Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Python for Data Analysts
Kanalga Telegram’da o‘tish
Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
Ko'proq ko'rsatish📈 Telegram kanali Python for Data Analysts analitikasi
Python for Data Analysts (@pythonanalyst) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 51 491 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 618-o'rinni va Hindiston mintaqasida 7 413-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 51 491 obunachiga ega bo‘ldi.
04 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 240 ga, so‘nggi 24 soatda esa 11 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 4.08% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 100 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 7 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent visualization, panda, analyst, sql, analytic kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Find top Python resources from global universities, cool projects, and learning materials for data analytics.
For promotions: @coderfun
Useful links: heylink.me/DataAnalytics”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 05 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
51 491
Obunachilar
+1124 soatlar
+467 kunlar
+24030 kunlar
Ma'lumot yuklanmoqda...
O'xshash kanallar
Taglar buluti
Kirish va chiqish esdaliklari
---
---
---
---
---
---
Obunachilarni jalb qilish
Iyun '26
Iyun '26
+58
0 kanalda
May '26
+373
4 kanalda
Get PRO
Aprel '26
+226
1 kanalda
Get PRO
Mart '26
+176
3 kanalda
Get PRO
Fevral '26
+507
7 kanalda
Get PRO
Yanvar '26
+679
1 kanalda
Get PRO
Dekabr '25
+606
1 kanalda
Get PRO
Noyabr '25
+659
6 kanalda
Get PRO
Oktabr '25
+766
0 kanalda
Get PRO
Sentabr '25
+880
12 kanalda
Get PRO
Avgust '25
+963
28 kanalda
Get PRO
Iyul '25
+691
28 kanalda
Get PRO
Iyun '25
+1 423
26 kanalda
Get PRO
May '25
+2 819
29 kanalda
Get PRO
Aprel '25
+3 765
24 kanalda
Get PRO
Mart '25
+689
32 kanalda
Get PRO
Fevral '25
+798
27 kanalda
Get PRO
Yanvar '25
+1 116
22 kanalda
Get PRO
Dekabr '24
+687
8 kanalda
Get PRO
Noyabr '24
+1 160
11 kanalda
Get PRO
Oktabr '24
+1 440
18 kanalda
Get PRO
Sentabr '24
+1 558
16 kanalda
Get PRO
Avgust '24
+2 026
13 kanalda
Get PRO
Iyul '24
+3 523
12 kanalda
Get PRO
Iyun '24
+2 963
11 kanalda
Get PRO
May '24
+3 323
10 kanalda
Get PRO
Aprel '24
+3 084
8 kanalda
Get PRO
Mart '24
+3 970
7 kanalda
Get PRO
Fevral '24
+3 196
1 kanalda
Get PRO
Yanvar '24
+3 989
4 kanalda
Get PRO
Dekabr '23
+2 324
6 kanalda
Get PRO
Noyabr '23
+3 962
4 kanalda
| Sana | Obunachilarni jalb qilish | Esdaliklar | Kanallar | |
| 05 Iyun | +20 | |||
| 04 Iyun | +11 | |||
| 03 Iyun | +9 | |||
| 02 Iyun | +11 | |||
| 01 Iyun | +7 |
Kanal postlari
🔥 Pandas Scenario-Based Interview Question 🐼
📊 Scenario:
You have an
orders dataset with:
order_id
customer_id
order_date
category
sales
🎯 Task:
Find the top-selling category for each month based on total sales.
✅ 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)
💡 Concepts Tested:
✔️ groupby()
✔️ Date handling
✔️ Aggregation
✔️ Ranking within groups
React ♥️ for more interview questions| 2 | Excel Basics for Data Analytics
Excel sits at the start of most analysis work.
What you use Excel for
• Cleaning raw data
• Exploring patterns
• Quick summaries for teams
Core concepts you must know
• Data setup
– Freeze header row. View → Freeze Top Row.
– Convert range to table. Ctrl + T.
– Use proper headers. No merged cells. One value per cell.
• Data cleaning
– Remove duplicates. Data → Remove Duplicates.
– Trim extra spaces. =TRIM(A2)
– Convert text to numbers. =VALUE(A2)
– Fix date format. Format Cells → Date.
– Handle blanks. Filter blanks, fill or delete.
– Find and replace. Ctrl + H.
• Essential formulas
– Math and counts
▪ SUM. =SUM(A2:A100)
▪ AVERAGE. =AVERAGE(A2:A100)
▪ MIN. =MIN(A2:A100)
▪ MAX. =MAX(A2:A100)
▪ COUNT. Counts numbers.
▪ COUNTA. Counts non blanks.
▪ COUNTBLANK. Counts blanks.
– Conditional formulas
▪ IF. =IF(A2>5000,"High","Low")
▪ IFS. Multiple conditions.
▪ AND. =AND(A2>5000,B2="West")
▪ OR. =OR(A2>5000,A2<1000)
– Lookup formulas
▪ XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
▪ VLOOKUP. Old but common.
▪ INDEX + MATCH. Powerful alternative.
– Text formulas
▪ LEFT. =LEFT(A2,4)
▪ RIGHT. =RIGHT(A2,2)
▪ MID. =MID(A2,2,3)
▪ LEN. =LEN(A2)
▪ CONCAT or TEXTJOIN.
