Data Analytics
Відкрити в Telegram
Perfect channel to learn Data Analytics Learn SQL, Python, Alteryx, Tableau, Power BI and many more For Promotions: @coderfun @love_data
Показати більше📈 Аналітичний огляд Telegram-каналу Data Analytics
Канал Data Analytics (@sqlspecialist) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 110 102 підписників, посідаючи 1 106 місце в категорії Технології та додатки та 2 308 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 110 102 підписників.
За останніми даними від 12 липня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 628, а за останні 24 години на -26, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.31%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.67% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 3 649 переглядів. Протягом першої доби публікація в середньому набирає 1 843 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 9.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як row, sql, analytic, analyst, visualization.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Perfect channel to learn Data Analytics
Learn SQL, Python, Alteryx, Tableau, Power BI and many more
For Promotions: @coderfun @love_data”
Завдяки високій частоті оновлень (останні дані отримано 13 липня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
110 102
Підписники
-2624 години
+867 днів
+62830 день
Архів дописів
110 115
📊 Excel Roadmap: From Basics to Advanced 🚀
🟢 Beginner Level
1. Excel Overview
- What is Excel?
- Workbook, Worksheet, Cells
- Navigating the interface
2. Basic Data Entry
- Entering numbers, text, dates
- Autofill and Flash Fill
- Formatting cells (font, color, alignment)
3. Basic Formulas
- SUM, AVERAGE, MIN, MAX
- Simple arithmetic (+, -, *, /)
- Cell references (relative, absolute)
4. Basic Charts
- Bar, Column, Pie charts
- Inserting and customizing charts
- Using Chart Tools
🟡 Intermediate Level
5. Data Management
- Sorting and filtering data
- Conditional formatting
- Data validation (dropdowns)
6. Intermediate Formulas
- IF, COUNTIF, SUMIF
- Text functions: CONCATENATE, LEFT, RIGHT, MID
- Date functions: TODAY, NOW, DATE
7. Tables & Named Ranges
- Creating and managing Tables
- Using Named Ranges for easier formulas
8. Pivot Tables
- Creating PivotTables
- Grouping and summarizing data
- Using slicers and filters
🔵 Advanced Level
9. Advanced Formulas
- VLOOKUP, HLOOKUP, INDEX & MATCH
- Array formulas
- Nested IFs and logical formulas
10. Advanced Charts & Dashboards
- Combo charts
- Sparklines
- Interactive dashboards with slicers
11. Macros & VBA Basics
- Recording macros
- Basic VBA editing
- Automating repetitive tasks
12. Data Analysis Tools
- What-If Analysis (Goal Seek, Data Tables)
- Solver Add-in
- Power Query for data transformation
13. Collaboration & Security
- Sharing & protecting workbooks
- Track changes & comments
- Version history
14. Power Pivot & DAX
- Importing large datasets
- Creating relationships
- Writing basic DAX formulas
🔥 Pro Tip: Practice by building monthly budgets, sales reports, and dashboards.
React ❤️ for detailed explanation!
110 115
𝟱 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗡𝗲𝗲𝗱𝗲𝗱!)😍
Ready to Upgrade Your Skills for a Data-Driven Career in 2025?📍
Whether you’re a student, a fresher, or someone switching to tech, these free beginner-friendly courses will help you get started in data analysis, machine learning, Python, and more👨💻🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4mwOACf
Best For: Beginners ready to dive into real machine learning✅️
110 115
10 Must-Have Habits for Data Analysts 📊🧠
1️⃣ Develop strong Excel & SQL skills
2️⃣ Master data cleaning — it’s 80% of the job
3️⃣ Always validate your data sources
4️⃣ Visualize data clearly (use Power BI/Tableau)
5️⃣ Ask the right business questions
6️⃣ Stay curious — dig deeper into patterns
7️⃣ Document your analysis & assumptions
8️⃣ Communicate insights, not just numbers
9️⃣ Learn basic Python or R for automation
🔟 Keep learning: analytics is always evolving
💬 Tap ❤️ for more!
110 115
You're STILL a data analyst even if...
- you only use Excel
- you forgot the SQL syntax
- you bombed the big interview
- you don't know how to program
- you did an analysis completely wrong
- you can't remember the right function name
- you have to Google how to do something easy you've done before
You're NOT a data analyst when...
- you give up
SO DON'T GIVE UP! KEEP GOING!
110 115
If I had to start learning data analyst all over again, I'd follow this:
1- Learn SQL:
---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)
2- Learn Excel:
---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers
3- Learn BI Tools:
---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation
4- Learn Python (Pandas) Optional:
---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)
Hope this helps you 😊
110 115
If you want to be a data analyst, you should work to become as good at SQL as possible. 📱
1. SELECT
What a surprise! I need to choose what data I want to return.
2. FROM
Again, no shock here. I gotta choose what table I am pulling my data from.
3. WHERE
This is also pretty basic, but I almost always filter the data to whatever range I need and filter the data to whatever condition I’m looking for.
4. JOIN
This may surprise you that the next one isn’t one of the other core SQL clauses, but at least for my work, I utilize some kind of join in almost every query I write.
5. Calculations
This isn’t necessarily a function of SQL, but I write a lot of calculations in my queries. Common examples include finding the time between two dates and multiplying and dividing values to get what I need.
