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🛑 Stop paying for online courses ever again. SQL Roadmap with FREE courses Learning SQL is essential for anyone dealing with relational databases or working in the field of data management. So You can take benifts from below ones 👇 🔺 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔹https://lnkd.in/e3uPshjh Repost so that all can take benifits 1. Learn SQL Basics for Data Science Specialization In this course, you will learn to: -Master SQL Commands -Data Preparation -End-to-End Data Pipeline -Project Development and Presentation: 🔺Link: https://lnkd.in/eFfhe57d 2. Google Data Analytics Professional Certificate -This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹Link: https://lnkd.in/euJUQjiP 3.SQL for Data Science What you'll learn -Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. -Use SQL commands to filter, sort, and summarize data. -Create an analysis table from multiple queries using the UNION operator. -Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis. 🔺Link: https://lnkd.in/eAX2aehf 4.Databases and SQL for Data Science with Python What you'll learn -Analyze data within a database using SQL and Python. -Create a relational database and work with multiple tables using DDL commands. -Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. 🔹Link: https://lnkd.in/emhYQ9A4 5. Introduction to Structured Query Language (SQL) What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses.What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses. 🔺Link: https://lnkd.in/ea7YQBpk Pdf credit- Respective Owner follow Suman Kumar for more Like 👍 Comment ✍️ repost 🔄 #sql #sqldatabase #freecertification #freecourses #dataanalytics #datasciences

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🛑 Stop paying for online courses ever again. SQL Roadmap with FREE courses Learning SQL is essential for anyone dealing with relational databases or working in the field of data management. So You can take benifts from below ones 👇 🔺 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔹https://lnkd.in/e3uPshjh Repost so that all can take benifits 1. Learn SQL Basics for Data Science Specialization In this course, you will learn to: -Master SQL Commands -Data Preparation -End-to-End Data Pipeline -Project Development and Presentation: 🔺Link: https://lnkd.in/eFfhe57d 2. Google Data Analytics Professional Certificate -This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹Link: https://lnkd.in/euJUQjiP 3.SQL for Data Science What you'll learn -Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. -Use SQL commands to filter, sort, and summarize data. -Create an analysis table from multiple queries using the UNION operator. -Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis. 🔺Link: https://lnkd.in/eAX2aehf 4.Databases and SQL for Data Science with Python What you'll learn -Analyze data within a database using SQL and Python. -Create a relational database and work with multiple tables using DDL commands. -Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. 🔹Link: https://lnkd.in/emhYQ9A4 5. Introduction to Structured Query Language (SQL) What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses.What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses. 🔺Link: https://lnkd.in/ea7YQBpk Pdf credit- Respective Owner Follow Suman Kumar for more Like 👍 Comment ✍️ Repost 🔄 #sql #sqldatabase #freecertification #freecourses #dataanalytics #datasciences#roadmap LinkedIn for Creators LinkedIn Learning

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4 Free Resources For SQL Interview Preparation ✅ Data Interview Pro by Emma Ding The first resource is the Data Interview Pro YouTube channel. This is really good to know what to expect in a SQL interview. It also will give you a very good review of the important concepts in addition to important tips to ace and pass the interview. ➡Link: https://lnkd.in/gCR-tPhR ✅ Leetcode The second resource to prepare for SQL interviews is Leetcode. LeetCode is a popular website that provides a platform for preparing for technical interviews, including those for SQL positions. The site offers a range of SQL problems, from beginner to advanced levels, which can help you prepare for SQL-related interview questions. ➡Link: https://leetcode.com/ ✅ Stratascratch The third resource is the Stratascratch platform. StrataScratch is a website that provides a platform for preparing for coding interviews, including those that involve SQL. The site offers a range of SQL problems, from beginner to advanced levels, which can help you prepare for SQL-related interview questions. ➡Link: https://lnkd.in/e4VTTfN ✅ DataLemur The fourth resource is the DataLemur website. This is a really good website if you are preparing for an SQL interview at a big company. It provides SQL questions from top companies from easy to medium and to hard questions. ➡Link: https://datalemur.com/ ➡ Do not miss the 60% discount on To Data & Beyond yearly subscription: https://lnkd.in/gmwFUqYr ➡ Check out To Data & Beyond's latest ebook: LLM Roadmap from Beginner to Advanced Level: https://lnkd.in/dp7_CssN

