Available now! Telegram Research 2025 β the year's key insights 
Python for Data Analysts
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Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
Show moreπ Analytical overview of Telegram channel Python for Data Analysts
Channel Python for Data Analysts (@pythonanalyst) in the English language segment is an active participant. Currently, the community unites 51 491 subscribers, ranking 2 618 in the Technologies & Applications category and 7 413 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 51 491 subscribers.
According to the latest data from 04 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 240 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.08%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 100 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 7.
- Thematic interests: Content is focused on key topics such as visualization, panda, analyst, sql, analytic.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βFind top Python resources from global universities, cool projects, and learning materials for data analytics.
For promotions: @coderfun
Useful links: heylink.me/DataAnalyticsβ
Thanks to the high frequency of updates (latest data received on 05 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
51 491
Subscribers
+1124 hours
+467 days
+24030 days
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| Date | Subscriber Growth | Mentions | Channels | |
| 05 June | +20 | |||
| 04 June | +11 | |||
| 03 June | +9 | |||
| 02 June | +11 | |||
| 01 June | +7 |
Channel Posts
π₯ Pandas Scenario-Based Interview Question πΌ
π Scenario:
You have an
orders dataset with:
order_id
customer_id
order_date
category
sales
π― Task:
Find the top-selling category for each month based on total sales.
β
Pandas Solution:
import pandas as pd
# Convert to datetime
df['order_date'] = pd.to_datetime(df['order_date'])
# Extract month
df['month'] = df['order_date'].dt.strftime('%b-%Y')
# Total sales by month & category
sales_summary = (
df.groupby(['month', 'category'])['sales']
.sum()
.reset_index()
)
# Rank categories within each month
sales_summary['rank'] = (
sales_summary.groupby('month')['sales']
.rank(method='dense', ascending=False)
)
# Top category per month
result = sales_summary[sales_summary['rank'] == 1]
print(result)
π‘ Concepts Tested:
βοΈ groupby()
βοΈ Date handling
βοΈ Aggregation
βοΈ Ranking within groups
React β₯οΈ for more interview questions| 2 | Excel Basics for Data Analytics
Excel sits at the start of most analysis work.
What you use Excel for
β’ Cleaning raw data
β’ Exploring patterns
β’ Quick summaries for teams
Core concepts you must know
β’ Data setup
β Freeze header row. View β Freeze Top Row.
β Convert range to table. Ctrl + T.
β Use proper headers. No merged cells. One value per cell.
β’ Data cleaning
β Remove duplicates. Data β Remove Duplicates.
β Trim extra spaces. =TRIM(A2)
β Convert text to numbers. =VALUE(A2)
β Fix date format. Format Cells β Date.
β Handle blanks. Filter blanks, fill or delete.
β Find and replace. Ctrl + H.
β’ Essential formulas
β Math and counts
βͺ SUM. =SUM(A2:A100)
βͺ AVERAGE. =AVERAGE(A2:A100)
βͺ MIN. =MIN(A2:A100)
βͺ MAX. =MAX(A2:A100)
βͺ COUNT. Counts numbers.
βͺ COUNTA. Counts non blanks.
βͺ COUNTBLANK. Counts blanks.
β Conditional formulas
βͺ IF. =IF(A2>5000,"High","Low")
βͺ IFS. Multiple conditions.
βͺ AND. =AND(A2>5000,B2="West")
βͺ OR. =OR(A2>5000,A2<1000)
β Lookup formulas
βͺ XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
βͺ VLOOKUP. Old but common.
βͺ INDEX + MATCH. Powerful alternative.
β Text formulas
βͺ LEFT. =LEFT(A2,4)
βͺ RIGHT. =RIGHT(A2,2)
βͺ MID. =MID(A2,2,3)
βͺ LEN. =LEN(A2)
βͺ CONCAT or TEXTJOIN.
βͺ LOWER, UPPER, PROPER.
β Date formulas
βͺ TODAY. Current date.
βͺ NOW. Date and time.
βͺ YEAR, MONTH, DAY.
βͺ DATEDIF. Date difference.
βͺ EOMONTH. Month end.
β’ Sorting and filtering
β Sort by multiple columns.
β Filter by value, color, condition.
β Top 10 filter for quick insights.
β’ Conditional formatting
β Highlight duplicates.
β Color scales for trends.
β Rules for thresholds. Example. Sales > 10000 in green.
β’ Pivot tables
β Insert β PivotTable.
β Rows. Category or Product.
β Values. Sum, Count, Average.
β Filters. Date, Region.
β Refresh after data update.
β’ Charts you must know
β Column. Comparison.
β Bar. Ranking.
β Line. Trends over time.
β Pie. Share or percentage.
β Combo. Actual vs target.
β’ Data validation
β Dropdown list. Data β Data Validation β List.
β Prevent wrong entries.
β’ Useful shortcuts
β Ctrl + Arrow. Jump data.
β Ctrl + Shift + Arrow. Select range.
β Ctrl + 1. Format cells.
