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Channel

Data science/ML/AI

@datascience_bds

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Polls · Citations · Telegram's recommendations · Cite this entry

13,964subscribers

+125 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001237015560
TypeChannel
Username@datascience_bds
CreatedBetween 1 March 2018 and 31 July 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live27 September 2026
Measurements held31
Confirmed unchanged1 time, most recently 27 September 2026
On Telegramt.me/datascience_bds

Topic

Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 87% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Growth

13,83913,96413,901.57 August 2026 — 13,839 subscribers8 August 2026 — 13,866 subscribers9 August 2026 — 13,874 subscribers10 August 2026 — 13,880 subscribers11 August 2026 — 13,877 subscribers12 August 2026 — 13,879 subscribers12 August 2026 — 13,880 subscribers14 August 2026 — 13,882 subscribers15 August 2026 — 13,892 subscribers16 August 2026 — 13,899 subscribers17 August 2026 — 13,904 subscribers18 August 2026 — 13,902 subscribers19 August 2026 — 13,906 subscribers20 August 2026 — 13,908 subscribers21 August 2026 — 13,904 subscribers23 August 2026 — 13,905 subscribers24 August 2026 — 13,904 subscribers26 August 2026 — 13,898 subscribers27 August 2026 — 13,901 subscribers28 August 2026 — 13,898 subscribers29 August 2026 — 13,899 subscribers30 August 2026 — 13,906 subscribers31 August 2026 — 13,908 subscribers1 September 2026 — 13,911 subscribers2 September 2026 — 13,915 subscribers6 September 2026 — 13,920 subscribers9 September 2026 — 13,917 subscribers12 September 2026 — 13,907 subscribers13 September 2026 — 13,923 subscribers17 September 2026 — 13,926 subscribers27 September 2026 — 13,964 subscribers7 August 202627 September 2026
31 measurements spanning 52 days, net +125. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 13,820–13,983 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 31
Measured (UTC)SubscribersChange
27 Sept 2026, 14:1513,964+38
17 Sept 2026, 13:0213,926+3
13 Sept 2026, 18:0213,923+16
12 Sept 2026, 00:1813,907-10
9 Sept 2026, 17:5913,917-3
6 Sept 2026, 11:5813,920+5
2 Sept 2026, 15:3413,915+4
1 Sept 2026, 17:5313,911+3
31 Aug 2026, 14:3213,908+2
30 Aug 2026, 16:4713,906+7
29 Aug 2026, 18:1513,899+1
28 Aug 2026, 17:5513,898-3
27 Aug 2026, 15:5813,901+3
26 Aug 2026, 18:2213,898-6
24 Aug 2026, 16:2813,904-1
23 Aug 2026, 04:1313,905+1
21 Aug 2026, 18:3813,904-4
20 Aug 2026, 17:5613,908+2
19 Aug 2026, 19:3513,906+4
18 Aug 2026, 20:4313,902first reading

Engagement

50 posts held, back to 12 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 51 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
3.09%
avg views ÷ 13,964 subscribers
Avg views / post
431
7 posts measured
Reaction rate
1.02%
reactions ÷ views · ER floor
Posts in window
7
of 50 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 6 of 7 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 2 September 2026
Posts held50 (12 July 2026 – 2 September 2026)
Views total3,020
Reactions total26
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken2 Sept 2026, 19:30 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

What this channel posts

Video runtime
1m 08s
Average length
23s

Measured directly from 3 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.

Reaction mix

290 reactions across 49 posts, in 5 distinct kinds. The most used accounts for 81.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤23781.7%
🔥3712.8%
👍134.48%
👏20.69%
😁10.345%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 49 of the 50 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 290 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 50 most recent posts we hold, published 12 July 2026 to 2 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

2 Sept 2026, 07:25 UTC221 views5 reactionsread 2 September 2026

From Zero to Data Scientist This is a free, open-source curriculum from Microsoft's Azure Cloud Advocates team that breaks data science down into 20 digestible lessons spread across 10 weeks. 👉 Free curriculum with quizzes and assignments 👉 No prior experience needed to start 👉 It's Project-based so you're building a portfolio as you learn 👉 Created by Microsoft experts and students 👉 Has a strong discord community…

❤5

1 Sept 2026, 15:02 UTC330 views6 reactionsread 2 September 2026
Photo

🧩 Why One-Hot Encoding Exists Machine learning models don't understand words. They understand numbers. So how do you feed a value like: Color = Red You can't simply write: Red = 1 Blue = 2 Green = 3 The model might think Green > Blue > Red, even though colors have no natural order. Instead, we create separate columns: Red 1 0 0 Blue 0 1 0 Green 0 0 1 This is called One-Hot Encoding. It represents categorie…

