Apr 11, 2023

Learning SQL: 3 Months Plan

We are adding some links here as its hard to mention on instagram. You can directly explore all mentioned resources here.

6️⃣ Super important 🚨 🚨 - Practice Prompt engineering on #generativeAI text to SQL models like these 👇🏻 - it will become essential in the future.

📌 Ask questions and validate queries with how you would have written them

📌 Write your query and then ask questions to see how correct they are. This is one good article to understand how to break down prompts. 🔗 https://lnkd.in/g7QFKsJe

‍7️⃣ Learn about popular databases and data structures.

‍#texttosql models to try out

- Seek AI - 🔗 https://www.seek.ai/

‍- Text2SQL.AI - 🔗 https://www.text2sql.ai/

‍- Super AI - 🔗 https://getsuper.ai/

‍- AI2sql.io - 🔗 https://www.ai2sql.io/

‍- AIHelperBot - 🔗 https://aihelperbot.com/

‍

Here's a 1-month beginner-level learning plan for SQL, with daily topics:

Week 1: SQL Basics

Day 1: Introduction to SQL, basic database concepts (tables, columns, rows, data types

Day 2: Creating tables in SQL

Day 3: Inserting data into tables

Day 4: Querying data with SELECT statements

Day 5: Filtering and sorting data with WHERE and ORDER BY

Day 6: Using logical operators AND, OR, NOT to refine queries

Day 7: Grouping and aggregating data with GROUP BY and aggregate functions

Week 2: Advanced SQL

Day 8: Joining tables with INNER JOIN and OUTER JOIN

Day 9: Using aliases to simplify queries

Day 10: Combining multiple queries with UNION

Day 11: Subqueries and nested queries

Day 12: Modifying data with UPDATE and DELETE statements

Day 13: Using transactions to ensure data integrity

Day 14: Working with views to simplify complex queries

Week 3: Database Design

Day 15: Introduction to database design and normalization

Day 16: First normal form (1NF)

Day 17: Second normal form (2NF)

Day 18: Third normal form (3NF)

Day 19: Denormalization and trade-offs

Day 20: Indexes and query optimization

Day 21: Using stored procedures and functions to simplify queries

Week 4: Practical Applications

Day 22: Introduction to data analysis with SQL

Day 23: Using SQL for business intelligence and reporting

Day 24: Working with time-series data

Day 25: Using SQL for machine learning and data science

Day 26: Integrating SQL with other programming languages

Day 27: Working with unstructured data

Day 28: Best practices for SQL development and deployment

You can adjust this plan based on your own learning pace and interests. It's recommended to practice writing SQL queries as much as possible, and to work on small projects or exercises to apply the concepts you've learned.

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Here's an advanced-level 2-month learning plan for SQL:

Month 2: Advanced SQL and Performance Tuning

Week 1:

Day 1: Review of SQL Basics

Day 2: Using CASE statements for conditional logic

Day 3: Advanced filtering with HAVING clause

Day 4: Analytical functions for data aggregation

Day 5: Using window functions to analyze data

Day 6: Introduction to query optimization

Day 7: Indexes and their role in query optimization

Week 2:

Day 8: Understanding execution plans

Day 9: Query tuning techniques

Day 10: Working with large datasets

Day 11: Optimizing joins for performance

Day 12: Using temporary tables for performance

Day 13: Partitioning tables for better performance

Day 14: Creating and optimizing indexes

Week 3:

Day 15: Introduction to Stored Procedures and Triggers

Day 16: Creating and executing stored procedures

Day 17: Triggers and their role in database operations

Day 18: Error handling and debugging in stored procedures

Day 19: Introduction to Database Administration

Day 20: Creating backups and restoring data

Day 21: Managing database security

Week 4:

Day 22: Advanced Topics in SQL

Day 23: Introduction to NoSQL databases

Day 24: Working with MongoDB

Day 25: Working with JSON data

Day 26: Introduction to Data Warehousing

Day 27: Creating and managing data warehouses

Day 28: Data Warehousing best practices

Month 3: Data Analytics with SQL

Week 1:

Day 1: Introduction to data analytics with SQL

Day 2: Overview of data analytics tools and technologies

Day 3: Data preparation and cleaning techniques

Day 4: Data exploration and visualization

Day 5: Data aggregation and summarization

Day 6: Time-series analysis and forecasting

Day 7: Advanced data visualization techniques

Week 2:

Day 8: Introduction to Machine Learning with SQL

Day 9: Overview of Machine Learning algorithms

Day 10: Preparing data for Machine Learning

Day 11: Building predictive models with SQL

Day 12: Evaluation and validation of Machine Learning models

Day 13: Deploying Machine Learning models with SQL

Day 14: Advanced topics in Machine Learning with SQL

Week 3:

Day 15: Introduction to Big Data with SQL

Day 16: Overview of Big Data technologies

Day 17: Working with Hadoop and Hive

Day 18: Working with Spark and SQL

Day 19: Introduction to Cloud Computing

Day 20: Deploying SQL databases in the Cloud

Day 21: Cloud-based data analytics with SQL

Week 4:

Day 22: Advanced Topics in Data Analytics with SQL

Day 23: Real-time data analytics with SQL

Day 24: Stream processing with SQL

Day 25: Introduction to Data Science with SQL

Day 26: Overview of Data Science tools and technologies

Day 27: Data preprocessing and cleaning with SQL

Day 28: Building and evaluating predictive models with SQL

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This plan can be adjusted based on your learning pace and interests. It is recommended to practice writing SQL queries and working on projects or exercises to apply the concepts learned. Additionally, it is advisable to keep up-to-date with the latest advancements and trends in the field to maintain a competitive edge.

Don't get overwhelmed looking at these, just focus on one thing each day and by end of 3 months you will be teaching others :)

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