In my opinion the best answer here is option 2

Clustering does not always improver performance especially when you are dealing with some small dataset ,at that point ,manual clustering does not make sense

Hey Guys
My apologies

I have been really busy these days,not getting enough time to focus on our community.

Stay tuned , I will be back with something interesting

Here is a job opportunity for Airtel payments bank for Gurgoan

Role-Data Analytics
Level-A.M./Manager

Location-Gurgaon
Department- Finance BPA

Job Description-
1- Extract, analyze, and validate data from AWS databases using efficient and accurate SOL queries
2- Write optimized queries to join, aggregate, and transform large datasets into meaningful business insights through Python or SQL Languages,
3- Leverage Power BI to visualize data and deliver intuitive dashboards and reports for business stakeholders
4 Validate analytical outputs against source tables to ensure data accuracy and reliability
5- Support the BPA team with actionable insights to drive reven ue and cost planning discussions 6- Apply analytical techniques to identify trends, variances, and drivers impacting business performance
7- Amazon Qyciksight and Python knowledge would be an added advantage.

Range 12 lpa (negotiatable)

Immediately joiner (max 1 month)

Let me know if anyone is looking for a job change
Can get you referred for this job opportunity

Ping me on linkedin

Hey guys
Reposting it

The minimum experience for this position is 1-1.5 years

This is a good opportunity offering good salary

Let me know if you are interested

Most people learn SQL like this:

SELECT → JOIN → GROUP BY

…but ignore one of the most useful concepts in reporting:

💡 PIVOT

Sooner or later, every Data Analyst / Data Engineer faces this requirement:

🧾 “Convert rows into columns.”

That’s exactly where PIVOT helps 🚀

It’s not just an interview topic —
it’s heavily used in dashboards, reporting, and business analytics.

💡 Real SQL growth starts when you think beyond syntax and focus on:
👉 “How will users consume this data?”

Have you used PIVOT in real projects yet? 👀

🚨 Upcoming SQL Interview Challenge 🚨

Most SQL developers know string functions…
But very few can solve THIS interview question correctly 👀

🎯 Problem:
Split a full customer name into:
✅ First Name
✅ Middle Name
✅ Last Name

Sounds easy? 😏

Now handle:
⚠️ NULL values
⚠️ Single names
⚠️ Multiple name combinations


New YouTube video dropping soon 🔥

Let’s see how many of you can solve it before the solution goes live 👇

🚨 New SQL Interview Video is LIVE 🚨

Can you split a full name into:
✅ First Name
✅ Middle Name
✅ Last Name

Sounds simple… until edge cases enter the game 😏

In this video, I solve a very interesting SQL interview question involving:
✔ String functions
✔ NULL handling
✔ Real-world logic

🎥 Watch now:
👉 Tricky SQL Interview Question in Hindi | Split Full Name into First, Middle & Last Name in SQL
https://youtu.be/_kcVqMJU72A

Let me know honestly…
Could YOU solve it before watching? 👀

🚨 Quick SQL Reality Check

Most developers think:

COUNT(*) = COUNT(Column)

But that’s not always true 👀

A single NULL value can completely change the result.

I recorded a quick 30-second Short explaining the difference with a simple example.

🎥 Watch here:
👉 [Paste YouTube Shorts Link]

Trust me, this is one concept worth remembering for SQL interviews and real-world projects.

Or, if you want something more aggressive:

⚠️ SQL Interview Trap

What will these return?

COUNT(*)

COUNT(Salary)

Same answer? 🤔

Most people get this wrong because of NULL values.

I explained it in a quick YouTube Short👇

🎥 Watch Now:
👉
https://youtube.com/shorts/1rHHZmMHx6E?feature=share

Did you know the answer before watching? 😏

Python File Handling

File handling is one of the most important skills in Python, especially for Data Engineering and Automation.

📂 Reading Files – Load data from text files, logs, and reports.

✍️ Writing Files – Create, update, and save data to files.

📊 CSV Processing – Read, transform, and analyze structured data stored in CSV files.

🔄 JSON Handling – Work with API responses and exchange data between applications.

💡 Most real-world applications and data pipelines rely heavily on CSV and JSON files. Learning file handling helps you automate tasks and work with data efficiently.

Expand your job search to increase your chances of becoming a data analyst.

Some alternative roles to explore:

1. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Focuses on using data to improve business processes and decision-making.

2. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Specializes in analyzing operational data to optimize efficiency and performance.