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🛑 Stop paying for online courses ever again. SQL Roadmap with FREE courses Learning SQL is essential for anyone dealing with relational databases or working in the field of data management. So You can take benifts from below ones 👇 🔺 7000+ Course Free Access : https://lnkd.in/gAypVcZr <>. Google Data Analytics: 🔹https://lnkd.in/gx4HsCst Repost so that all can take benifits 1. Learn SQL Basics for Data Science Specialization In this course, you will learn to: -Master SQL Commands -Data Preparation -End-to-End Data Pipeline -Project Development and Presentation: 🔺Link: https://lnkd.in/gK9UhPa3 2. Google Data Analytics Professional Certificate -This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹Link: https://lnkd.in/g7VexVyZ 3.SQL for Data Science What you'll learn -Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. -Use SQL commands to filter, sort, and summarize data. -Create an analysis table from multiple queries using the UNION operator. -Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis. 🔺Link: https://lnkd.in/gCydEhCM 4.Databases and SQL for Data Science with Python What you'll learn -Analyze data within a database using SQL and Python. -Create a relational database and work with multiple tables using DDL commands. -Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. 🔹Link: https://lnkd.in/gNujzRay 5. Introduction to Structured Query Language (SQL) What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses.What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses. 🔺Link: https://lnkd.in/gBzTYdh3 Pdf credit- Respective Owner ⭐ Follow - SUMIT KUMAR For More 👉 Like 👍 Comment 💬 #sql #sqldatabase #freecertification #freecourses #dataanalytics #datasciences#roadmap LinkedIn for Creators LinkedIn Learning

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SQL Complete Learning Plan in 2024 |-- Week 1: Introduction to SQL |   |-- SQL Basics |   |   |-- What is SQL? |   |   |-- History and Evolution of SQL |   |   |-- Relational Databases |   |-- Setting up for SQL |   |   |-- Installing MySQL/PostgreSQL |   |   |-- Setting up a Database |   |   |-- Basic SQL Syntax |   |-- First SQL Queries |   |   |-- SELECT Statements |   |   |-- WHERE Clauses |   |   |-- Basic Filtering | |-- Week 2: Intermediate SQL |   |-- Advanced SELECT Queries |   |   |-- ORDER BY |   |   |-- LIMIT |   |   |-- Aliases |   |-- Joining Tables |   |   |-- INNER JOIN |   |   |-- LEFT JOIN |   |   |-- RIGHT JOIN |   |   |-- FULL OUTER JOIN |   |-- Aggregations |   |   |-- COUNT, SUM, AVG, MIN, MAX |   |   |-- GROUP BY |   |   |-- HAVING Clauses | |-- Week 3: Advanced SQL Techniques |   |-- Subqueries |   |   |-- Basic Subqueries |   |   |-- Correlated Subqueries |   |-- Window Functions |   |   |-- ROW_NUMBER, RANK, DENSE_RANK |   |   |-- NTILE, LEAD, LAG |   |-- Advanced Joins |   |   |-- Self Joins |   |   |-- Cross Joins |   |-- Data Types and Functions |   |   |-- Date Functions |   |   |-- String Functions |   |   |-- Numeric Functions | |-- Week 4: Database Design and Normalization |   |-- Database Design Principles |   |   |-- ER Diagrams |   |   |-- Relationships and Cardinality |   |-- Normalization |   |   |-- First Normal Form (1NF) |   |   |-- Second Normal Form (2NF) |   |   |-- Third Normal Form (3NF) |   |-- Indexes and Performance Tuning |   |   |-- Creating Indexes |   |   |-- Understanding Execution Plans |   |   |-- Optimizing Queries | |-- Week 5: Stored Procedures and Functions |   |-- Stored Procedures |   |   |-- Creating Stored Procedures |   |   |-- Parameters in Stored Procedures |   |   |-- Error Handling |   |-- Functions |   |   |-- Scalar Functions |   |   |-- Table-Valued Functions |   |   |-- System Functions | |-- Week 6: Transactions and Concurrency |   |-- Transactions |   |   |-- ACID Properties |   |   |-- COMMIT and ROLLBACK |   |   |-- Savepoints |   |-- Concurrency Control |   |   |-- Locking Mechanisms |   |   |-- Isolation Levels |   |   |-- Deadlocks and How to Avoid Them | |-- Week 7-8: Advanced SQL Topics |   |-- Triggers |   |   |-- Creating and Using Triggers |   |   |-- AFTER and BEFORE Triggers |   |   |-- INSTEAD OF Triggers |   |-- Views |   |   |-- Creating Views |   |   |-- Updating Views |   |   |-- Indexed Views |   |-- Security |   |   |-- User Management |   |   |-- Roles and Permissions |   |   |-- SQL Injection Prevention | |-- Week 9-11: Real-world Applications and Projects |   |-- Capstone Project |   |   |-- Designing a Database Schema |   |   |-- Implementing the Schema |   |   |-- Writing Complex Queries |   |   |-- Optimizing and Tuning Here you can find free SQL Resources 👇👇 https://t.me/sqlspecialist Hope it helps :)

