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@Tomilaay

#CRYPTO TRADER#Food microbiologist,Online entrepreneur, Bitcoin enthusiast,Volatility trader.
Joined April 2017
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Job Corner Verified account @JOBCORNER247
Free Computer Science Certifications to try in 2026: ๐Ÿ”ธGIT https://t.co/H5chWHunbB ๐Ÿ”ธPython https://t.co/RY3zrvA81O ๐Ÿ”ธSQL https://t.co/zoVT1cqo7R ๐Ÿ”ธDSA https://t.co/F87Cc4IyBe ๐Ÿ”ธJava https://t.co/en2OUCwDSi ๐Ÿ”ธJavaScript https://t.co/S5k0INPFdn ๐Ÿ”ธC https://t.co/LFqyNLpGZc ๐Ÿ”ธC++ https://t.co/AycExxxzks ๐Ÿ”ธData Science https://t.co/6BshafwIGg ๐Ÿ”ธMachine Learning https://t.co/Nrz1boiOri ๐Ÿ”ธGoogle Data Analytics Certificate https://t.co/ygnQezioJr ๐Ÿ”ธDeep Learning https://t.co/9Fzh5TRX95 ๐Ÿ”ธLinux https://t.co/uzEwESHs6S ๐Ÿ”ธDevOps https://t.co/MF4EMhB1Sy ๐Ÿ”ธSQL https://t.co/l7sz1rgfoU ๐Ÿ”ธSQL for Data Science https://t.co/BxGEiQMd1X ๐Ÿ”ธPostgreSQL https://t.co/VFEilyX1kJ ๐Ÿ”ธMySQL https://t.co/RhFXpgPSFX ๐Ÿ”ธSQL Server https://t.co/koHwMfW57W ๐Ÿ”ธOracle https://t.co/sYvJSVqNcK ๐Ÿ”ธFull Stack Web https://t.co/mRFjzmQtYQ ๐Ÿ”ธMeta Back-End Developer Professional Certificate https://t.co/r2oj5aYplH ๐Ÿ”ธMeta Front-End Developer Professional Certificate https://t.co/MXMJkfqGgD ๐Ÿ”ธProgramming with JavaScript https://t.co/TqDgAA6jhV ๐Ÿ”ธLinux https://t.co/Y5rvEDj29v ๐Ÿ”ธDevOps https://t.co/iZ0QsTuMQn ๐Ÿ”ธCI/CD https://t.co/yiEo81TLzO ๐Ÿ”ธDocker https://t.co/r4WRQyz8kL ๐Ÿ”ธWeb Applications for Everybody Specialization https://t.co/mZ6auYtLRx ๐Ÿ”ธKubernetes https://t.co/HyIrVlqvmB ๐Ÿ”ธHTML, CSS, and Javascript for Web Developers https://t.co/8x3fZTt4CY
JOBCORNER247's tweet photo. Free Computer Science Certifications to try in 2026: ๐Ÿ”ธGIT https://t.co/H5chWHunbB ๐Ÿ”ธPython https://t.co/RY3zrvA81O ๐Ÿ”ธSQL https://t.co/zoVT1cqo7R ๐Ÿ”ธDSA https://t.co/F87Cc4IyBe ๐Ÿ”ธJava https://t.co/en2OUCwDSi ๐Ÿ”ธJavaScript https://t.co/S5k0INPFdn ๐Ÿ”ธC https://t.co/LFqyNLpGZc ๐Ÿ”ธC++ https://t.co/AycExxxzks ๐Ÿ”ธData Science https://t.co/6BshafwIGg ๐Ÿ”ธMachine Learning https://t.co/Nrz1boiOri ๐Ÿ”ธGoogle Data Analytics Certificate https://t.co/ygnQezioJr ๐Ÿ”ธDeep Learning https://t.co/9Fzh5TRX95 ๐Ÿ”ธLinux https://t.co/uzEwESHs6S ๐Ÿ”ธDevOps https://t.co/MF4EMhB1Sy ๐Ÿ”ธSQL https://t.co/l7sz1rgfoU ๐Ÿ”ธSQL for Data Science https://t.co/BxGEiQMd1X ๐Ÿ”ธPostgreSQL https://t.co/VFEilyX1kJ ๐Ÿ”ธMySQL https://t.co/RhFXpgPSFX ๐Ÿ”ธSQL Server https://t.co/koHwMfW57W ๐Ÿ”ธOracle https://t.co/sYvJSVqNcK ๐Ÿ”ธFull Stack Web https://t.co/mRFjzmQtYQ ๐Ÿ”ธMeta Back-End Developer Professional Certificate https://t.co/r2oj5aYplH ๐Ÿ”ธMeta Front-End Developer Professional Certificate https://t.co/MXMJkfqGgD ๐Ÿ”ธProgramming with JavaScript https://t.co/TqDgAA6jhV ๐Ÿ”ธLinux https://t.co/Y5rvEDj29v ๐Ÿ”ธDevOps https://t.co/iZ0QsTuMQn ๐Ÿ”ธCI/CD https://t.co/yiEo81TLzO ๐Ÿ”ธDocker https://t.co/r4WRQyz8kL ๐Ÿ”ธWeb Applications for Everybody Specialization https://t.co/mZ6auYtLRx ๐Ÿ”ธKubernetes https://t.co/HyIrVlqvmB ๐Ÿ”ธHTML, CSS, and Javascript for Web Developers https://t.co/8x3fZTt4CY
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The Psychology Of Achievement: 9 Essential Mindsets That Define World-Class Success Please follow me @SiaYiing1997
SiaYiing1997's tweet photo. The Psychology Of Achievement: 9 Essential Mindsets That Define World-Class Success Please follow me @SiaYiing1997 https://t.co/kuvWhxNpBc
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There is no God but one .... and we will fight.... Imran Khanโค๏ธ
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100 SQL Concepts for Mastering SQL โ†’ Understanding Relational Databases and Tables in SQL โ†’ SQL vs. NoSQL (When to Use Each) in SQL โ†’ Data Definition Language (DDL): CREATE, ALTER, DROP in SQL โ†’ Data Manipulation Language (DML): INSERT, UPDATE, DELETE in SQL โ†’ Data Query Language (DQL): SELECT Statements in SQL โ†’ Data Control Language (DCL): GRANT and REVOKE in SQL โ†’ Transaction Control Language (TCL): COMMIT, ROLLBACK, SAVEPOINT in SQL โ†’ Creating Databases and Schemas in SQL โ†’ Creating Tables with Data Types (INT, VARCHAR, DATE, DECIMAL, BOOLEAN) in SQL โ†’ Using PRIMARY KEY Constraints for Row Uniqueness in SQL โ†’ Using FOREIGN KEY Constraints for Referential Integrity in SQL โ†’ Using UNIQUE Constraints to Prevent Duplicate Values in SQL โ†’ Using NOT NULL Constraints for Required Fields in SQL โ†’ Using CHECK Constraints for Value Validation in SQL โ†’ Using DEFAULT Constraints for Default Column Values in SQL โ†’ Altering Table Structures (ADD, MODIFY, DROP COLUMN) in SQL โ†’ Dropping Tables vs. Truncating Tables (DROP vs. TRUNCATE vs. DELETE) in SQL โ†’ Basic SELECT with FROM and WHERE Clauses in SQL โ†’ Using Aliases (AS) for Columns and Tables in SQL โ†’ Filtering with Comparison Operators (=, <>, >, <, >=, <=) in SQL โ†’ Filtering with Logical Operators (AND, OR, NOT) in SQL โ†’ Filtering with IN and NOT IN Operators in SQL โ†’ Filtering with BETWEEN for Range Conditions in SQL โ†’ Filtering with LIKE and Wildcards (%, _) for Pattern Matching in SQL