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Channel
Power BI & Tableau Resources
@PowerBI_analyst
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Polls · Citations · Telegram's recommendations · Cite this entry
55,789subscribers
-49 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
Register entry
| Telegram ID | -1001926764027 |
|---|---|
| Type | Channel |
| Username | @PowerBI_analyst |
| Description | 🆓 Resources to learn Power BI, Tableau & Data Visualisation Perfect channel to start learning everything about Data Analytics Admin: @coderfun |
| Created | Between 1 April 2023 and 31 October 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 25 October 2026 |
| Measurements held | 49 |
| Confirmed unchanged | 1 time, most recently 25 October 2026 |
| On Telegram | t.me/PowerBI_analyst |
Topic
Job listings — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 21 August 2026 and assigned it the closest of 31 fixed categories, at 70% 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 |
|---|---|---|
| 25 Oct 2026, 18:09 | 55,789 | -11 |
| 24 Oct 2026, 15:05 | 55,800 | -14 |
| 23 Oct 2026, 16:27 | 55,814 | -15 |
| 22 Oct 2026, 18:57 | 55,829 | -7 |
| 21 Oct 2026, 11:41 | 55,836 | -6 |
| 20 Oct 2026, 10:06 | 55,842 | -10 |
| 19 Oct 2026, 11:17 | 55,852 | -20 |
| 18 Oct 2026, 10:20 | 55,872 | +7 |
| 16 Oct 2026, 07:26 | 55,865 | -8 |
| 15 Oct 2026, 04:05 | 55,873 | +23 |
| 14 Oct 2026, 03:50 | 55,850 | -18 |
| 11 Oct 2026, 19:20 | 55,868 | -29 |
| 7 Oct 2026, 05:21 | 55,897 | +49 |
| 2 Oct 2026, 07:57 | 55,848 | +6 |
| 18 Sept 2026, 10:59 | 55,842 | +2 |
| 16 Sept 2026, 05:21 | 55,840 | -7 |
| 14 Sept 2026, 15:20 | 55,847 | -2 |
| 13 Sept 2026, 03:19 | 55,849 | -9 |
| 11 Sept 2026, 04:19 | 55,858 | -4 |
| 8 Sept 2026, 10:18 | 55,862 | first reading |
Engagement
168 posts held, back to 31 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 163 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.20%
- avg views ÷ 55,789 subscribers
- Avg views / post
- 1,230
- 42 posts measured
- Reaction rate
- 0.253%
- reactions ÷ views · ER floor
- Posts in window
- 42
- of 168 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 24 of 42 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 24 October 2026 |
|---|---|
| Posts held | 168 (31 July 2026 – 24 October 2026) |
| Views total | 51,609 |
| Reactions total | 79 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 25 Oct 2026, 21:59 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
- Photos
- 388
- Links
- 593
Lifetime counters from Telegram’s own channel header, read 25 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
331 reactions across 95 posts, in 4 distinct kinds. The most used accounts for 95.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 317 | 95.8% | |
| 🔥 | 7 | 2.11% | |
| 👍 | 6 | 1.81% | |
| 👏 | 1 | 0.302% |
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 99 of the 168 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 331 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 168 most recent posts we hold, published 31 July 2026 to 24 October 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
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❤3
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❤1
Mistake 4: Not testing the function Always test your function with a few inputs before applying it to hundreds of files. 🧪 Practical Exercise Create a simple function that calculates profit margin. Use: • Sales • Cost Formula: (Sales - Cost) / Sales For example: Sales = ₹10,000 Cost = ₹7,000 Profit Margin: (10000 - 7000) / 10000 = 0.30 = 30% Then test your function with different Sales and Cost values…
❤4
Now you can provide both the price and tax rate. For example: CalculatePrice(1000, 0.18) Result: 1180 And: CalculatePrice(1000, 0.05) Result: 1050 The function becomes more flexible. 6. Custom Functions with Tables Custom functions become especially powerful when working with tables. Imagine you receive these files: January.xlsx February.xlsx March.xlsx April.xlsx Each file contains raw sales data. …
❤2
📊 Power BI Learning Roadmap — Part 4 Topic 13: Custom M Functions After learning the basics of M language, the next important concept is Custom Functions. Custom functions allow you to write a transformation once and reuse it multiple times. This becomes very useful when you have many files or tables that need the same transformation logic. 1. What Is a Custom Function? A custom function is a reusable piece of …
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📊 Power BI Learning Roadmap — Part 4 Topic 12: Power Query M Language Until now, we have mostly used the Power Query interface to clean and transform data. But behind almost every transformation you perform in Power Query, there is code. That language is called M. Understanding M doesn't mean you need to become a programmer. You just need to understand how Power Query represents the transformations you perform. …
These functions become much easier to understand once you recognize the pattern. 7. The each Keyword You will see each frequently in Power Query. For example: each [Sales] > 5000 It means the expression should be evaluated for each row. Another example: each [Quantity] * [Price] This calculates the result for every row. If: Quantity = 10 Price = 500 then: 10 × 500 = 5000 8. Column References In M, a co…
❤4
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Showing the 12 most recent of 168 posts we hold for @PowerBI_analyst. 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 6 most recent of 20 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.