Add operators and a couple data cleaning functions and that’s 80%+ of the SQL I write on the job.
React ♥️ for more
110 115
🎓 𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 – 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗧𝗶𝗺𝗲! 😍
Upskill in today’s most in-demand tech domains and boost your career 🚀
✅ FREE Courses Offered:
🧠 Modern AI
🔐 Cyber Security
🌐 Networking
📲 Internet of Things (IoT)
💫Perfect for students, freshers, and tech enthusiasts.
𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/45WnGy1
🎓 Get Certified by Cisco – 100% Free!
110 115
SQL Joins — A Practical Cheatsheet for Professionals
If you’re working with relational data — whether you’re a business analyst, backend dev, or aspiring data scientist — mastering SQL joins isn’t optional. It’s fundamental.
Here’s a concise guide to the most important join types, with real-world use cases:
INNER JOIN
Returns records with matching keys from both tables.
Use case: Show only customers who’ve placed at least one order.
LEFT JOIN (OUTER)
Returns all rows from the left table, and matched rows from the right.
Use case: List all customers, including those with zero orders.
RIGHT JOIN (OUTER)
Returns all rows from the right table. Rarely used, but powerful.
Use case: Show all orders, even if the customer was deleted.
FULL OUTER JOIN
Returns all records from both tables.
Use case: Capture everything — matched and unmatched.
CROSS JOIN
Returns the cartesian product.
Use case: Generate every possible product/supplier combo.
SELF JOIN
Joins a table to itself.
Use case: Show employees and their reporting managers.
Best Practices
Use aliases (A, B) for clean code
Prefer JOIN ON over WHERE for clarity
Always test joins with LIMIT to prevent overloads
110 115
📘 SQL Challenges for Data Analytics – With Explanation 🧠
(Beginner ➡️ Advanced)
1️⃣ Select Specific Columns
SELECT name, email FROM users;name and email columns from the users table.
✔️ Used when you don’t want all columns from a table.
2️⃣ Filter Records with WHERE
SELECT * FROM users WHERE age > 30;WHERE clause filters rows where age is greater than 30.
✔️ Used for applying conditions on data.
3️⃣ ORDER BY Clause
SELECT * FROM users ORDER BY registered_at DESC;registered_at in descending order.
✔️ Helpful to get latest data first.
4️⃣ Aggregate Functions (COUNT, AVG)
SELECT COUNT(*) AS total_users, AVG(age) AS avg_age FROM users;COUNT(*) counts total rows (users).
- AVG(age) calculates the average age.
✔️ Used for quick stats from tables.
5️⃣ GROUP BY Usage
SELECT city, COUNT(*) AS user_count FROM users GROUP BY city;city and counts users in each group.
✔️ Use when you want grouped summaries.
6️⃣ JOIN Tables
SELECT users.name, orders.amount
FROM users
JOIN orders ON users.id = orders.user_id;users and orders on matching IDs.
✔️ Essential when combining data from multiple tables.
7️⃣ Use of HAVING
SELECT city, COUNT(*) AS total
FROM users
GROUP BY city
HAVING COUNT(*) > 5;WHERE, but used with aggregates. This filters cities with more than 5 users.
✔️ **Use HAVING after GROUP BY.**
8️⃣ Subqueries
SELECT * FROM users
WHERE salary > (SELECT AVG(salary) FROM users);SELECT name,
CASE
WHEN age < 18 THEN 'Teen'
WHEN age <= 40 THEN 'Adult'
ELSE 'Senior'
END AS age_group
FROM users;SELECT name, city, score,
RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank
FROM users;110 115
Top Excel Formulas Every Data Analyst Should Know
SUM():
Purpose: Adds up a range of numbers.
Example: =SUM(A1:A10)
AVERAGE():
Purpose: Calculates the average of a range of numbers.
Example: =AVERAGE(B1:B10)
COUNT():
Purpose: Counts the number of cells containing numbers.
Example: =COUNT(C1:C10)
IF():
Purpose: Returns one value if a condition is true, and another if false.
Example: =IF(A1 > 10, "Yes", "No")
VLOOKUP():
Purpose: Searches for a value in the first column and returns a value in the same row from another column.
Example: =VLOOKUP(D1, A1:B10, 2, FALSE)
HLOOKUP():
Purpose: Searches for a value in the first row and returns a value in the same column from another row.
Example: =HLOOKUP("Sales", A1:F5, 3, FALSE)
INDEX():
Purpose: Returns the value of a cell based on row and column numbers.
Example: =INDEX(A1:C10, 2, 3)
MATCH():
Purpose: Searches for a value and returns its position in a range.
Example: =MATCH("Product B", A1:A10, 0)
CONCATENATE() or CONCAT():
Purpose: Joins multiple text strings into one.
Example: =CONCATENATE(A1, " ", B1)
TEXT():
Purpose: Formats numbers or dates as text.
Example: =TEXT(A1, "dd/mm/yyyy")
Excel Resources: t.me/excel_data
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
110 115
𝟯 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to master Python for Data Analytics without spending a single rupee?💰✨️
You don’t need expensive bootcamps or paid certifications to get started. Thanks to the open-source community, there are incredible free GitHub repositories that cover everything you need🧑💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/47hf59F
Don’t just study theory—start coding, analyzing, and building today. Your portfolio (and future self) will thank you✅️