▪ LOWER, UPPER, PROPER.
– Date formulas
▪ TODAY. Current date.
▪ NOW. Date and time.
▪ YEAR, MONTH, DAY.
▪ DATEDIF. Date difference.
▪ EOMONTH. Month end.
• Sorting and filtering
– Sort by multiple columns.
– Filter by value, color, condition.
– Top 10 filter for quick insights.
• Conditional formatting
– Highlight duplicates.
– Color scales for trends.
– Rules for thresholds. Example. Sales > 10000 in green.
• Pivot tables
– Insert → PivotTable.
– Rows. Category or Product.
– Values. Sum, Count, Average.
– Filters. Date, Region.
– Refresh after data update.
• Charts you must know
– Column. Comparison.
– Bar. Ranking.
– Line. Trends over time.
– Pie. Share or percentage.
– Combo. Actual vs target.
• Data validation
– Dropdown list. Data → Data Validation → List.
– Prevent wrong entries.
• Useful shortcuts
– Ctrl + Arrow. Jump data.
– Ctrl + Shift + Arrow. Select range.
– Ctrl + 1. Format cells.
– Ctrl + L. Apply filter.
– Alt + =. Auto sum.
– Ctrl + Z / Y. Undo redo.
• Common analyst mistakes to avoid
– Merged cells.
– Hard coded totals.
– Mixed data types in one column.
– No backup before cleaning.
• Daily practice task
– Download any sales CSV.
– Clean it.
– Build one pivot table.
– Create one chart.
Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354
Double Tap ♥️ For More | 2 490 |
| 3 | Read this once. There won't be a second message.
Brainlancer just launched today.
Investor-backed marketplace for ALL AI freelancers. Designers, builders, copywriters, marketers, video creators, automation experts, consultants.
If you build, design, write, or sell anything with AI, this is your moment.
How it works:
• Register free at brainlancer.com
• Stripe verification, 5 minutes, instant approval
• List up to 5 services from $49 to $4,999
• Add monthly subscriptions on top if you want
• We bring the clients. You keep 80%.
The deal:
No subscription.
No bidding.
No chasing.
We pay all marketing.
Real talk: no services live yet. We just launched. Whoever joins first gets seen first.
The first 100 Brainlancers are onboarding right now.
In 6 months others will have founding status, recurring income, featured services on the homepage.
You'll scroll past and remember this post.
Don't.
→ brainlancer.com | 2 837 |
| 4 | 🔥 Python Case Study-Based Interview Q&A (Top 5 🔥)
📊 Q1. Sales Drop Analysis
Scenario: Sales dropped last month. How will you analyze?
👉 Check monthly trends using groupby()
👉 Compare MoM performance
👉 Identify drop by region/product
👉 Drill down to root cause
📊 Q2. Customer Segmentation
Scenario: Segment customers based on purchase behaviour
👉 Group by customer ID
👉 Calculate total spend / frequency
👉 Create segments (High, Medium, Low)
👉 Useful for business decisions
📊 Q3. Data Cleaning Case
Scenario: Dataset has missing values, duplicates, inconsistent formats
👉 Handle missing → fillna()/dropna()
👉 Remove duplicates → drop_duplicates()
👉 Standardize formats (dates, text)
👉 Ensure clean dataset before analysis
📊 Q4. Top Performing Products
Scenario: Find best-selling products
👉 groupby(product) + sum(sales)
👉 Sort descending
👉 Use head() for top results
👉 Can also analyze category-wise
📊 Q5. Conversion Rate Analysis
Scenario: Calculate conversion rate from visits to purchases
👉 Conversion Rate = purchases / total visits
👉 Aggregate data properly
👉 Analyze by channel/source
👉 Helps optimize marketing
🔥 React with ♥️ for more case-study questions | 3 686 |
| 5 |  🔰 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:
👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX
👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)
👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions
👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS
Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences
👉🏻CTE
👉🏻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 | 🚀 Roadmap to Master Data Analytics in 50 Days! 📊📈
📅 Week 1–2: Foundations
🔹 Day 1–3: What is Data Analytics? Tools overview
🔹 Day 4–7: Excel/Google Sheets (formulas, pivot tables, charts)
🔹 Day 8–10: SQL basics (SELECT, WHERE, JOIN, GROUP BY)
📅 Week 3–4: Programming Data Handling
🔹 Day 11–15: Python for data (variables, loops, functions)
🔹 Day 16–20: Pandas, NumPy – data cleaning, filtering, aggregation
📅 Week 5–6: Visualization EDA
🔹 Day 21–25: Data visualization (Matplotlib, Seaborn)
🔹 Day 26–30: Exploratory Data Analysis – ask questions, find trends
📅 Week 7–8: BI Tools Advanced Skills
🔹 Day 31–35: Power BI / Tableau – dashboards, filters, DAX
🔹 Day 36–40: Real-world case studies – sales, HR, marketing data
🎯 Final Stretch: Projects Career Prep
🔹 Day 41–45: Capstone projects (end-to-end analysis + report)
🔹 Day 46–48: Resume, GitHub portfolio, LinkedIn optimization
🔹 Day 49–50: Mock interviews + SQL + Excel + scenario questions
💬 Tap ❤️ 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. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. 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 😄👍 | 3 329 |
