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Normalization in SQL is a fundamental concept of database design. In this video, you will learn the basics of normalization and understand the different types of keys in a database. You will look the various types of normal formals and address issues such as partial dependency and transitive dependency. You will understand how 1NF, 2NF, 3NF and BCNF can solve these issues, reduce redundancy and help achieve data integrity.

The below topics are covered in this video:
00:00 What is Normalization?
02:04 What is a Key?
03:40 Types of Keys
07:25 Types of Normal Forms
07:42 1NF
08:58 2NF
12:53 3NF
14:50 BCNF

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What is Normalization in DBMS?
The Normalization theory was first explained by IBM researcher E.F. Codd in the 1970s. In a database, a large amount of data is stored in different tables. There can be the possibility of redundancy being present in the data. So normalization aims to eliminate this redundancy from the data and ensures data integrity. Using normalization techniques, you can split a large table into smaller tables and define relationships between them to enhance the clarity in organizing data. It also removes anomalies like Insert, Update and Delete Anomalies.

What is SQL?
SQL stands from Structured Query Language. It is used to communicate with several relational databases such as MySQL, Oracle and MS SQL Server. Using SQL queries, you can fetch, update, delete and manipulate data that is stored in the form of rows and columns. SQL became the de facto standard programming language for relational databases after they emerged in the late 1970s and early 1980s. You can also permit users to perform specific tasks on tables, procedures, and views. SQL commands are divided into different types, such as data manipulation language (DML), data definition language (DDL), transaction control language (TCL), and data query language (DQL).

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➡ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning indepth via our applied learning model with live sessions by leading practitioners and industry projects.

✅ Key Features
Post Graduate Program certificate and Alumni Association membership
Exclusive hackathons and Ask me Anything sessions by IBM
8X higher live interaction in live online classes by industry experts
Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
Master Classes delivered by Purdue faculty and IBM experts
Simplilearn's JobAssist helps you get noticed by top hiring companies
Resume preparation and LinkedIn profile building
1:1 mock interview
Career accelerator webinars

✅ Skills Covered
Data Analytics
Statistical Analysis using Excel
Data Analysis Python and R
Data Visualization Tableau and Power BI
Linear and logistic regression modules
Clustering using kmeans
Supervised Learning

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