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.
Here's an advanced-level 2-month learning plan for SQL:
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
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
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 :)
We at Alphaa AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists, who bring in data first approach to their work, core specialisation, and the organisation.With Saurabh Moody and Preksha Kaparwan you can start your journey as a citizen data scientist.