โ†’ Handling NULL Values with IS NULL and IS NOT NULL in SQL โ†’ Sorting Results with ORDER BY (ASC, DESC) in SQL โ†’ Limiting Rows with LIMIT, TOP, or FETCH FIRST in SQL โ†’ Using DISTINCT to Remove Duplicate Rows in SQL โ†’ Aggregate Functions (COUNT, SUM, AVG, MIN, MAX) in SQL โ†’ Grouping Data with GROUP BY Clause in SQL โ†’ Filtering Groups with HAVING Clause in SQL โ†’ WHERE vs. HAVING (Pre-aggregation vs. Post-aggregation) in SQL โ†’ INNER JOIN for Matching Rows Across Tables in SQL โ†’ LEFT JOIN (LEFT OUTER JOIN) for All Left Table Rows in SQL โ†’ RIGHT JOIN (RIGHT OUTER JOIN) for All Right Table Rows in SQL โ†’ FULL OUTER JOIN for All Rows From Both Tables in SQL โ†’ CROSS JOIN for Cartesian Products in SQL โ†’ SELF JOIN for Joining a Table to Itself in SQL โ†’ NATURAL JOIN (Automatic Join on Common Columns) in SQL โ†’ UNION for Combining Results (With Duplicate Removal) in SQL โ†’ UNION ALL for Combining Results (With Duplicates Preserved) in SQL โ†’ INTERSECT for Common Rows Between Queries in SQL โ†’ EXCEPT (or MINUS) for Rows in First Query but Not Second in SQL โ†’ Subqueries in WHERE Clause (Scalar, Row, Table Subqueries) in SQL โ†’ Correlated Subqueries (Referencing Outer Query) in SQL โ†’ Subqueries in SELECT Clause (Scalar Subqueries) in SQL โ†’ Subqueries in FROM Clause (Derived Tables) in SQL โ†’ EXISTS and NOT EXISTS Operators in SQL โ†’ ANY, SOME, and ALL Operators with Subqueries in SQL โ†’ Common Table Expressions (CTE) with WITH Clause in SQL โ†’ Recursive CTEs for Hierarchical Data (Tree Traversal) in SQL โ†’ Window Functions (OVER Clause) for Row-Level Calculations in SQL โ†’ ROW_NUMBER() for Sequential Row Numbering in SQL โ†’ RANK() and DENSE_RANK() for Ranking with Tie Handling in SQL โ†’ NTILE(n) for Dividing Rows into Buckets in SQL โ†’ LEAD() and LAG() for Accessing Adjacent Row Values in SQL โ†’ FIRST_VALUE() and LAST_VALUE() for Boundary Values in SQL โ†’ SUM() OVER() for Running Totals in SQL โ†’ AVG() OVER() for Moving Averages in SQL โ†’ PARTITION BY for Window Function Groups in SQL โ†’ ORDER BY Within OVER Clause for Window Ordering in SQL โ†’ ROWS vs. RANGE for Window Frame Boundaries in SQL โ†’ CASE Expressions for Conditional Logic in SQL โ†’ COALESCE() for Returning First Non-NULL Value in SQL โ†’ NULLIF() for Conditional NULL Return in SQL โ†’ CAST() and CONVERT() for Data Type Conversion in SQL โ†’ String Functions (CONCAT, SUBSTRING, LENGTH, TRIM, UPPER, LOWER, REPLACE) in SQL โ†’ Date/Time Functions (EXTRACT, DATEADD, DATEDIFF, CURRENT_DATE, NOW) in SQL โ†’ Mathematical Functions (ROUND, CEIL, FLOOR, ABS, MOD, POWER) in SQL โ†’ INSERT INTO SELECT for Copying Data Between Tables in SQL โ†’ UPDATE with JOIN for Cross-Table Updates in SQL โ†’ DELETE with JOIN for Cross-Table Deletions in SQL โ†’ MERGE (UPSERT) for Insert or Update Logic in SQL โ†’ Transactions (BEGIN, COMMIT, ROLLBACK, SAVEPOINT) in SQL โ†’ ACID Properties (Atomicity, Consistency, Isolation, Durability) in SQL โ†’ Isolation Levels (Read Uncommitted, Read Committed, Repeatable Read, Serializable) in SQL โ†’ Dirty Reads, Non-Repeatable Reads, and Phantom Reads in SQL โ†’ Indexing Fundamentals (Clustered vs. Non-Clustered Indexes) in SQL โ†’ Creating and Dropping Indexes in SQL โ†’ Composite Indexes (Multiple Columns) in SQL โ†’ Unique Indexes for Uniqueness Enforcement in SQL โ†’ Full-Text Indexes for Text Search in SQL โ†’ Index Scan vs. Index Seek vs. Table Scan in SQL โ†’ Views (Virtual Tables) for Simplifying Queries in SQL โ†’ Materialized Views for Stored Query Results in SQL โ†’ Updatable Views (With Restrictions) in SQL โ†’ Stored Procedures for Reusable Code Blocks in SQL โ†’ Stored Functions (Scalar, Table-Valued) in SQL โ†’ Triggers (Automatic Execution on INSERT, UPDATE, DELETE) in SQL โ†’ Trigger Timing (BEFORE, AFTER, INSTEAD OF) in SQL โ†’ Cursors for Row-by-Row Processing in SQL โ†’ Primary Key vs. Unique Key vs. Foreign Key in SQL โ†’ Normalization (1NF, 2NF, 3NF, BCNF) in SQL โ†’ Denormalization for Performance Optimization in SQL โ†’ EXPLAIN (Query Execution Plan) for Performance Analysis in SQL โ†’ Query Optimization Techniques (Predicate Pushdown, Join Order) in SQL โ†’ Table Partitioning (Range, List, Hash) for Large Tables in SQL โ†’ Temporary Tables for Intermediate Storage in SQL โ†’ Dynamic SQL (Building and Executing Queries at Runtime) in SQL โ†’ Database Security (User Permissions, Roles, Encryption) in SQL Grab the SQL Mastery Handbook: https://t.co/0bRVxTZBbK