You have Quantity and Price columns and want to create a new Sales Amount column using Quantity × Price. Which Power Query feature is most appropriate?
- A) Group By14%
- B) Custom Column50%
- C) Conditional Column30%
- D) Remove Duplicates6%
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Which of the following is NOT typically an operation performed by Group By?
- A) Sum7%
- B) Average8%
- C) Count4%
- D) Create a line chart81%
Shares as published. No per-option vote count is published by Telegram, so none is shown.
What is the main purpose of Group By?
- A) Change data types2%
- B) Remove duplicate columns7%
- C) Summarize data based on one or more columns83%
- D) Create relationships8%
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Which language is used when creating a Custom Column in Power Query?
- A) DAX50%
- B) SQL18%
- C) M29%
- D) Python4%
Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is shown.
What is the primary purpose of a Conditional Column in Power Query?
- A) Create relationships between tables14%
- B) Create a new column based on conditions72%
- C) Create a DAX measure11%
- D) Create a visualization3%
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Before building visualizations, what is the recommended first step after importing data?
- A) Publish the report3%
- B) Create dashboards8%
- C) Clean and transform the data using Power Query88%
- D) Add slicers1%
Shares as published. 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 168 most recent posts we hold, published 31 July 2026 to 24 October 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.
Mentions
Named by 12 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.
@learndataanalysis · 53,33218 postsMachine Learning & Artificial Intelligence | Data Science Free Courses
@datasciencefree · 68,67013 postsGoogle Jobs - FAANG Companies • Facebook • Microsoft • Amazon • Netflix • Apple
@FAANGJob · 26,85510 postsCoding & AI Resources
@leadcoding · 35,30210 postsBest AI Tools | ChatGPT | Perplexity | Deepseek | Artificial Intelligence
@AI_Best_Tools · 59,6847 postsData Analytics & AI | SQL Interviews | Power BI Resources
@Data_Visual · 27,4687 postsFree Online Courses with Certificate | Udacity Free Courses | Eduonix | IP Cybersecurity | Coursera | Premium Certified Courses
@udacityfreecourse · 46,4866 postsFreelancing Tips: Earn Money Online
@freelancing_upwork · 22,3814 postsCoding Interview Resources
@crackingthecodinginterview · 52,0472 postsData Analytics
@sqlspecialist · 110,7882 postsAlgorithms and Data structure
@Algorithm_s1 · 241 postDevelopers India 🇮🇳
@developersindiamain · 2,5161 post
Names
Channels on the register whose handles appear in this channel's posts.
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.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
@sqlspecialist · 110,788#1
@excel_analyst · 73,323#2
@learndataanalysis · 53,332#3
@dataanalystinterview · 52,347#4
@dsabooks · 59,138#5
@machinelearning_deeplearning · 56,534#6
@pythonanalyst · 51,634#7
@sqlproject · 39,623#8
@DataPortfolio · 37,890#9
@DataScienceInterviews · 27,598#10
@powerbi_sql_analyst · 6,793#11
@MachineLearning9 · 41,879#12
@codingfreebooks · 32,555#13
@datascience_bds · 14,062#14
@Data_Visual · 27,468#15
@data_visualization_bds · 6,317#16
@DataAnalyticsX · 30,842#17
@leadcoding · 35,302#18
@thinkbroadly · 6,644#19
@analystcommunity · 4,583#20
@datamindscape · 5,224#21
@sqlanalyst · 76,548#22
@jobs_SQL · 91,282#23
@pythonspecialist · 56,482#24
@datasciencefree · 68,670#25
@datasciencefun · 77,754#26
@AI_Best_Tools · 59,684#27
@udemy_free_courses_with_certi · 70,484#28
@sql_engineer · 11,239#29
@pythondevelopersindia · 63,435#30
@Programming_experts · 67,945#31
@programming_guide · 55,824#32
@webdevcoursefree · 79,560#33
@Curiousprogrammer · 42,086#34
@learngpt · 38,830#35
@crackingthecodinginterview · 52,047#36
@datasets1 · 29,323#37
@LearnPython3 · 125,187#38
@Python53 · 51,259#39
@aiindi · 59,851#40
@InterviewBooks · 40,431#41
@aineeringdotcom · 16,081#42
@Learn_Startup · 33,128#43
@Java_Programming_Notes · 33,072#44
@PythonInterviews · 28,836#45
@aijobss · 14,030#46
@PaperNexus · 34,333#47
@sql_databases · 70,726#48
@pythonfreebootcamp · 40,444#49