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𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 Share with everyone so that everyone get benifts <> 7000+ Courses Access : : https://lnkd.in/e7A7hd-t Google Data Analytics: 🔹 https://lnkd.in/edtXhkzb [[𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬:]] - Learn the basics of linear algebra, calculus, and Understand advanced concepts. Mathematics: https://lnkd.in/ejH42Rs7 [[𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠:]] - Learn Python and R, the most popular programming languages. - Master essential libraries like NumPy, Pandas, Matplotlib. - Learn how to use databases like SQL and MongoDB. 🔹Python: https://lnkd.in/eyFAXpnZ 🔹R language: https://lnkd.in/d35WCs37 🔹SQL: https://lnkd.in/e26sy9N3 🔹MongoDB: https://lnkd.in/eC8Dgj66 [[𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬:]] - Understand the fundamentals of probability and statistics. - Learn how to apply these concepts to real-world data problems. 🔹Probability: https://lnkd.in/ePMjCpVc 🔹Statistics: https://lnkd.in/ePMjCpVc [[𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:]] - Learn the basics of machine learning, including model construction, data exploration, and validation. - Explore intermediate concepts like handling missing values, categorical variables. - Dive into ensemble learning techniques like Random Forests. https://lnkd.in/ea4bYqTx [[𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:]] - Learn about artificial neural networks, convolutional neural networks, and recurrent neural networks. - Implement deep learning models using TensorFlow, or PyTorch. - Understand crucial concepts like stochastic gradient descent, dropout. 🔹https://lnkd.in/gSyAkqpZ [Happy Learning] follow @Kanchan Kumari for more Like comment 👍 👍 #datascience #datascienceroadmap #sql #python #development #mongodb #devops #deeplearning