3. 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Uses data to drive marketing strategies and measure campaign effectiveness.

4. 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Analyzes financial data to support investment decisions and financial planning.

5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Evaluates product performance and user data to help product development.

6. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Conducts data-driven research to support strategic decisions and policy development.

7. 𝗕𝗜 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Transforms data into actionable business insights through reporting and visualization.

8. 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Utilizes statistical and mathematical models to analyze large datasets, often in finance.

9. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Analyzes customer data to improve customer experience and drive retention.

10. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁: Provides expert advice on data strategies, data management, and analytics to organizations.

11. 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Analyzes supply chain data to optimize logistics, reduce costs, and improve efficiency.

12. 𝗛𝗥 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Uses data to improve human resources processes, from recruitment to employee retention and performance management.

🚨 New SQL Shorts Alert! 🚨

Can you solve this Deloitte SQL Interview Question? 👀

🎯 Find all employees who earn more than their manager.

Sounds simple… until you actually try writing the query 😏

I’ve explained the complete logic in a quick 30-second YouTube Short.

🎥 Watch now:
👉 Deloitte SQL Interview Question 🤯 | Employees Earning More Than Their Manager#sqlinterview#deloitte
https://youtube.com/shorts/D98qCDNnSdw?feature=share

Drop a 🔥 if you got the answer right before watching!

A–Z of Data Analyst 📊💼

A – Analytics
The process of analyzing data to discover insights and support decision-making.

B – Business Intelligence (BI)
Technologies and tools used to analyze business data (Power BI, Tableau).

C – Cleaning (Data Cleaning)
Removing errors, duplicates, and inconsistencies from data.

D – Dashboard
A visual display of key metrics and insights.

E – ETL (Extract, Transform, Load)
Process of collecting, cleaning, and storing data for analysis.

F – Forecasting
Predicting future trends using historical data.

G – Group By
A method to organize data into categories for analysis.

H – Hypothesis Testing
Testing assumptions using statistical methods.

I – Insight
Meaningful information derived from data analysis.

J – Join
Combining data from multiple tables (SQL concept).

K – KPI (Key Performance Indicator)
A measurable value showing business performance.

L – Linear Regression
A statistical method used to predict relationships between variables.

M – Metrics
Quantifiable measures used to track performance.

N – Normalization
Organizing data to reduce redundancy and improve efficiency.

O – Outlier
A data point significantly different from others.

P – Pivot Table
A tool used to summarize and analyze data quickly.

Q – Query
A request to retrieve data from a database.

R – Reporting
Presenting data insights through charts and summaries.

S – SQL
Language used to manage and analyze structured data.

T – Trend Analysis
Identifying patterns or changes over time.

U – Unstructured Data
Data without predefined format (text, images).

V – Visualization
Representing data using charts or graphs.

W – Warehousing (Data Warehouse)
Central storage of large structured datasets.

X – X-axis
Horizontal axis in charts representing variables.

Y – YoY (Year-over-Year)
Comparing data from one year to another.

Z – Z-Score
Statistical measure showing how far a value is from the mean

Like for more ❤️👍

Complete Python topics required for the Data Engineer role:

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:

- Python Syntax
- Data Types
- Lists
- Tuples
- Dictionaries
- Sets
- Variables
- Operators
- Control Structures:
- if-elif-else
- Loops
- Break & Continue try-except block
- Functions
- Modules & Packages

➤ 𝗣𝗮𝗻𝗱𝗮𝘀:

- What is Pandas & imports?
- Pandas Data Structures (Series, DataFrame, Index)
- Working with DataFrames:
-> Creating DFs
-> Accessing Data in DFs Filtering & Selecting Data
-> Adding & Removing Columns
-> Merging & Joining in DFs
-> Grouping and Aggregating Data
-> Pivot Tables

- Input/Output Operations with Pandas:
-> Reading & Writing CSV Files
-> Reading & Writing Excel Files
-> Reading & Writing SQL Databases
-> Reading & Writing JSON Files
-> Reading & Writing - Text & Binary Files

➤ 𝗡𝘂𝗺𝗽𝘆:

- What is NumPy & imports?
- NumPy Arrays
- NumPy Array Operations:
- Creating Arrays
- Accessing Array Elements
- Slicing & Indexing
- Reshaping, Combining & Arrays
- Arithmetic Operations
- Broadcasting
- Mathematical Functions
- Statistical Functions

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗡𝘂𝗺𝗽𝘆 are more than enough for Data Engineer role.

Like for more 👍🏻❤️