e_opore's tweet photo. 100 SQL Concepts for Mastering SQL โ†’ Understanding Relational Databases and Tables in SQL โ†’ SQL vs. NoSQL (When to Use Each) in SQL โ†’ Data Definition Language (DDL): CREATE, ALTER, DROP in SQL โ†’ Data Manipulation Language (DML): INSERT, UPDATE, DELETE in SQL โ†’ Data Query Language (DQL): SELECT Statements in SQL โ†’ Data Control Language (DCL): GRANT and REVOKE in SQL โ†’ Transaction Control Language (TCL): COMMIT, ROLLBACK, SAVEPOINT in SQL โ†’ Creating Databases and Schemas in SQL โ†’ Creating Tables with Data Types (INT, VARCHAR, DATE, DECIMAL, BOOLEAN) in SQL โ†’ Using PRIMARY KEY Constraints for Row Uniqueness in SQL โ†’ Using FOREIGN KEY Constraints for Referential Integrity in SQL โ†’ Using UNIQUE Constraints to Prevent Duplicate Values in SQL โ†’ Using NOT NULL Constraints for Required Fields in SQL โ†’ Using CHECK Constraints for Value Validation in SQL โ†’ Using DEFAULT Constraints for Default Column Values in SQL โ†’ Altering Table Structures (ADD, MODIFY, DROP COLUMN) in SQL โ†’ Dropping Tables vs. Truncating Tables (DROP vs. TRUNCATE vs. DELETE) in SQL โ†’ Basic SELECT with FROM and WHERE Clauses in SQL โ†’ Using Aliases (AS) for Columns and Tables in SQL โ†’ Filtering with Comparison Operators (=, <>, >, <, >=, <=) in SQL โ†’ Filtering with Logical Operators (AND, OR, NOT) in SQL โ†’ Filtering with IN and NOT IN Operators in SQL โ†’ Filtering with BETWEEN for Range Conditions in SQL โ†’ Filtering with LIKE and Wildcards (%, _) for Pattern Matching in SQL โ†’ Handling NULL Values with IS NULL and IS NOT NULL in SQL โ†’ Sorting Results with ORDER BY (ASC, DESC) in SQL โ†’ Limiting Rows with LIMIT, TOP, or FETCH FIRST in SQL โ†’ Using DISTINCT to Remove Duplicate Rows in SQL โ†’ Aggregate Functions (COUNT, SUM, AVG, MIN, MAX) in SQL โ†’ Grouping Data with GROUP BY Clause in SQL โ†’ Filtering Groups with HAVING Clause in SQL โ†’ WHERE vs. HAVING (Pre-aggregation vs. Post-aggregation) in SQL โ†’ INNER JOIN for Matching Rows Across Tables in SQL โ†’ LEFT JOIN (LEFT OUTER JOIN) for All Left Table Rows in SQL โ†’ RIGHT JOIN (RIGHT OUTER JOIN) for All Right Table Rows in SQL โ†’ FULL OUTER JOIN for All Rows From Both Tables in SQL โ†’ CROSS JOIN for Cartesian Products in SQL โ†’ SELF JOIN for Joining a Table to Itself in SQL โ†’ NATURAL JOIN (Automatic Join on Common Columns) in SQL โ†’ UNION for Combining Results (With Duplicate Removal) in SQL โ†’ UNION ALL for Combining Results (With Duplicates Preserved) in SQL โ†’ INTERSECT for Common Rows Between Queries in SQL โ†’ EXCEPT (or MINUS) for Rows in First Query but Not Second in SQL โ†’ Subqueries in WHERE Clause (Scalar, Row, Table Subqueries) in SQL โ†’ Correlated Subqueries (Referencing Outer Query) in SQL โ†’ Subqueries in SELECT Clause (Scalar Subqueries) in SQL โ†’ Subqueries in FROM Clause (Derived Tables) in SQL โ†’ EXISTS and NOT EXISTS Operators in SQL โ†’ ANY, SOME, and ALL Operators with Subqueries in SQL โ†’ Common Table Expressions (CTE) with WITH Clause in SQL โ†’ Recursive CTEs for Hierarchical Data (Tree Traversal) in SQL โ†’ Window Functions (OVER Clause) for Row-Level Calculations in SQL โ†’ ROW_NUMBER() for Sequential Row Numbering in SQL โ†’ RANK() and DENSE_RANK() for Ranking with Tie Handling in SQL โ†’ NTILE(n) for Dividing Rows into Buckets in SQL โ†’ LEAD() and LAG() for Accessing Adjacent Row Values in SQL โ†’ FIRST_VALUE() and LAST_VALUE() for Boundary Values in SQL โ†’ SUM() OVER() for Running Totals in SQL โ†’ AVG() OVER() for Moving Averages in SQL โ†’ PARTITION BY for Window Function Groups in SQL โ†’ ORDER BY Within OVER Clause for Window Ordering in SQL โ†’ ROWS vs. RANGE for Window Frame Boundaries in SQL โ†’ CASE Expressions for Conditional Logic in SQL โ†’ COALESCE() for Returning First Non-NULL Value in SQL โ†’ NULLIF() for Conditional NULL Return in SQL โ†’ CAST() and CONVERT() for Data Type Conversion in SQL โ†’ String Functions (CONCAT, SUBSTRING, LENGTH, TRIM, UPPER, LOWER, REPLACE) in SQL โ†’ Date/Time Functions (EXTRACT, DATEADD, DATEDIFF, CURRENT_DATE, NOW) in SQL โ†’ Mathematical Functions (ROUND, CEIL, FLOOR, ABS, MOD, POWER) in SQL โ†’ INSERT INTO SELECT for Copying Data Between Tables in SQL โ†’ UPDATE with JOIN for Cross-Table Updates in SQL โ†’ DELETE with JOIN for Cross-Table Deletions in SQL โ†’ MERGE (UPSERT) for Insert or Update Logic in SQL โ†’ Transactions (BEGIN, COMMIT, ROLLBACK, SAVEPOINT) in SQL โ†’ ACID Properties (Atomicity, Consistency, Isolation, Durability) in SQL โ†’ Isolation Levels (Read Uncommitted, Read Committed, Repeatable Read, Serializable) in SQL โ†’ Dirty Reads, Non-Repeatable Reads, and Phantom Reads in SQL โ†’ Indexing Fundamentals (Clustered vs. Non-Clustered Indexes) in SQL โ†’ Creating and Dropping Indexes in SQL โ†’ Composite Indexes (Multiple Columns) in SQL โ†’ Unique Indexes for Uniqueness Enforcement in SQL โ†’ Full-Text Indexes for Text Search in SQL โ†’ Index Scan vs. Index Seek vs. Table Scan in SQL โ†’ Views (Virtual Tables) for Simplifying Queries in SQL โ†’ Materialized Views for Stored Query Results in SQL โ†’ Updatable Views (With Restrictions) in SQL โ†’ Stored Procedures for Reusable Code Blocks in SQL โ†’ Stored Functions (Scalar, Table-Valued) in SQL โ†’ Triggers (Automatic Execution on INSERT, UPDATE, DELETE) in SQL โ†’ Trigger Timing (BEFORE, AFTER, INSTEAD OF) in SQL โ†’ Cursors for Row-by-Row Processing in SQL โ†’ Primary Key vs. Unique Key vs. Foreign Key in SQL โ†’ Normalization (1NF, 2NF, 3NF, BCNF) in SQL โ†’ Denormalization for Performance Optimization in SQL โ†’ EXPLAIN (Query Execution Plan) for Performance Analysis in SQL โ†’ Query Optimization Techniques (Predicate Pushdown, Join Order) in SQL โ†’ Table Partitioning (Range, List, Hash) for Large Tables in SQL โ†’ Temporary Tables for Intermediate Storage in SQL โ†’ Dynamic SQL (Building and Executing Queries at Runtime) in SQL โ†’ Database Security (User Permissions, Roles, Encryption) in SQL Grab the SQL Mastery Handbook: https://t.co/0bRVxTZBbK
