📊 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…

Channel
Data Science
@datasciencemorons
On this record: Topic · Growth · Engagement · Posts · Telegram's recommendations · Cite this entry
5,311subscribers
-60 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 |
|---|---|
| Type | Channel |
| Username | @datasciencemorons |
| Created | Between 1 December 2018 and 30 November 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 21 August 2026 |
| Last confirmed live | 14 October 2026 |
| Measurements held | 12 |
| Confirmed unchanged | 1 time, most recently 14 October 2026 |
| On Telegram | t.me/datasciencemorons |
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 13 September 2026 and assigned it the closest of 31 fixed categories, at 99% 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
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Oct 2026, 18:07 | 5,311 | -6 |
| 11 Oct 2026, 16:00 | 5,317 | -17 |
| 5 Oct 2026, 07:16 | 5,334 | -14 |
| 27 Sept 2026, 08:16 | 5,348 | -9 |
| 13 Sept 2026, 16:59 | 5,357 | +1 |
| 9 Sept 2026, 13:18 | 5,356 | -3 |
| 4 Sept 2026, 06:40 | 5,359 | -4 |
| 31 Aug 2026, 15:54 | 5,363 | +1 |
| 28 Aug 2026, 13:04 | 5,362 | -4 |
| 25 Aug 2026, 04:56 | 5,366 | -4 |
| 22 Aug 2026, 12:37 | 5,370 | -1 |
| 21 Aug 2026, 07:30 | 5,371 | first reading |
Engagement
20 posts held, back to 25 March 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 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 19 August 2026. An engagement rate over an empty window would be a number about nothing.
Recent posts
📊 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…
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…
🚀 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…
🐍 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() •…
🚀 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…
🚀 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…
📊 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…
📊 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…
🚀 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…
📊 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…
🚀 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.
@dataflair · 18,774
Telegram ranks this channel #2 of 83 here — alongside 82 others — read 4 October 2026
@cloudymlofficial · 47,192
Telegram ranks this channel #30 of 88 here — alongside 87 others — read 26 August 2026
@Artificial_intelligence_in · 65,831
Telegram ranks this channel #44 of 90 here — alongside 89 others — read 21 August 2026
@thedataschoool · 42,372
Telegram ranks this channel #53 of 85 here — alongside 84 others — read 29 August 2026
@TechProgramMind_official · 40,094
Telegram ranks this channel #69 of 83 here — alongside 82 others — read 30 August 2026
This channel appears in 5 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 14 October 2026 — this entry's latest reading, not the date you are reading this.
“Data Science” (@datasciencemorons), 5,311 subscribers as measured 14 October 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.