❤6

31 Aug 2026, 11:01 UTC433 views5 reactionsread 2 September 2026
Video

🚨 UC Berkeley just open-sourced FreeToken. It claims 2–4× faster local LLM inference than Ollama, and the wild part is the models it can run: • Qwen3.6-35B on 8GB VRAM → 39.3 tok/s • DeepSeek-V4-Flash 284B on 32GB VRAM → 22 tok/s • GLM-5.2 753B on 96GB VRAM → 14.9 tok/s How? These are Mixture-of-Experts models. A 35B model doesn't actually use all 35B parameters for every token. FreeToken keeps the experts in syst…

❤3🔥2

30 Aug 2026, 12:45 UTC486 views2 reactionsread 2 September 2026
Forwarded from @programming_quizzPoll

What does this query return?

  1. All employees earning more than the overall company average14%
  2. Employees earning more than the average salary within their own department77%
  3. A syntax error, since subqueries can't reference the outer query7%
  4. The single highest paid employee company-wide1%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

❤2

30 Aug 2026, 12:45 UTC476 viewsread 2 September 2026
Forwarded from @programming_quizz

Topic: SQL 🔍 Quick look before the question: SELECT e.name, e.salary FROM employees e WHERE e.salary > ( SELECT AVG(salary) FROM employees WHERE department = e.department );

30 Aug 2026, 10:01 UTC500 views4 reactionsread 2 September 2026

🐼 One Pandas Function That Can Save You From Ugly if/else Suppose you want to classify customers: spending >= 1000 → VIP spending >= 500 → Regular otherwise → Low You could write a complicated function. Or: import numpy as np df["segment"] = np.select( [ df["spending"] >= 1000, df["spending"] >= 500 ], [ "VIP", "Regular" ], default="Low" ) Now the rules a…

❤4

29 Aug 2026, 08:04 UTC574 views4 reactionsread 2 September 2026

📚 12 Free Statistics Resources Worth Bookmarking If your statistics foundation is weak, you'll spend more time memorizing algorithms than understanding them. Here are 12 resources that are genuinely worth your time. 1. Penn State STAT Online Complete university-level statistics courses available for free. 2. Seeing Theory Interactive visualizations that make probability and statistics much easier to grasp. 3. Ope…

🔥4

28 Aug 2026, 08:10 UTC602 views4 reactionsread 2 September 2026
Video

RAG vs. Graph RAG vs. Agentic RAG Standard RAG embeds documents into vectors and retrieves similar chunks. Effective for direct factual lookups, but fails when queries require connecting facts across multiple documents, as similarity search misses relationships between chunks. Graph RAG adds a knowledge graph layer. An LLM extracts entities and relationships during indexing; retrieval traverses these connections ra…

❤4

27 Aug 2026, 13:10 UTC625 views4 reactionsread 2 September 2026
Forwarded from @ai_revolution_bdsVideo

The #1 problem with local AI seems to be solved now. There’s a free tool called canIrun.ai that checks your hardware and tells you which models will actually run well before you download anything. So instead of guessing and hitting out-of-memory errors…it grades every model against your machine. What it does (right in your browser, no install): → detects your setup (RAM / CPU / GPU / VRAM) → scores each model for …

❤4

27 Aug 2026, 07:45 UTC651 views5 reactionsread 2 September 2026
Photo

Normal Distribution vs t-Distribution

❤4🔥1

26 Aug 2026, 08:02 UTC737 views6 reactionsread 2 September 2026

📈 Mean vs Median Suppose these are five salaries: $35k, $38k, $42k, $44k, $2.5M Mean (average): $531,800 Median (middle value): $42,000 The average suggests everyone is wealthy. The median tells a completely different story. 👉 Whenever your data contains extreme values (called outliers), the median often represents the data much better than the mean. That's why you'll often see median house prices and median inco…

❤6

Showing the 12 most recent of 50 posts we hold for @datascience_bds. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Polls

The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.

30 Aug 2026, 12:45 UTCAnonymous Quiz84 voters

What does this query return?

  1. All employees earning more than the overall company average14%
  2. Employees earning more than the average salary within their own department77%
  3. A syntax error, since subqueries can't reference the outer query7%
  4. The single highest paid employee company-wide1%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.

The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.

