SQL

Data Analytics

Define the abbreviation SQL in data analytics

Structured Query Language

Name the most common method of accessing & retrieving data in data analytics

SQL

What is SQL used for in data analytics?

SQL is used for managing and manipulating relational data in databases

Define the word ‘Query’ in SQL

A request for information from a database. Retrieving data from a table

Define the word ‘Output’ in SQL

the data returned from an SQL query

Define the word ‘Table’ in SQL 

this is what stores the data

Define the word ‘Database’ in SQL

a structured system that stores data in tables and lets you retrieve or manage that data using queries

Name the 4 different types of Databases

Object-Oriented

Hierarchy

Network

Relational

What’s the most popular /common database used in SQL 

Relational 

What are the 3 main ways SQL is used in data analytics?

ETL

Data Cleaning

EDA

Define the abbreviation ETL in SQL

Extract Transform Load

Explain the ETL process

Data is extracted from source systems and transformed and loaded into a database warehouse where it is then analysis.

Define the ‘ Staging Area’ in the ETL process

The staging area is where data engineer drops data to be analyzed 

Define ‘Database Warehouse’ in the ETL process

a central storage system where cleaned and transformed data is loaded and managed for analysis by data engineers

Define ‘Analytics’ in the ETL process

After data is cleaned and transformed, analysts explore and use it to find insights, answer questions, and support business decisions 

Define Data Cleaning

data cleaning identifies and fixes errors in the data, allowing for accurate and reliable data to be analyzed

Name the 8 steps in the Data Cleaning Cycle

1. Import Data

2. Merging Data Sets

3. Rebuilding Missing Data

4. Standardization

5. Normalization

6. De-Duplication

7. Verification & Enrichment

8. Exporting Data

Define the acronym EDA in SQL

Exploratory Data Analysis

Describe the EDA process

EDA is used by data analyst to investigate data for errors, summarize main characteristics of a data set, and discover trends and patterns.