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CONormalization in SQL
#datascientiest #datascience #programming #coding #coder #programmer #normalization #codeverse #web #webdevelopment #softwaredevelopement
@codeverse007
#Normalization In Data Preprocessing
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GE🚀 Normalization — The Secret to Clean & Powerful Databases! 📊
Messy data ❌ Slow queries ❌ Duplicate records ❌
Normalization fixes it all 🔥
If you understand 1NF, 2NF, 3NF…
you’re already thinking like a real Data Analyst 💡
Master database design — not just SQL queries 💼
👉 Follow @GeekswithRaj for daily Data Analytics & SQL content
👉 Save this for interview prep 🚀
#SQL
#DataAnalytics
#Database
#LearnSQL
#GeeksWithRaj
@geekswithraj
51.7K
FU📍Bhide ka Normalization part -1
Bhide ne data ka achar bana diya sara data duplicate ho gaya🤯
Normalization
Denormalization
How it works
Data redundancy
#normalization #database #sql #mongodb #fullstackdeveloper #backend #denormalization #databaseengineer #datascience #computerscience #rdbms #devops #developer #devtech #dailyinsta #fullstackgada #tmkoc #jethalal #bhide
@fullstackgada
6.1K
DANormalization vs Standardization: When to use what 🎯
━━━━━━━━━━━━━━━━━━━━
NORMALIZATION (0-1 scaling):
Use when:
→ Neural networks (bounded inputs better)
→ NO outliers in data
→ Need specific bounded range
Example: Image pixels (0-255) → normalize to (0-1) for CNN
━━━━━━━━━━━━━━━━━━━━
STANDARDIZATION (mean=0, std=1):
Use when:
→ Linear models (LR, SVM, PCA)
→ Distance algorithms (KNN, K-means)
→ HAVE outliers (more robust)
→ Features roughly normally distributed
Example: Customer segmentation with age + income → standardize before K-means
━━━━━━━━━━━━━━━━━━━━
NEITHER needed:
→ Tree-based models (Random Forest, XGBoost)
━━━━━━━━━━━━━━━━━━━━
Senior DS decision process:
1. Check algorithm requirements
2. Look at feature distributions
3. Identify outliers
4. Then decide
This shows you THINK, not just memorize.
Drop your scenario: Normalization or Standardization? 👇
#machinelearning #datascienceinterview #featureengineering #interviewprep #datascience
@datascience.interview
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PRPreprocessing pipeline for llm
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday
@priyal.py
62.0K
FARELATIONAL DATABASE MANAGEMENT SYSTEMS (RDBMS)|UNIT-1 || SEMESTER-3 ||IMPORTANT ANSWERS EXPLANATION
@fab_ali_khan
175.4K
LE📊 Why is DBMS normalization so important?
It helps clean your data, remove duplicates, and prevent errors in your database! 🚫🧨
In this short, we break it down in a simple and beginner-friendly way 💡
Follow @learnzdevelopmenthub for more tech content in Tamil ❤️
#DBMS #Normalization #DatabaseDesign #LearnzConnect #SQL #learnzdevelopmenthub #tamiltech
@learnzconnect
606.9K
COSQL me Normalization kya hota hai?
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#codeimpact #coding #codingtutorials #programmingmemes #programmingtutorials #programminglife #normalization #sql #sqltraining #database #sqltutorial #mysql #mssql #db #programmingconcepts #programmingfun
@codeimpact.in
199.0K
VIData preparation is the most underestimated part of LLM training.
Pre-training, supervised fine-tuning, and RL-based alignment —each stage requires completely different data formats and preprocessing strategies.
If you get the data wrong, no model architecture can save you.
My colleagues and I at AWS wrote a detailed blog
• preprocessing techniques
• scalable data pipelines
• prompt–response formatting
• preference data for alignment
• synthetic data generation (with code examples)
Comment “LINK” and I’ll send it to you 📩
#LLM #machinelearning #generativeai #dataengineering #aws
@vinayak.tech.ai
76.0K
RA"Normalization is the process of organizing a relational database to minimize data redundancy and improve data integrity."
In a production environment, we typically aim for 3rd Normal Form (3NF):
1. 1NF (Atomicity): Each cell contains a single value (no lists).
2. 2NF (No Partial Dependency): All data must depend on the entire Primary Key.
3. 3NF (No Transitive Dependency): Non-key columns should not depend on other non-key columns. (Data depends on "The Key, the whole Key, and nothing but the Key").
In short: It’s about structuring data efficiently so that when a piece of information changes, we only have to update it in one place.
However, it is a trade-off. While normalization optimizes for writing data and saving space, highly normalized databases can be slower for reading because of complex joins. In read-heavy systems (like Data Warehouses), we sometimes intentionally 'denormalize' for performance.
Let's try to understand with a real life example.
### 📲 The Simple Normalization Fix: Address Redundancy
Here is how we use normalization to stop repeating addresses, using the contact list example:
---
#### 🗑️ The Problem: One Messy List (Unnormalized)
You have one giant table where Aunt Sue's address is written multiple times, wasting space.
| Table Name: **CONTACTS\_MESSY** |
| :--- |
| **ContactName** |
| **Phone** |
| **Street Address** |
| **City** |
| **Email** |
---
#### ✨ The Solution: Two Clean Lists (Normalized)
We break the table into two, ensuring each unique address is stored only once.
**1. The Core Contacts List**
This table tracks the person and uses a link (key) to find their address.
| Table Name: **CONTACTS** |
| :--- |
| 🔑 **ContactID (Primary Key)** |
| **ContactName** |
| **Phone** |
| **Email** |
| 🔗 **AddressID (Foreign Key)** |
**2. The Address Lookup List**
This table stores each unique address only once.
| Table Name: **ADDRESSES** |
| :--- |
| 🔑 **AddressID (Primary Key)** |
| **Street Address** |
| **City** |
---
When Aunt Sue moves, you only update the *Street Address* and *City* in *one row* of the *ADDRESSES* table. Every contact linked to that AddressID is instantly updated!
@random_geek.exe
13.2K
TRSave this reel for quick revision 🚀
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normalization in dbms
dbms normalization explained
dbms interview questions
placement
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@tripti.builds
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FIData replication in system design
@fit.engineer5
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