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
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%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.003% |
| 25th percentile | 0.022% |
| 50th percentile | 0.127% |
| 75th percentile | 0.604% |
| 90th percentile | 2.32% |
| 99th percentile | 83.5% |
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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 0% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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.