β Ctrl + L. Apply filter.
β Alt + =. Auto sum.
β Ctrl + Z / Y. Undo redo.
β’ Common analyst mistakes to avoid
β Merged cells.
β Hard coded totals.
β Mixed data types in one column.
β No backup before cleaning.
β’ Daily practice task
β Download any sales CSV.
β Clean it.
β Build one pivot table.
β Create one chart.
Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354
Double Tap β₯οΈ For More | 2 490 |
| 3 | Read this once. There won't be a second message.
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β’ Add monthly subscriptions on top if you want
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The deal:
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In 6 months others will have founding status, recurring income, featured services on the homepage.
You'll scroll past and remember this post.
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β brainlancer.com | 2 837 |
| 4 | π₯ Python Case Study-Based Interview Q&A (Top 5 π₯)
π Q1. Sales Drop Analysis
Scenario: Sales dropped last month. How will you analyze?
π Check monthly trends using groupby()
π Compare MoM performance
π Identify drop by region/product
π Drill down to root cause
π Q2. Customer Segmentation
Scenario: Segment customers based on purchase behaviour
π Group by customer ID
π Calculate total spend / frequency
π Create segments (High, Medium, Low)
π Useful for business decisions
π Q3. Data Cleaning Case
Scenario: Dataset has missing values, duplicates, inconsistent formats
π Handle missing β fillna()/dropna()
π Remove duplicates β drop_duplicates()
π Standardize formats (dates, text)
π Ensure clean dataset before analysis
π Q4. Top Performing Products
Scenario: Find best-selling products
π groupby(product) + sum(sales)
π Sort descending
π Use head() for top results
π Can also analyze category-wise
π Q5. Conversion Rate Analysis
Scenario: Calculate conversion rate from visits to purchases
π Conversion Rate = purchases / total visits
π Aggregate data properly
π Analyze by channel/source
π Helps optimize marketing
π₯ React with β₯οΈ for more case-study questions | 3 686 |
| 5 |  π° Local vs global variable in python | 3 901 |
| 6 | If you are trying to transition into the data analytics domain and getting started with SQL, focus on the most useful concept that will help you solve the majority of the problems, and then try to learn the rest of the topics:
ππ» Basic Aggregation function:
1οΈβ£ AVG
2οΈβ£ COUNT
3οΈβ£ SUM
4οΈβ£ MIN
5οΈβ£ MAX
ππ» JOINS
1οΈβ£ Left
2οΈβ£ Inner
3οΈβ£ Self (Important, Practice questions on self join)
ππ» Windows Function (Important)
1οΈβ£ Learn how partitioning works
2οΈβ£ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3οΈβ£ Use Cases of LEAD & LAG functions
4οΈβ£ Use cases of Aggregate window functions
ππ» GROUP BY
ππ» WHERE vs HAVING
ππ» CASE STATEMENT
ππ» UNION vs Union ALL
ππ» LOGICAL OPERATORS
Other Commonly used functions:
ππ» IFNULL
ππ» COALESCE
ππ» ROUND
ππ» Working with Date Functions
1οΈβ£ EXTRACTING YEAR/MONTH/WEEK/DAY
2οΈβ£ Calculating date differences
ππ»CTE
ππ»Views & Triggers (optional)
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://t.me/sqlspecialist
Hope it helps :) | 4 090 |
| 7 | π Roadmap to Master Data Analytics in 50 Days! ππ
π
Week 1β2: Foundations
πΉ Day 1β3: What is Data Analytics? Tools overview
πΉ Day 4β7: Excel/Google Sheets (formulas, pivot tables, charts)
πΉ Day 8β10: SQL basics (SELECT, WHERE, JOIN, GROUP BY)
π
Week 3β4: Programming Data Handling
πΉ Day 11β15: Python for data (variables, loops, functions)
πΉ Day 16β20: Pandas, NumPy β data cleaning, filtering, aggregation
π
Week 5β6: Visualization EDA
πΉ Day 21β25: Data visualization (Matplotlib, Seaborn)
πΉ Day 26β30: Exploratory Data Analysis β ask questions, find trends
π
Week 7β8: BI Tools Advanced Skills
πΉ Day 31β35: Power BI / Tableau β dashboards, filters, DAX
πΉ Day 36β40: Real-world case studies β sales, HR, marketing data
π― Final Stretch: Projects Career Prep
πΉ Day 41β45: Capstone projects (end-to-end analysis + report)
πΉ Day 46β48: Resume, GitHub portfolio, LinkedIn optimization
πΉ Day 49β50: Mock interviews + SQL + Excel + scenario questions
π¬ Tap β€οΈ for more! | 3 182 |
| 8 | 10 Steps to Landing a High Paying Job in Data Analytics
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. β Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. β Know basics of descriptive statistics
6. β With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. β Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. β WORK on atleast 2 end to end projects and create a portfolio of it
9. β Prepare an ATS friendly resume & start applying
10. β Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback ππ | 3 329 |