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Abraham Okah(info hub) Verified account @AbrahamOkah2
If you aren't learning AI, you're falling behind. Harvard ,Stanford, Google and Microsoft are now offering free courses. No fees or application needed. Here are 10 FREE courses you don't want to miss: 1. Introduction to AI with Python. https://t.co/9Z01j6AEBI 2. AI For Beginners. https://t.co/6PZpgrAeoU 3. Artificial Intelligence in Business. https://t.co/M4C2Y2vd3A 4. Generative AI for Everyone. https://t.co/Yv2NYsjopN 5. Ultimate ChatGPT Crash Course. https://t.co/9vzdzpW5NG 6. Introduction to Generative AI. https://t.co/QMAAySbcnM 7. Computer Science 101. https://t.co/MppW5TDA6U 8. Machine Learning Specialization. https://t.co/l86MgrBxqw 9. Introduction to Computer science. https://t.co/JtxpbpJuqh 10. Introduction to Data Science with Python. https://t.co/ivHQcsFI22
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Master SQL SQL MASTER TREE โ”‚ โ”œโ”€โ”€ 1. Database Fundamentals โ”‚ โ”œโ”€โ”€ What is DB / DBMS / RDBMS โ”‚ โ”œโ”€โ”€ Tables, Rows, Columns โ”‚ โ”œโ”€โ”€ Primary Key โ”‚ โ”œโ”€โ”€ Foreign Key โ”‚ โ”œโ”€โ”€ Candidate Key โ”‚ โ”œโ”€โ”€ Composite Key โ”‚ โ”œโ”€โ”€ Constraints โ”‚ โ”‚ โ”œโ”€โ”€ NOT NULL โ”‚ โ”‚ โ”œโ”€โ”€ UNIQUE โ”‚ โ”‚ โ”œโ”€โ”€ PRIMARY KEY โ”‚ โ”‚ โ”œโ”€โ”€ FOREIGN KEY โ”‚ โ”‚ โ”œโ”€โ”€ CHECK โ”‚ โ”‚ โ””โ”€โ”€ DEFAULT โ”‚ โ””โ”€โ”€ Data Integrity โ”‚ โ”œโ”€โ”€ 2. SQL Data Types โ”‚ โ”œโ”€โ”€ Numeric โ”‚ โ”‚ โ”œโ”€โ”€ INT โ”‚ โ”‚ โ”œโ”€โ”€ BIGINT โ”‚ โ”‚ โ”œโ”€โ”€ DECIMAL โ”‚ โ”‚ โ””โ”€โ”€ FLOAT โ”‚ โ”œโ”€โ”€ String โ”‚ โ”‚ โ”œโ”€โ”€ CHAR โ”‚ โ”‚ โ”œโ”€โ”€ VARCHAR โ”‚ โ”‚ โ””โ”€โ”€ TEXT โ”‚ โ”œโ”€โ”€ Date & Time โ”‚ โ”‚ โ”œโ”€โ”€ DATE โ”‚ โ”‚ โ”œโ”€โ”€ TIME โ”‚ โ”‚ โ”œโ”€โ”€ DATETIME โ”‚ โ”‚ โ””โ”€โ”€ TIMESTAMP โ”‚ โ””โ”€โ”€ Boolean / Binary โ”‚ โ”œโ”€โ”€ 3. DDL (Data Definition Language) โ”‚ โ”œโ”€โ”€ CREATE โ”‚ โ”‚ โ”œโ”€โ”€ DATABASE โ”‚ โ”‚ โ”œโ”€โ”€ TABLE โ”‚ โ”‚ โ””โ”€โ”€ INDEX โ”‚ โ”œโ”€โ”€ ALTER โ”‚ โ”‚ โ”œโ”€โ”€ ADD COLUMN โ”‚ โ”‚ โ”œโ”€โ”€ MODIFY COLUMN โ”‚ โ”‚ โ””โ”€โ”€ DROP COLUMN โ”‚ โ”œโ”€โ”€ DROP โ”‚ โ”‚ โ”œโ”€โ”€ DATABASE โ”‚ โ”‚ โ””โ”€โ”€ TABLE โ”‚ โ””โ”€โ”€ TRUNCATE โ”‚ โ”œโ”€โ”€ 4. DML (Data Manipulation Language) โ”‚ โ”œโ”€โ”€ INSERT โ”‚ โ”œโ”€โ”€ UPDATE โ”‚ โ”œโ”€โ”€ DELETE โ”‚ โ””โ”€โ”€ MERGE / UPSERT โ”‚ โ”œโ”€โ”€ 5. DQL (Data Query Language) โ”‚ โ”œโ”€โ”€ SELECT โ”‚ โ”œโ”€โ”€ DISTINCT โ”‚ โ”œโ”€โ”€ WHERE โ”‚ โ”‚ โ”œโ”€โ”€ AND โ”‚ โ”‚ โ”œโ”€โ”€ OR โ”‚ โ”‚ โ””โ”€โ”€ NOT โ”‚ โ”œโ”€โ”€ ORDER BY โ”‚ โ”œโ”€โ”€ GROUP BY โ”‚ โ”œโ”€โ”€ HAVING โ”‚ โ””โ”€โ”€ LIMIT / OFFSET โ”‚ โ”œโ”€โ”€ 6. SQL Operators โ”‚ โ”œโ”€โ”€ Arithmetic (+ - * /) โ”‚ โ”œโ”€โ”€ Comparison (= != > < >= <=) โ”‚ โ”œโ”€โ”€ Logical (AND OR NOT) โ”‚ โ”œโ”€โ”€ BETWEEN โ”‚ โ”œโ”€โ”€ IN โ”‚ โ”œโ”€โ”€ LIKE โ”‚ โ””โ”€โ”€ IS NULL โ”‚ โ”œโ”€โ”€ 7. SQL Functions โ”‚ โ”œโ”€โ”€ Aggregate โ”‚ โ”‚ โ”œโ”€โ”€ COUNT โ”‚ โ”‚ โ”œโ”€โ”€ SUM โ”‚ โ”‚ โ”œโ”€โ”€ AVG โ”‚ โ”‚ โ”œโ”€โ”€ MIN โ”‚ โ”‚ โ””โ”€โ”€ MAX โ”‚ โ”œโ”€โ”€ String โ”‚ โ”‚ โ”œโ”€โ”€ CONCAT โ”‚ โ”‚ โ”œโ”€โ”€ SUBSTRING โ”‚ โ”‚ โ”œโ”€โ”€ LENGTH โ”‚ โ”‚ โ””โ”€โ”€ TRIM โ”‚ โ”œโ”€โ”€ Numeric โ”‚ โ”‚ โ”œโ”€โ”€ ROUND โ”‚ โ”‚ โ””โ”€โ”€ ABS โ”‚ โ””โ”€โ”€ Date โ”‚ โ”œโ”€โ”€ NOW โ”‚ โ”œโ”€โ”€ DATEADD โ”‚ โ””โ”€โ”€ DATEDIFF โ”‚ โ”œโ”€โ”€ 8. Joins โ”‚ โ”œโ”€โ”€ INNER JOIN โ”‚ โ”œโ”€โ”€ LEFT JOIN โ”‚ โ”œโ”€โ”€ RIGHT JOIN โ”‚ โ”œโ”€โ”€ FULL JOIN โ”‚ โ”œโ”€โ”€ CROSS JOIN โ”‚ โ””โ”€โ”€ SELF JOIN โ”‚ โ”œโ”€โ”€ 9. Subqueries โ”‚ โ”œโ”€โ”€ Scalar Subquery โ”‚ โ”œโ”€โ”€ Correlated Subquery โ”‚ โ””โ”€โ”€ Nested Subquery โ”‚ โ”œโ”€โ”€ 10. Views โ”‚ โ”œโ”€โ”€ CREATE VIEW โ”‚ โ”œโ”€โ”€ UPDATE VIEW โ”‚ โ””โ”€โ”€ MATERIALIZED VIEW โ”‚ โ”œโ”€โ”€ 11. Indexing โ”‚ โ”œโ”€โ”€ Clustered Index โ”‚ โ”œโ”€โ”€ Non-Clustered Index โ”‚ โ”œโ”€โ”€ Composite Index โ”‚ โ””โ”€โ”€ Index Optimization โ”‚ โ”œโ”€โ”€ 12. Transactions โ”‚ โ”œโ”€โ”€ BEGIN โ”‚ โ”œโ”€โ”€ COMMIT โ”‚ โ”œโ”€โ”€ ROLLBACK โ”‚ โ””โ”€โ”€ SAVEPOINT โ”‚ โ”œโ”€โ”€ 13. ACID Properties โ”‚ โ”œโ”€โ”€ Atomicity โ”‚ โ”œโ”€โ”€ Consistency โ”‚ โ”œโ”€โ”€ Isolation โ”‚ โ””โ”€โ”€ Durability โ”‚ โ”œโ”€โ”€ 14. Normalization โ”‚ โ”œโ”€โ”€ 1NF โ”‚ โ”œโ”€โ”€ 2NF โ”‚ โ”œโ”€โ”€ 3NF โ”‚ โ”œโ”€โ”€ BCNF โ”‚ โ””โ”€โ”€ Denormalization โ”‚ โ”œโ”€โ”€ 15. Advanced SQL โ”‚ โ”œโ”€โ”€ Stored Procedures โ”‚ โ”œโ”€โ”€ Triggers โ”‚ โ”œโ”€โ”€ CTE (WITH) โ”‚ โ”œโ”€โ”€ Window Functions โ”‚ โ”‚ โ”œโ”€โ”€ ROW_NUMBER โ”‚ โ”‚ โ”œโ”€โ”€ RANK โ”‚ โ”‚ โ”œโ”€โ”€ DENSE_RANK โ”‚ โ”‚ โ””โ”€โ”€ PARTITION BY โ”‚ โ””โ”€โ”€ Recursive Queries โ”‚ โ”œโ”€โ”€ 16. Performance Optimization โ”‚ โ”œโ”€โ”€ Query Optimization โ”‚ โ”œโ”€โ”€ Execution Plan โ”‚ โ”œโ”€โ”€ Index Tuning โ”‚ โ””โ”€โ”€ Query Caching โ”‚ โ”œโ”€โ”€ 17. SQL Ecosystem โ”‚ โ”œโ”€โ”€ MySQL โ”‚ โ”œโ”€โ”€ PostgreSQL โ”‚ โ”œโ”€โ”€ SQLite โ”‚ โ”œโ”€โ”€ SQL Server โ”‚ โ””โ”€โ”€ Oracle DB โ”‚ โ””โ”€โ”€ 18. Real-World Usage โ”œโ”€โ”€ Backend APIs โ”œโ”€โ”€ Data Analytics โ”œโ”€โ”€ Reporting Systems โ”œโ”€โ”€ ETL Pipelines โ””โ”€โ”€ Data Warehousing
