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Channel

Data Science

@datasciencemorons

On this record: Growth · Engagement · Posts · Telegram's recommendations · Cite this entry

5,362subscribers

-9 since we began measuring on 21 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001406529465
TypeChannel
Username@datasciencemorons
CreatedBetween 1 March 2019 and 31 October 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded21 August 2026
Last confirmed live28 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 28 August 2026
On Telegramt.me/datasciencemorons

Growth

5,3625,3715,366.521 August 2026 — 5,371 subscribers22 August 2026 — 5,370 subscribers25 August 2026 — 5,366 subscribers28 August 2026 — 5,362 subscribers21 August 202628 August 2026
4 measurements spanning 7 days, net -9. 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 5,361–5,372 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
28 Aug 2026, 13:045,362-4
25 Aug 2026, 04:565,366-4
22 Aug 2026, 12:375,370-1
21 Aug 2026, 07:305,371first reading

Engagement

20 posts held, back to 25 March 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
4.44%
avg views ÷ 5,362 subscribers
Avg views / post
238
3 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
3
of 20 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 19 August 2026
Posts held20 (25 March 202619 August 2026)
Views total715
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken21 Aug 2026, 07:30 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.

Recent posts

19 Aug 2026, 13:54 UTC99 viewsread 21 August 2026
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📊 Statistics for Data Science: 10 Must-Know Concepts Statistics is the foundation of Data Science. It helps you understand data, identify patterns, measure uncertainty, and make better decisions. 🔟 Essential Concepts: 1️⃣ Mean — Average value 2️⃣ Median — Middle value; useful with outliers 3️⃣ Mode — Most frequent value 4️⃣ Variance — Measures data spread 5️⃣ Standard Deviation — Typical distance from the mean

12 Aug 2026, 14:41 UTC253 viewsread 21 August 2026
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📊 24 Mathematical Concepts Every Data Scientist Should Understand Data Science isn’t just Python and ML libraries. A strong math foundation helps you understand how models learn, optimize, and make predictions. 🔹 Statistics & Probability • Normal Distribution • Z-Score • Correlation • Entropy • Naive Bayes • MLE 🔹 ML Fundamentals • Gradient Descent • Regression • Sigmoid • ReLU • Softmax • SVM • F1 Score • MSE • L

5 Aug 2026, 13:53 UTC363 viewsread 21 August 2026
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Want to Build a Career in AI & Data Science? Don’t just watch random tutorials. Know what to learn, how to learn & how to become job-ready. 🔥 FREE AI & Data Science Career Masterclass 📅 9th August | 5:30 PM IST 🎯 Discover: ✅ Skills companies are hiring for ✅ AI & Data Science career opportunities ✅ Job-ready learning roadmap ✅ Salary & job-role insights ✅ LIVE guidance from an Industry Expert ⚡️ FREE Registratio

29 Jul 2026, 13:48 UTC420 viewsread 21 August 2026
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🚀 21 Essential Pandas Functions Every Data Analyst Should Know You don't need to memorize hundreds of functions—master these essentials to clean, analyze, and explore data efficiently. 🧹 Data Cleaning: head(), info(), describe(), dropna(), fillna(), rename() 🔍 Filtering: loc[], iloc[], query(), isin() 📊 Aggregation: groupby(), agg(), sum(), mean(), count() 🔗 Data Combining: merge(), concat(), join() 📈 Explorati

22 Jul 2026, 14:14 UTC435 viewsread 21 August 2026
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🐍 20 Essential Pandas Commands Every Data Analyst Should Know Data cleaning takes up a major part of every analytics project. Mastering a few core Pandas functions can save hours of work. Key functions: • duplicated() • drop_duplicates() • isna() • fillna() • replace() • describe() • groupby() • merge() • pivot_table() • crosstab() • query() • sort_values() • value_counts() • str.contains() • clip() •

15 Jul 2026, 13:33 UTC461 viewsread 21 August 2026
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🚀 Essential Pandas Methods Every Data Professional Should Know Pandas is the backbone of Data Science, Data Analytics, and Machine Learning. Master these core methods to work with data efficiently: 📥 Import Data: read_csv(), read_excel(), read_json(), read_sql() 🧹 Clean Data: fillna(), dropna(), sort_values(), groupby(), concat() 📊 Analyze Data: describe(), info(), mean(), median(), std() 🔄 Transform Data: pivot

