SQL for Data Analysis (1324234242355).pptx

SQL for Data Analysis (1).pptx

1. SQL for Data Analysis

2. Introduction What is SQL? SQL (Structured Query Language) is used for querying, updating, and managing relational databases. Why Learn SQL for Data Analysis? Extract, filter, and summarize data using SQL commands like SELECT, WHERE, GROUP BY, ORDER BY. Clean and prepare data using functions like TRIM(), ISNULL(), and data type conversions. Course Goal: Build hands-on SQL skills using GitHub Codespaces in real-time environments.

3. Course Overview 01

4. Getting Started What You Should Know: Familiarity with data and basic computing terms No prior SQL knowledge is mandatory Setting Up Codespaces Steps to configure GitHub Codespaces with PostgreSQL or MySQL Loading practice datasets using .csv and .sql files Initializing a Database Creating tables using CREATE TABLE Populating data using INSERT INTO

5. SQL Introduction & Asking the Right Questions Topics Covered: SQL Syntax Refresher: SELECT, FROM, WHERE, LIMIT Relational Databases: Primary/Foreign Keys, Normalization SQL Structures: JOIN, UNION, CASE WHEN, nested queries Objective: Understand foundational SQL concepts and craft meaningful data questions

6. Using Data Types Topics Covered: Data Types: INT, VARCHAR, DATE, BOOLEAN, DECIMAL Handling Nulls: IS NULL, COALESCE(), IFNULL() Inaccurate Data Detection: BETWEEN, NOT IN, regex in LIKE Finding Duplicates: GROUP BY, HAVING COUNT(*) > 1 Objective: Clean and validate your dataset for reliable analysis

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8. Working with Dates Topics Covered: Date Functions: NOW(), GETDATE(), CURRENT_DATE, DATEPART(), DATEDIFF() Filtering by Dates: WHERE Date BETWEEN '2023-01-01' AND '2023-12-31' Formatting Dates: TO_CHAR(), STRFTIME() (PostgreSQL/SQLite) Objective: Leverage date-based queries to analyze trends and timelines

9. Easy SQL Functions Topics Covered: String Functions: LEFT(), RIGHT(), SUBSTRING(), TRIM(), CONCAT() Aggregate Functions: SUM(), AVG(), MIN(), MAX(), COUNT() Data Manipulation: UPDATE, DELETE, using CASE for conditional logic Objective: Apply built-in functions to transform and analyze data efficiently

10. Presenting Results with Visualization Topics Covered: Basic Data Visualization Concepts Exporting SQL Results for Visualization (CSV/Excel) Using SQL in Tools like Power BI: Connecting database, using queries in visuals Creating KPIs and Charts: Pie, Bar, Line using SQL summaries Objective: Communicate insights through visuals powered by SQL results

11. Conclusions In conclusion, effective SQL skills are paramount for data analysis professionals. This course arms participants with the necessary tools for data cleaning, date management, and visualization, ensuring they can translate complex data into actionable insights for informed decision-making.

12. Thank you! Do you have any questions? +00 000 000 000