BharukaShraddha's tweet photo. Master SQL SQL MASTER TREE โ”‚ โ”œโ”€โ”€ 1. Database Fundamentals โ”‚ โ”œโ”€โ”€ What is DB / DBMS / RDBMS โ”‚ โ”œโ”€โ”€ Tables, Rows, Columns โ”‚ โ”œโ”€โ”€ Primary Key โ”‚ โ”œโ”€โ”€ Foreign Key โ”‚ โ”œโ”€โ”€ Candidate Key โ”‚ โ”œโ”€โ”€ Composite Key โ”‚ โ”œโ”€โ”€ Constraints โ”‚ โ”‚ โ”œโ”€โ”€ NOT NULL โ”‚ โ”‚ โ”œโ”€โ”€ UNIQUE โ”‚ โ”‚ โ”œโ”€โ”€ PRIMARY KEY โ”‚ โ”‚ โ”œโ”€โ”€ FOREIGN KEY โ”‚ โ”‚ โ”œโ”€โ”€ CHECK โ”‚ โ”‚ โ””โ”€โ”€ DEFAULT โ”‚ โ””โ”€โ”€ Data Integrity โ”‚ โ”œโ”€โ”€ 2. SQL Data Types โ”‚ โ”œโ”€โ”€ Numeric โ”‚ โ”‚ โ”œโ”€โ”€ INT โ”‚ โ”‚ โ”œโ”€โ”€ BIGINT โ”‚ โ”‚ โ”œโ”€โ”€ DECIMAL โ”‚ โ”‚ โ””โ”€โ”€ FLOAT โ”‚ โ”œโ”€โ”€ String โ”‚ โ”‚ โ”œโ”€โ”€ CHAR โ”‚ โ”‚ โ”œโ”€โ”€ VARCHAR โ”‚ โ”‚ โ””โ”€โ”€ TEXT โ”‚ โ”œโ”€โ”€ Date & Time โ”‚ โ”‚ โ”œโ”€โ”€ DATE โ”‚ โ”‚ โ”œโ”€โ”€ TIME โ”‚ โ”‚ โ”œโ”€โ”€ DATETIME โ”‚ โ”‚ โ””โ”€โ”€ TIMESTAMP โ”‚ โ””โ”€โ”€ Boolean / Binary โ”‚ โ”œโ”€โ”€ 3. DDL (Data Definition Language) โ”‚ โ”œโ”€โ”€ CREATE โ”‚ โ”‚ โ”œโ”€โ”€ DATABASE โ”‚ โ”‚ โ”œโ”€โ”€ TABLE โ”‚ โ”‚ โ””โ”€โ”€ INDEX โ”‚ โ”œโ”€โ”€ ALTER โ”‚ โ”‚ โ”œโ”€โ”€ ADD COLUMN โ”‚ โ”‚ โ”œโ”€โ”€ MODIFY COLUMN โ”‚ โ”‚ โ””โ”€โ”€ DROP COLUMN โ”‚ โ”œโ”€โ”€ DROP โ”‚ โ”‚ โ”œโ”€โ”€ DATABASE โ”‚ โ”‚ โ””โ”€โ”€ TABLE โ”‚ โ””โ”€โ”€ TRUNCATE โ”‚ โ”œโ”€โ”€ 4. DML (Data Manipulation Language) โ”‚ โ”œโ”€โ”€ INSERT โ”‚ โ”œโ”€โ”€ UPDATE โ”‚ โ”œโ”€โ”€ DELETE โ”‚ โ””โ”€โ”€ MERGE / UPSERT โ”‚ โ”œโ”€โ”€ 5. DQL (Data Query Language) โ”‚ โ”œโ”€โ”€ SELECT โ”‚ โ”œโ”€โ”€ DISTINCT โ”‚ โ”œโ”€โ”€ WHERE โ”‚ โ”‚ โ”œโ”€โ”€ AND โ”‚ โ”‚ โ”œโ”€โ”€ OR โ”‚ โ”‚ โ””โ”€โ”€ NOT โ”‚ โ”œโ”€โ”€ ORDER BY โ”‚ โ”œโ”€โ”€ GROUP BY โ”‚ โ”œโ”€โ”€ HAVING โ”‚ โ””โ”€โ”€ LIMIT / OFFSET โ”‚ โ”œโ”€โ”€ 6. SQL Operators โ”‚ โ”œโ”€โ”€ Arithmetic (+ - * /) โ”‚ โ”œโ”€โ”€ Comparison (= != > < >= <=) โ”‚ โ”œโ”€โ”€ Logical (AND OR NOT) โ”‚ โ”œโ”€โ”€ BETWEEN โ”‚ โ”œโ”€โ”€ IN โ”‚ โ”œโ”€โ”€ LIKE โ”‚ โ””โ”€โ”€ IS NULL โ”‚ โ”œโ”€โ”€ 7. SQL Functions โ”‚ โ”œโ”€โ”€ Aggregate โ”‚ โ”‚ โ”œโ”€โ”€ COUNT โ”‚ โ”‚ โ”œโ”€โ”€ SUM โ”‚ โ”‚ โ”œโ”€โ”€ AVG โ”‚ โ”‚ โ”œโ”€โ”€ MIN โ”‚ โ”‚ โ””โ”€โ”€ MAX โ”‚ โ”œโ”€โ”€ String โ”‚ โ”‚ โ”œโ”€โ”€ CONCAT โ”‚ โ”‚ โ”œโ”€โ”€ SUBSTRING โ”‚ โ”‚ โ”œโ”€โ”€ LENGTH โ”‚ โ”‚ โ””โ”€โ”€ TRIM โ”‚ โ”œโ”€โ”€ Numeric โ”‚ โ”‚ โ”œโ”€โ”€ ROUND โ”‚ โ”‚ โ””โ”€โ”€ ABS โ”‚ โ””โ”€โ”€ Date โ”‚ โ”œโ”€โ”€ NOW โ”‚ โ”œโ”€โ”€ DATEADD โ”‚ โ””โ”€โ”€ DATEDIFF โ”‚ โ”œโ”€โ”€ 8. Joins โ”‚ โ”œโ”€โ”€ INNER JOIN โ”‚ โ”œโ”€โ”€ LEFT JOIN โ”‚ โ”œโ”€โ”€ RIGHT JOIN โ”‚ โ”œโ”€โ”€ FULL JOIN โ”‚ โ”œโ”€โ”€ CROSS JOIN โ”‚ โ””โ”€โ”€ SELF JOIN โ”‚ โ”œโ”€โ”€ 9. Subqueries โ”‚ โ”œโ”€โ”€ Scalar Subquery โ”‚ โ”œโ”€โ”€ Correlated Subquery โ”‚ โ””โ”€โ”€ Nested Subquery โ”‚ โ”œโ”€โ”€ 10. Views โ”‚ โ”œโ”€โ”€ CREATE VIEW โ”‚ โ”œโ”€โ”€ UPDATE VIEW โ”‚ โ””โ”€โ”€ MATERIALIZED VIEW โ”‚ โ”œโ”€โ”€ 11. Indexing โ”‚ โ”œโ”€โ”€ Clustered Index โ”‚ โ”œโ”€โ”€ Non-Clustered Index โ”‚ โ”œโ”€โ”€ Composite Index โ”‚ โ””โ”€โ”€ Index Optimization โ”‚ โ”œโ”€โ”€ 12. Transactions โ”‚ โ”œโ”€โ”€ BEGIN โ”‚ โ”œโ”€โ”€ COMMIT โ”‚ โ”œโ”€โ”€ ROLLBACK โ”‚ โ””โ”€โ”€ SAVEPOINT โ”‚ โ”œโ”€โ”€ 13. ACID Properties โ”‚ โ”œโ”€โ”€ Atomicity โ”‚ โ”œโ”€โ”€ Consistency โ”‚ โ”œโ”€โ”€ Isolation โ”‚ โ””โ”€โ”€ Durability โ”‚ โ”œโ”€โ”€ 14. Normalization โ”‚ โ”œโ”€โ”€ 1NF โ”‚ โ”œโ”€โ”€ 2NF โ”‚ โ”œโ”€โ”€ 3NF โ”‚ โ”œโ”€โ”€ BCNF โ”‚ โ””โ”€โ”€ Denormalization โ”‚ โ”œโ”€โ”€ 15. Advanced SQL โ”‚ โ”œโ”€โ”€ Stored Procedures โ”‚ โ”œโ”€โ”€ Triggers โ”‚ โ”œโ”€โ”€ CTE (WITH) โ”‚ โ”œโ”€โ”€ Window Functions โ”‚ โ”‚ โ”œโ”€โ”€ ROW_NUMBER โ”‚ โ”‚ โ”œโ”€โ”€ RANK โ”‚ โ”‚ โ”œโ”€โ”€ DENSE_RANK โ”‚ โ”‚ โ””โ”€โ”€ PARTITION BY โ”‚ โ””โ”€โ”€ Recursive Queries โ”‚ โ”œโ”€โ”€ 16. Performance Optimization โ”‚ โ”œโ”€โ”€ Query Optimization โ”‚ โ”œโ”€โ”€ Execution Plan โ”‚ โ”œโ”€โ”€ Index Tuning โ”‚ โ””โ”€โ”€ Query Caching โ”‚ โ”œโ”€โ”€ 17. SQL Ecosystem โ”‚ โ”œโ”€โ”€ MySQL โ”‚ โ”œโ”€โ”€ PostgreSQL โ”‚ โ”œโ”€โ”€ SQLite โ”‚ โ”œโ”€โ”€ SQL Server โ”‚ โ””โ”€โ”€ Oracle DB โ”‚ โ””โ”€โ”€ 18. Real-World Usage โ”œโ”€โ”€ Backend APIs โ”œโ”€โ”€ Data Analytics โ”œโ”€โ”€ Reporting Systems โ”œโ”€โ”€ ETL Pipelines โ””โ”€โ”€ Data Warehousing
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Jian Wang Verified account @jianw851
Master SQL from 0 โ†’ 100 with this roadmap ๐Ÿงต If you learn this properly, youโ€™re ahead of 90% devs. Save this. Bookmark this. Execute this. ๐Ÿ‘‡ 1. Database Fundamentals โ€ข DB vs DBMS vs RDBMS โ€ข Tables, Rows, Columns โ€ข Keys: Primary, Foreign, Candidate, Composite โ€ข Constraints: NOT NULL, UNIQUE, CHECK, DEFAULT โ€ข Data Integrity 2. SQL Data Types โ€ข Numeric โ†’ INT, BIGINT, DECIMAL โ€ข String โ†’ VARCHAR, TEXT โ€ข Date โ†’ DATE, TIMESTAMP โ€ข Boolean 3. DDL (Structure Control) โ€ข CREATE (DB, Table, Index) โ€ข ALTER (Add/Modify/Delete columns) โ€ข DROP โ€ข TRUNCATE 4. DML (Data Control) โ€ข INSERT โ€ข UPDATE โ€ข DELETE โ€ข UPSERT 5. DQL (Querying Data) โ€ข SELECT, DISTINCT โ€ข WHERE (AND, OR, NOT) โ€ข ORDER BY โ€ข GROUP BY + HAVING โ€ข LIMIT 6. SQL Operators