X accounts closest to it in follower count (15,519 accounts), which puts it in the bottom quarter for its size group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @TDataScience post?
Most posts go out around 11:00 UTC, and Wednesday is its busiest day, at roughly 2.7 posts per day across the measured window.

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