@CodeProgrammer · 68,583#50
@AWS_GCP_Azure · 5,693#51
@DataAnalyticsJob · 12,924#52
@webdevelopmentbook · 32,500#53
@datasciencej · 8,284#54
@Jobs204 · 23,170#55
@pythonRe · 40,892#56
@AlwaysFreeUdemyCourses · 18,309#57
@webdev_trainings · 133,760#58
@Learn_Business_101 · 53,242#59
@datascience69 · 20,454#60
@CdiNotes · 19,308#61
@freelancing_upwork · 22,381#62
@getjobss · 113,242#63
@javascript_courses · 55,673#64
@stockmarketinginsights · 25,982#65
@trueminds · 27,330#66
@englishlearnerspro · 41,590#67
@Finance_Stock_Trading · 23,111#68
@generativeai_gpt · 31,031#69
@BestFinanceBooks · 25,110#70
@Bitcoin_Crypto_Web · 23,061#71
@internshiptojobs · 48,390#72
@OpenAI_Mastery · 50,188#73
@machine_learning_courses · 96,321#74
@JavaScript_Trainings · 96,345#75
@HealthFitnessGymTips · 19,819#76
@digitalmarketing_trick · 22,353#77
@ProgramsStore · 15,246#78
@udemyrip · 68,607#79
@DevopsDoc · 9,812#80
@the_programming_girls · 30,345#81
@deeplearning005 · 11,296#82
@FAANGJob · 26,855#83
@aipost · 675,880#84
Read from Telegram’s recommendation API, most recently 23 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
@excel_analyst · 73,323
Telegram ranks this channel #3 of 84 here — alongside 83 others — read 20 August 2026
@learndataanalysis · 53,332
Telegram ranks this channel #4 of 83 here — alongside 82 others — read 24 August 2026
@sqlspecialist · 110,788
Telegram ranks this channel #4 of 83 here — alongside 82 others — read 14 August 2026
@dataanalystinterview · 52,347
Telegram ranks this channel #5 of 85 here — alongside 84 others — read 24 August 2026
@dsabooks · 59,138
Telegram ranks this channel #6 of 89 here — alongside 88 others — read 22 August 2026
@Data_Visual · 27,468
Telegram ranks this channel #7 of 83 here — alongside 82 others — read 10 September 2026
@DataScienceInterviews · 27,598
Telegram ranks this channel #7 of 86 here — alongside 85 others — read 10 September 2026
@pythonanalyst · 51,634
Telegram ranks this channel #7 of 82 here — alongside 81 others — read 24 August 2026
@sqlproject · 39,623
Telegram ranks this channel #8 of 84 here — alongside 83 others — read 30 August 2026
@DataPortfolio · 37,890
Telegram ranks this channel #9 of 83 here — alongside 82 others — read 31 August 2026
@machinelearning_deeplearning · 56,534
Telegram ranks this channel #12 of 86 here — alongside 85 others — read 23 August 2026
@thedataschoool · 42,526
Telegram ranks this channel #15 of 85 here — alongside 84 others — read 29 August 2026
@codingfreebooks · 32,555
Telegram ranks this channel #18 of 79 here — alongside 78 others — read 5 September 2026
@shubham_wadekar_jobs · 24,505
Telegram ranks this channel #19 of 93 here — alongside 92 others — read 15 September 2026
@leadcoding · 35,302
Telegram ranks this channel #19 of 79 here — alongside 78 others — read 2 September 2026
@sqlanalyst · 76,548
Telegram ranks this channel #25 of 85 here — alongside 84 others — read 19 August 2026
@jobs_SQL · 91,282
Telegram ranks this channel #28 of 86 here — alongside 85 others — read 17 August 2026
@SkillsPW · 36,382
Telegram ranks this channel #30 of 54 here — alongside 53 others — read 1 September 2026
@MachineLearning9 · 41,879
Telegram ranks this channel #34 of 79 here — alongside 78 others — read 30 August 2026
@datalemur · 20,740
Telegram ranks this channel #36 of 72 here — alongside 71 others — read 26 September 2026
@sql_databases · 70,726
Telegram ranks this channel #36 of 73 here — alongside 72 others — read 20 August 2026
@DataAnalyticsX · 30,842
Telegram ranks this channel #40 of 85 here — alongside 84 others — read 7 September 2026
@Learn_Startup · 33,128
Telegram ranks this channel #44 of 81 here — alongside 80 others — read 4 September 2026
@datascience69 · 20,454
Telegram ranks this channel #45 of 82 here — alongside 81 others — read 27 September 2026
This channel appears in 92 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 25 October 2026 — this entry's latest reading, not the date you are reading this.
“Power BI & Tableau Resources” (@PowerBI_analyst), 55,789 subscribers as measured 25 October 2026. Telegram Register, tgregister.com/channel/PowerBI_analyst.
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.