โ€ข Arithmetic (+ โˆ’ ร— รท) โ€ข Comparison (=, >, <) โ€ข Logical (AND, OR) โ€ข IN, BETWEEN, LIKE, NULL 7. Functions (Most Asked in Interviews) โ€ข Aggregate โ†’ COUNT, SUM, AVG โ€ข String โ†’ CONCAT, LENGTH โ€ข Numeric โ†’ ROUND โ€ข Date โ†’ NOW, DATEDIFF 8. Joins (๐Ÿ”ฅ Must Master) โ€ข INNER โ€ข LEFT / RIGHT โ€ข FULL โ€ข CROSS โ€ข SELF 9. Subqueries โ€ข Scalar โ€ข Correlated โ€ข Nested 10. Views โ€ข Virtual tables โ€ข Materialized views 11. Indexing (โšก Speed Booster) โ€ข Clustered โ€ข Non-clustered โ€ข Composite 12. Transactions โ€ข BEGIN โ€ข COMMIT โ€ข ROLLBACK โ€ข SAVEPOINT 13. ACID (Core DB Concept) โ€ข Atomicity โ€ข Consistency โ€ข Isolation โ€ข Durability 14. Normalization โ€ข 1NF โ†’ 3NF โ†’ BCNF โ€ข + Denormalization 15. Advanced SQL (๐Ÿš€) โ€ข Stored Procedures โ€ข Triggers โ€ข CTE (WITH) โ€ข Window Functions (RANK, ROW_NUMBER) 16. Performance Optimization โ€ข Query tuning โ€ข Execution plans โ€ข Index optimization 17. SQL Ecosystem โ€ข MySQL โ€ข PostgreSQL โ€ข SQLite โ€ข SQL Server โ€ข Oracle 18. Real-World Usage โ€ข Backend APIs โ€ข Analytics โ€ข ETL Pipelines โ€ข Data Warehousing If you complete this roadmap: Youโ€™re not โ€œlearning SQLโ€ anymoreโ€ฆ Youโ€™re thinking in SQL. Follow for more dev roadmaps ๐Ÿš€
jianw851's tweet photo. Master SQL from 0 โ†’ 100 with this roadmap ๐Ÿงต If you learn this properly, youโ€™re ahead of 90% devs. Save this. Bookmark this. Execute this. ๐Ÿ‘‡ 1. Database Fundamentals โ€ข DB vs DBMS vs RDBMS โ€ข Tables, Rows, Columns โ€ข Keys: Primary, Foreign, Candidate, Composite โ€ข Constraints: NOT NULL, UNIQUE, CHECK, DEFAULT โ€ข Data Integrity 2. SQL Data Types โ€ข Numeric โ†’ INT, BIGINT, DECIMAL โ€ข String โ†’ VARCHAR, TEXT โ€ข Date โ†’ DATE, TIMESTAMP โ€ข Boolean 3. DDL (Structure Control) โ€ข CREATE (DB, Table, Index) โ€ข ALTER (Add/Modify/Delete columns) โ€ข DROP โ€ข TRUNCATE 4. DML (Data Control) โ€ข INSERT โ€ข UPDATE โ€ข DELETE โ€ข UPSERT 5. DQL (Querying Data) โ€ข SELECT, DISTINCT โ€ข WHERE (AND, OR, NOT) โ€ข ORDER BY โ€ข GROUP BY + HAVING โ€ข LIMIT 6. SQL Operators โ€ข Arithmetic (+ โˆ’ ร— รท) โ€ข Comparison (=, >, <) โ€ข Logical (AND, OR) โ€ข IN, BETWEEN, LIKE, NULL 7. Functions (Most Asked in Interviews) โ€ข Aggregate โ†’ COUNT, SUM, AVG โ€ข String โ†’ CONCAT, LENGTH โ€ข Numeric โ†’ ROUND โ€ข Date โ†’ NOW, DATEDIFF 8. Joins (๐Ÿ”ฅ Must Master) โ€ข INNER โ€ข LEFT / RIGHT โ€ข FULL โ€ข CROSS โ€ข SELF 9. Subqueries โ€ข Scalar โ€ข Correlated โ€ข Nested 10. Views โ€ข Virtual tables โ€ข Materialized views 11. Indexing (โšก Speed Booster) โ€ข Clustered โ€ข Non-clustered โ€ข Composite 12. Transactions โ€ข BEGIN โ€ข COMMIT โ€ข ROLLBACK โ€ข SAVEPOINT 13. ACID (Core DB Concept) โ€ข Atomicity โ€ข Consistency โ€ข Isolation โ€ข Durability 14. Normalization โ€ข 1NF โ†’ 3NF โ†’ BCNF โ€ข + Denormalization 15. Advanced SQL (๐Ÿš€) โ€ข Stored Procedures โ€ข Triggers โ€ข CTE (WITH) โ€ข Window Functions (RANK, ROW_NUMBER) 16. Performance Optimization โ€ข Query tuning โ€ข Execution plans โ€ข Index optimization 17. SQL Ecosystem โ€ข MySQL โ€ข PostgreSQL โ€ข SQLite โ€ข SQL Server โ€ข Oracle 18. Real-World Usage โ€ข Backend APIs โ€ข Analytics โ€ข ETL Pipelines โ€ข Data Warehousing If you complete this roadmap: Youโ€™re not โ€œlearning SQLโ€ anymoreโ€ฆ Youโ€™re thinking in SQL. Follow for more dev roadmaps ๐Ÿš€
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thedigitor__'s profile image
Goodness | The Digitor @thedigitor__
Data Analyst, leave Kaggle alone. It's for Nepo babies. Here are 12 underutilized platforms that offer you real world datasets to make your portfolio stand out. 1. Maven Analytics Data Playground - https://t.co/9JE836Fr1L 2. Google Dataset Search - https://t.co/8oGlqBUWDK 3. U.S. Government's Open Data: https://t.co/YIOqB6qytq 4. OECD Data: https://t.co/ycadaVR1J6 5. World Bank Data: https://t.co/qdJqEdeWy0 6. University of Michigan ICPSR: https://t.co/CB0Vk1Kw4V 7. UN data: https://t.co/hVvvFo2YTF 8. StatCan (Statistics Canada): https://t.co/h1XzaS4hpw 9. Eurostat: https://t.co/DTYT099pg2 10. WHO Global Health Observatory: https://t.co/y2fpuhqSU5 11. UN Comtrade Database: https://t.co/e7tTY5lC8d 12. UCI Machine Learning Repository - https://t.co/ocyYZMomNM Found this helpful? Save and share it to help other aspiring data analyst who needs it. I'll see you in the next one. โ˜› You're welcome! . . ยฉ๏ธ Goodness | The Digitor I'm a DATA ANALYST and STORYTELLER and Iโ€™m on a mission to help newbies like you go from shy, confused, broke and invisible, to confident, skilled, paid and positioned for global opportunities. You can now book a 2 hours 1:1 virtual clarity call with me here: https://t.co/83MtYFY3vr Anyhooo, I have 2 FREEBIES for you and I'm so sure you'll love them. Get them here: https://t.co/MPNlXaUk0d