Read from the 50 most recent posts we hold, published 12 July 2026 to 2 September 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.

Forward network

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

Named by

Channels on the register whose posts name this channel's handle.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Programming, data science, ML - free courses by Big Data Specialist
@bigdataspecialist · 26,486
Telegram ranks this channel #1 of 80 here — alongside 79 others — read 12 September 2026
Ai Tools: ChatGPT, Midjourney, Deepseek
@OpenAI_Mastery · 50,064
Telegram ranks this channel #12 of 65 here — alongside 64 others — read 25 August 2026
E-learning
@elearninglinks · 34,287
Telegram ranks this channel #13 of 31 here — alongside 30 others — read 3 September 2026
Power BI & Tableau Resources
@PowerBI_analyst · 55,842
Telegram ranks this channel #14 of 84 here — alongside 83 others — read 23 August 2026
Data Science & Machine Learning Free Resources
@datascience69 · 20,492
Telegram ranks this channel #16 of 82 here — alongside 81 others — read 27 September 2026
Generative AI
@generativeai_gpt · 30,868
Telegram ranks this channel #16 of 82 here — alongside 81 others — read 7 September 2026
CloudyML - Data Science & Analytics
@cloudymlofficial · 47,680
Telegram ranks this channel #16 of 88 here — alongside 87 others — read 26 August 2026
AI and Machine Learning
@machine_learning_courses · 95,724
Telegram ranks this channel #16 of 74 here — alongside 73 others — read 16 August 2026
Data Engineers❤️
@shubham_wadekar_jobs · 24,844
Telegram ranks this channel #17 of 93 here — alongside 92 others — read 15 September 2026
CSAT UPSC CSE Mathematics, Arithmetic, Apptitute, Reasoning. Mathematics for All. UPSC CSAT CAT MAT
@CSATMathematics · 32,206
Telegram ranks this channel #18 of 66 here — alongside 65 others — read 5 September 2026
Remote Jobs — Daily LinkedIn Picks 💼
@Jobs204 · 22,576
Telegram ranks this channel #20 of 80 here — alongside 79 others — read 20 September 2026
Freelancer | Freelance | Find Jobs & Projects
@Freelanceroff · 32,941
Telegram ranks this channel #20 of 88 here — alongside 87 others — read 4 September 2026
Machine Learning
@MachineLearning9 · 41,424
Telegram ranks this channel #20 of 79 here — alongside 78 others — read 30 August 2026
Learn Python Coding
@pythonRe · 40,364
Telegram ranks this channel #21 of 83 here — alongside 82 others — read 30 August 2026
Kaggle Data Hub
@datasets1 · 29,023
Telegram ranks this channel #22 of 84 here — alongside 83 others — read 9 September 2026
Data Analytics & AI | SQL Interviews | Power BI Resources
@Data_Visual · 27,546
Telegram ranks this channel #23 of 83 here — alongside 82 others — read 10 September 2026
Artificial Intelligence & ChatGPT Prompts
@Curiousprogrammer · 42,238
Telegram ranks this channel #25 of 81 here — alongside 80 others — read 29 August 2026
MS Excel for Data Analysis
@excel_analyst · 72,836
Telegram ranks this channel #25 of 84 here — alongside 83 others — read 20 August 2026
GitHub repos
@github_repos · 27,297
Telegram ranks this channel #26 of 76 here — alongside 75 others — read 11 September 2026
Machine Learning with Python
@CodeProgrammer · 68,301
Telegram ranks this channel #26 of 84 here — alongside 83 others — read 20 August 2026
ML - DS/DA/DE - AI [Jobs, InterviewPrep] 🇮🇳
@ml_ds_ai_jobs · 25,932
Telegram ranks this channel #28 of 81 here — alongside 80 others — read 13 September 2026
Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
@learndataanalysis · 53,262
Telegram ranks this channel #28 of 83 here — alongside 82 others — read 24 August 2026
AI & ML Papers
@PaperNexus · 33,858
Telegram ranks this channel #31 of 85 here — alongside 84 others — read 4 September 2026
AI Prompts | ChatGPT | Google Gemini | Claude
@aiindi · 58,966
Telegram ranks this channel #31 of 85 here — alongside 84 others — read 23 August 2026

This channel appears in 75 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 27 September 2026 — this entry's latest reading, not the date you are reading this.

“Data science/ML/AI” (@datascience_bds), 13,964 subscribers as measured 27 September 2026. Telegram Register, tgregister.com/channel/datascience_bds.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.