9 Jul 2026, 14:20 UTC506 viewsread 21 August 2026
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🚀 10 AI Concepts Every Data Professional Should Know (2026) AI is now a must-have skill for Data Analysts, Data Scientists, Data Engineers, and BI professionals. Key Concepts: ✅ Machine Learning (ML) ✅ Models ✅ Generative AI (GenAI) ✅ Large Language Models (LLMs) ✅ Prompt Engineering ✅ AI Hallucinations ✅ Embeddings ✅ Fine-Tuning ✅ Retrieval-Augmented Generation (RAG) ✅ Vector Databases 💡 Understanding these conce

4 Jul 2026, 13:33 UTC540 viewsread 21 August 2026
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📊 10 Essential Statistics Concepts Every Data Professional Should Know Statistics is the foundation of Data Science, Machine Learning, and Analytics. Master these core concepts to analyze data, build better models, and make informed decisions. ✅ Mean ✅ Median ✅ Mode ✅ Variance ✅ Standard Deviation ✅ Probability ✅ Correlation (Correlation ≠ Causation) ✅ Hypothesis Testing ✅ Confidence Interval ✅ Regression 💡 Strong

25 Jun 2026, 13:27 UTC594 viewsread 21 August 2026
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📊 10 Essential Excel Data Cleaning Techniques Clean data = Better insights. Before dashboards, SQL queries, or ML models, make sure your data is accurate and reliable. ✅ Remove Duplicates ✅ TRIM Extra Spaces ✅ Standardize Text (UPPER/LOWER/PROPER) ✅ Find & Replace Errors ✅ Handle Missing Values ✅ Text to Columns ✅ Flash Fill ✅ Convert Text to Numbers ✅ Data Validation ✅ Remove Blank Rows 💡 Did you know? Data clea

10 Jun 2026, 14:38 UTC730 viewsread 21 August 2026
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🚀 Want to Become a Data Analyst? Stop chasing every new tool. Master the fundamentals. ✅ Excel – Data cleaning & analysis ✅ SQL – Querying and manipulating data ✅ Python – Automation & advanced analytics ✅ Power BI – Dashboards & storytelling ✅ Git/GitHub – Version control & portfolio ✅ Statistics – Data-driven decisions ✅ Communication – Turning insights into impact 📌 Beginner Roadmap: Excel → SQL → Power B

4 Jun 2026, 13:53 UTC668 viewsread 21 August 2026
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📊 Data Formats & Data Handling in AI AI is only as good as the data it learns from. 🔹 Types of Data ✅ Structured Data – SQL databases, spreadsheets ✅ Unstructured Data – Images, videos, audio, text ✅ Semi-Structured Data – JSON, XML, APIs, logs 🔹 Key Data Handling Steps 1️⃣ Data Collection 2️⃣ Data Cleaning 3️⃣ Data Preprocessing 4️⃣ Data Transformation 5️⃣ Data Storage 6️⃣ Data Analysis 7️⃣ Data Visuali

27 May 2026, 13:54 UTC618 viewsread 21 August 2026
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🚀 ML Life Cycle Cheat Sheet — From Data to Production Building ML models is only one part of the journey. Real-world AI success comes from mastering the complete ML lifecycle 👇 🔹 Define the business problem (SOW) 🔹 Collect reliable data 🔹 Perform EDA & uncover insights 🔹 Engineer meaningful features 🔹 Train & validate models 🔹 Fine-tune for better accuracy 🔹 Deploy to production 🔹 Monitor performance & retrain cont

Showing the 12 most recent of 20 posts we hold for @datasciencemorons. 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.

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.

CloudyML - Data Science & Analytics
@cloudymlofficial · 48,302
Telegram ranks this channel #30 of 88 here — alongside 87 others — read 26 August 2026
Artificial Intelligence
@Artificial_intelligence_in · 65,558
Telegram ranks this channel #44 of 90 here — alongside 89 others — read 21 August 2026
Thedataschoool
@thedataschoool · 42,681
Telegram ranks this channel #53 of 85 here — alongside 84 others — read 29 August 2026
Tech Program Mind
@TechProgramMind_official · 40,948
Telegram ranks this channel #69 of 83 here — alongside 82 others — read 30 August 2026

This channel appears in 4 seed channels' Telegram-generated recommendation lists in total. 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 28 August 2026 — this entry's latest reading, not the date you are reading this.

“Data Science” (@datasciencemorons), 5,362 subscribers as measured 28 August 2026. Telegram Register, tgregister.com/channel/datasciencemorons.

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.