thedigitor__'s tweet photo. Data Analyst, leave Kaggle alone. It's for Nepo babies. Here are 12 underutilized platforms that offer you real world datasets to make your portfolio stand out. 1. Maven Analytics Data Playground - https://t.co/9JE836Fr1L 2. Google Dataset Search - https://t.co/8oGlqBUWDK 3. U.S. Government's Open Data: https://t.co/YIOqB6qytq 4. OECD Data: https://t.co/ycadaVR1J6 5. World Bank Data: https://t.co/qdJqEdeWy0 6. University of Michigan ICPSR: https://t.co/CB0Vk1Kw4V 7. UN data: https://t.co/hVvvFo2YTF 8. StatCan (Statistics Canada): https://t.co/h1XzaS4hpw 9. Eurostat: https://t.co/DTYT099pg2 10. WHO Global Health Observatory: https://t.co/y2fpuhqSU5 11. UN Comtrade Database: https://t.co/e7tTY5lC8d 12. UCI Machine Learning Repository - https://t.co/ocyYZMomNM Found this helpful? Save and share it to help other aspiring data analyst who needs it. I'll see you in the next one. โ˜› You're welcome! . . ยฉ๏ธ Goodness | The Digitor I'm a DATA ANALYST and STORYTELLER and Iโ€™m on a mission to help newbies like you go from shy, confused, broke and invisible, to confident, skilled, paid and positioned for global opportunities. You can now book a 2 hours 1:1 virtual clarity call with me here: https://t.co/83MtYFY3vr Anyhooo, I have 2 FREEBIES for you and I'm so sure you'll love them. Get them here: https://t.co/MPNlXaUk0d
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Adufe @Tomilaay
@DabereNnamani Yes please
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Annie๐Ÿฆ‹ Verified account @DabereNnamani
If youโ€™re a Data Analyst, looking for experience to add to your CV, Here are 10 platforms where you can volunteer or intern in data analytics, all with relatively straightforward application processes: VOLUNTEER PLATFORMS 1. DataKind (https://t.co/gie9SGxUJW) You create a profile on https://t.co/EM799aAPkL, add your data skills and experience, and get considered for projects as they arise. Volunteers can work remotely from anywhere in the world. ๏ฟผ Great for applying real-world analytical skills to social impact causes. 2. Catchafire (https://t.co/poQyscJO23) You can volunteer in various ways ranging from a short 1-hour phone call to a 3-month scoped project, working remotely. Skills like data analysis, digital marketing, and IT can be offered to nonprofits. ๏ฟผ One of the easiest sign-up processes out there. 3. Taproot Foundation (https://t.co/8lbd9YZF4u) Taproot acts as a middleman between social impact organizations and professionals with the right skills. People can post projects or browse available ones, and the platform connects volunteers with pro-bono consulting opportunities. ๏ฟผ 4. Solve for Good (DSSG Solve) (https://t.co/t4786IUq1W) Itโ€™s a platform where social good organizations post data projects they need help with, and volunteers help scope and solve those problems using data-driven methods. ๏ฟผ Good for both beginners and intermediate analysts. 5. Omdena (https://t.co/CvR5K65KrX) Omdena offers a collaborative environment where participants work in teams to solve real-world problems using AI and data science. You gain hands-on experience with the benefit of mentorship from experts, and a certificate is provided at the end. ๏ฟผ INTERNSHIP PLATFORMS 6. Internshala (https://t.co/7WURbdjNYK) Internshala offers various remote data science internships with startups and companies. While some roles are unpaid, they provide a great learning experience with hands-on projects, and a certificate is awarded for most positions. ๏ฟผ Very popular and beginner-friendly. 7. The Sparks Foundation (https://t.co/E1Hbjgt3L1) A widely known virtual internship program in data analytics and data science thatโ€™s easy to apply for. Projects are self-paced and you earn a certificate upon completion great for portfolio building. 8. Extern (https://t.co/26BFE2eZGD) Extern offers externships specifically designed for those with zero experience, with real-world impact opportunities and resources like resume templates and direct application links. ๏ฟผ 9. https://t.co/dKMWchVWPQ / GitHub Internship Repo (https://t.co/2hgbTCTrrz) This is a curated repository of data analysis internship opportunities updated regularly, pulling from 400,000+ positions added daily and matching them to your skills and experience. ๏ฟผ Low friction, you browse and apply directly. 10. Idealist (https://t.co/UCZ0ZiAZFa) Idealist is listed among platforms where you can find jobs and volunteer roles in social-change organizations. ๏ฟผ It frequently lists data-related volunteer and intern roles with nonprofits globally, and applying is as simple as submitting a profile.
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Ezekiel Verified account @ezekiel_aleke
My go to channels on YouTube for Data Analytics contents: 9. Data With Ezekiel 8. Alex the Analyst 7. Kevin Stratvert 6. Leila Gharani 5. Luke Barouse 4. How to Power BI 3. AnalyzeWithAli 2. Pragmatic Works 1. Learnit Training Don't forget to repost๐Ÿ”ฅ
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Shraddha Bharuka Verified account @BharukaShraddha
๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€ ๐Ÿšจ ๐Ÿ“˜ Introduction to Machine Learning Systems ๐Ÿ‘จ๐Ÿซ By a Harvard Professor ๐Ÿ“„ 2,048 Pages of Pure ML Gold Want it? ๐Ÿ‘‡ 1๏ธโƒฃ Like, Retweet &amp; Reply "ML" 2๏ธโƒฃ Follow (Iโ€™ll DM the book) ๐Ÿ”ฅ Donโ€™t miss outโ€”this will NOT stay free forever.
BharukaShraddha's tweet photo. ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€ ๐Ÿšจ ๐Ÿ“˜ Introduction to Machine Learning Systems ๐Ÿ‘จ๐Ÿซ By a Harvard Professor ๐Ÿ“„ 2,048 Pages of Pure ML Gold Want it? ๐Ÿ‘‡ 1๏ธโƒฃ Like, Retweet &amp; Reply "ML" 2๏ธโƒฃ Follow (Iโ€™ll DM the book) ๐Ÿ”ฅ Donโ€™t miss outโ€”this will NOT stay free forever. https://t.co/i07azhxBo5
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Abraham Okah(info hub) Verified account @AbrahamOkah2
Looking to get Started in Tech? Here are 18 FREE Tech courses with Certificates ๐Ÿ‘‡๐Ÿฝ 1. Cyber Security: https://t.co/apPbQEVv7h 2. Web Development: https://t.co/ZV1f07zHZE 3. Data Science: https://t.co/W1CMbFvnqG 4. CS50 from Harvard: https://t.co/pHmBbWs4wg 5. IBM Data Science: https://t.co/GPQ8MpTPCs 6. Python: https://t.co/zupOVPofq6 7. HTML, CSS, JavaScript: https://t.co/STeLz3n8LK 8. SQL: https://t.co/TtsPAameZV 9. Java: https://t.co/voBeaWInX4 10. C/C++: https://t.co/qANmsaOsem 11. DSA: https://t.co/mXbvEWjLeI 12. Linux: https://t.co/roPhEv42Ns 13. Machine Learning: https://t.co/x4AC5rqBow 14. Networking: https://t.co/w7tIXDvwXO 15. Software Engineering: https://t.co/2lyJSpJo9J 16. Computer Architecture: https://t.co/9oJHkYqmJm 17. GitHub: https://t.co/UYW8FtO5VV 18. DSA Problem Solving for Interviews using Java: https://t.co/t6rdkH8oo1
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Shruti Codes Verified account @Shruti_0810
Excel &amp; SQL For Data Analysts #sql
Shruti_0810's tweet photo. Excel &amp; SQL For Data Analysts #sql https://t.co/BY9oyonFPa
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Rony Verified account @Ronycoder
Paid Course? I'm giving you access to 15+ FREE Courses 1. Microsoft Excel 2. Advanced Excel 3. Probability 4. Statistics 5. Mathematics 6. Tableau 7. PowerBI 8. SQL 9. SQL + Tableau 10. Python 11. Python Programmer Bootcamp 12. Git and GitHub And Many More To get it, just: 1. Like & Retweet 2. Comment "Free" 3. MUST be Following (so that I can dm)
Ronycoder's tweet photo. Paid Course? I'm giving you access to 15+ FREE Courses 1. Microsoft Excel 2. Advanced Excel 3. Probability 4. Statistics 5. Mathematics 6. Tableau 7. PowerBI 8. SQL 9. SQL + Tableau 10. Python 11. Python Programmer Bootcamp 12. Git and GitHub And Many More To get it, just: 1. Like & Retweet 2. Comment "Free" 3. MUST be Following (so that I can dm)
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Gabby Verified account @thenaijacarguy
When to use what chart
thenaijacarguy's tweet photo. When to use what chart https://t.co/4Dhh8HvbG3
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Job Corner Verified account @JOBCORNER247
๐…๐ซ๐ž๐ž ๐„๐ฑ๐œ๐ž๐ฅ ๐‘๐ž๐ฌ๐จ๐ฎ๐ซ๐œ๐ž๐ฌ ๐ญ๐จ ๐›๐ž๐œ๐จ๐ฆ๐ž ๐š ๐ƒ๐š๐ญ๐š ๐€๐ง๐š๐ฅ๐ฒ๐ฌ๐ญ ๐ข๐ง ๐Ÿ๐ŸŽ๐Ÿ๐Ÿ“: 1. Excel Data Analytics Full Course: https://t.co/wjFNtvizvZ 2. Excel Tutorials for Data Analysts: https://t.co/Yh926Z6pOZ 3. Practise Excel: https://t.co/Ah5wm3zV8f 4. Excel Project Recommendation: https://t.co/h3mvWiCqnk 5. Excel Skills for Business Specialization: https://t.co/UAuI4Uqfp1 6. Introduction to Microsoft Excel: https://t.co/DTEZN2bfJ2 7. Excel Basics for Data Analysis: https://t.co/wE8e5N9oQH 8. Excel video training: https://t.co/JyG7mvPifJ 9. Useful Excel for Beginners: https://t.co/Nj5tflVSDA 10.Excel Quick Start Tutorial: https://t.co/ownZoO4jwv 11. Excel for Beginners: https://t.co/wyHAw2HOT2 12. Introduction to MS Excel: https://t.co/17MVK7z9I5 13. Data Analytics using Excel: https://t.co/FfD5rUZQrY 14. Introduction to Data Analysis Using Excel: https://t.co/fBaYVAcBxi 15. Data Visualization with Advanced Excel: https://t.co/agCPH8LQFV 16. Data Analysis with Python: https://t.co/ichP39b57E 17. Master Microsoft Excel https://t.co/t3T7MfEq0d 18. Microsoft Excel Tutorial for Beginners https://t.co/xi7AqaPqT0 ๐Ÿ“Œ๐—ช๐—ฒ๐—ฏ๐˜€๐—ถ๐˜๐—ฒ๐˜€ ๐˜๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—˜๐˜…๐—ฐ๐—ฒ๐—น ๐Ÿš€ โ€ข Excel Easy https://t.co/8g0gez6ca8 โ€ข Excel Campus https://t.co/K733IooOHH โ€ข Chandoo https://t.co/vjaMVhF9DR โ€ข Exceljet https://t.co/aOtKvwi1IV โ€ข Contextures https://t.co/9YebMVNo6E โ€ข MyExcelOnline https://t.co/lR6Xo8J9nq Save it!!
JOBCORNER247's tweet photo. ๐…๐ซ๐ž๐ž ๐„๐ฑ๐œ๐ž๐ฅ ๐‘๐ž๐ฌ๐จ๐ฎ๐ซ๐œ๐ž๐ฌ ๐ญ๐จ ๐›๐ž๐œ๐จ๐ฆ๐ž ๐š ๐ƒ๐š๐ญ๐š ๐€๐ง๐š๐ฅ๐ฒ๐ฌ๐ญ ๐ข๐ง ๐Ÿ๐ŸŽ๐Ÿ๐Ÿ“: 1. Excel Data Analytics Full Course: https://t.co/wjFNtvizvZ 2. Excel Tutorials for Data Analysts: https://t.co/Yh926Z6pOZ 3. Practise Excel: https://t.co/Ah5wm3zV8f 4. Excel Project Recommendation: https://t.co/h3mvWiCqnk 5. Excel Skills for Business Specialization: https://t.co/UAuI4Uqfp1 6. Introduction to Microsoft Excel: https://t.co/DTEZN2bfJ2 7. Excel Basics for Data Analysis: https://t.co/wE8e5N9oQH 8. Excel video training: https://t.co/JyG7mvPifJ 9. Useful Excel for Beginners: https://t.co/Nj5tflVSDA 10.Excel Quick Start Tutorial: https://t.co/ownZoO4jwv 11. Excel for Beginners: https://t.co/wyHAw2HOT2 12. Introduction to MS Excel: https://t.co/17MVK7z9I5 13. Data Analytics using Excel: https://t.co/FfD5rUZQrY 14. Introduction to Data Analysis Using Excel: https://t.co/fBaYVAcBxi 15. Data Visualization with Advanced Excel: https://t.co/agCPH8LQFV 16. Data Analysis with Python: https://t.co/ichP39b57E 17. Master Microsoft Excel https://t.co/t3T7MfEq0d 18. Microsoft Excel Tutorial for Beginners https://t.co/xi7AqaPqT0 ๐Ÿ“Œ๐—ช๐—ฒ๐—ฏ๐˜€๐—ถ๐˜๐—ฒ๐˜€ ๐˜๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—˜๐˜…๐—ฐ๐—ฒ๐—น ๐Ÿš€ โ€ข Excel Easy https://t.co/8g0gez6ca8 โ€ข Excel Campus https://t.co/K733IooOHH โ€ข Chandoo https://t.co/vjaMVhF9DR โ€ข Exceljet https://t.co/aOtKvwi1IV โ€ข Contextures https://t.co/9YebMVNo6E โ€ข MyExcelOnline https://t.co/lR6Xo8J9nq Save it!!
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