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#DailyPost #questionoftheday #𝐌𝐨𝐜𝐤𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧  Are you a #PMP aspirant. Don’t miss to answer the below question posted by me every day. Put your answer in comment section and look for correct answer next day. 𝑨 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝑴𝒂𝒏𝒂𝒈𝒆𝒓 𝒊𝒔 𝒅𝒐𝒊𝒏𝒈 𝒕𝒉𝒆 𝒇𝒐𝒍𝒍𝒐𝒘𝒊𝒏𝒈: -C𝒐𝒏𝒅𝒖𝒄𝒕𝒊𝒏𝒈 𝒂 𝒓𝒊𝒔𝒌 𝒂𝒏𝒂𝒍𝒚𝒔𝒊𝒔 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒂 𝒉𝒊𝒈𝒉-𝒍𝒆𝒗𝒆𝒍 𝒔𝒄𝒐𝒑𝒆, 𝒂𝒏𝒅 𝒖𝒔𝒊𝒏𝒈 𝒆𝒙𝒑𝒆𝒓𝒕 𝒋𝒖𝒅𝒈𝒆𝒎𝒆𝒏𝒕 𝒕𝒐 𝒑𝒓𝒆𝒑𝒂𝒓𝒆 𝒂 𝒅𝒐𝒄𝒖𝒎𝒆𝒏𝒕. 𝑾𝒉𝒂𝒕 𝒊𝒔 𝒕𝒉𝒆 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝑴𝒂𝒏𝒂𝒈𝒆𝒓 𝒅𝒐𝒊𝒏𝒈, 𝒃𝒂𝒔𝒆𝒅 𝒐𝒏 𝒕𝒉𝒆 𝒅𝒆𝒔𝒄𝒓𝒊𝒑𝒕𝒊𝒐𝒏 𝒐𝒇 𝒕𝒉𝒆 𝒂𝒄𝒕𝒊𝒗𝒊𝒕𝒚? 𝑨.𝑪𝒓𝒆𝒂𝒕𝒊𝒏𝒈 𝒂 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒄𝒉𝒂𝒓𝒕𝒆𝒓 𝑩.𝑷𝒓𝒆𝒑𝒂𝒓𝒊𝒏𝒈 𝒂 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒔𝒄𝒐𝒑𝒆 𝒔𝒕𝒂𝒕𝒆𝒎𝒆𝒏𝒕 𝑪.𝑪𝒓𝒆𝒂𝒕𝒊𝒏𝒈 𝒕𝒉𝒆 𝒑𝒓𝒐𝒋𝒆𝒄𝒕 𝒎𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝒑𝒍𝒂𝒏 𝑫.𝑷𝒓𝒆𝒑𝒂𝒓𝒊𝒏𝒈 𝒂 𝒓𝒊𝒔𝒌 𝒎𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝒑𝒍𝒂𝒏 ..Follow me on LinkedIn Neetu Verma ,PMI ATP Instructor or on below link https://lnkd.in/ddRAZH9    Practice with me daily and get yourself certified with flying colors…All the best ….    For PMI trainings, Corporates/ Institutes / Individuals  Contact: Neetu Verma ,PMI ATP Instructor  𝐂𝐚𝐥𝐥 / 𝐰𝐡𝐚𝐭𝐬𝐚𝐩𝐩 : +𝟗𝟕𝟏𝟓𝟎𝟏𝟖𝟏𝟎𝟓𝟒𝟖    Get daily updated PMP question very near to real PMP exam...  My linkedin page https://lnkd.in/dy8D-e4  Facebook page: https://lnkd.in/dGnEWY9  You tube page: https://lnkd.in/ex5wnD-M    #pmp #pmpcertification #pmi #pmbok7 #pmpcertificationexam #questionoftheday #mocktest

𝐃𝐨𝐧'𝐭 𝐆𝐞𝐭 𝐂𝐨𝐧𝐟𝐮𝐬𝐞𝐝 𝐛𝐲 𝐒𝐐𝐋 𝐐𝐮𝐞𝐫𝐲 𝐎𝐫𝐝𝐞𝐫! Here's the key: writing order is different from execution order. 𝐖𝐫𝐢𝐭𝐢𝐧𝐠: We typically follow a logical flow: 1. SELECT what data we want 2. FROM where to get it 3. WHERE to filter specific rows 4. GROUP BY to categorize data 5. HAVING to filter groups 6. ORDER BY to sort results 7. LIMIT the number of results 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧: Behind the scenes, the SQL engine optimizes for efficiency: 1. Identify the data source (FROM & JOINs) 2. Apply filtering conditions (WHERE) 3. Group data (GROUP BY) 4. Filter groups (HAVING) 5. Select desired columns (SELECT) 6. Sort results (ORDER BY) 7. Limit the number of results #SQL #DataAnalysis #Sqlqueries #Data #DatabaseManagement #Share

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Stop Spending a Huge Amount of Money on Courses every again👇 Become a Google Certified Data Analytics for FREE No payment is required! ✅✅ 🔺 7000+ Course Free Access : https://lnkd.in/gtVQZnpE 1. Google Data Analytics: In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹 https://lnkd.in/gsV-x7W8 3. Data Analysis with R Programming In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R. 🔺 https://lnkd.in/gmpT6RDx 4. Foundations: Data, Data, Everywhere This course will help you gain an understanding of the practices and processes used by data analysts in their day-to-day job. 🔹 https://lnkd.in/gbNtFda8 5. Ask Questions to Make Data-Driven Decisions This course will help you learn how to ask effective questions to make data-driven decisions while connecting with stakeholders’ needs. 🔺 https://lnkd.in/g-PU_cX2 6. Process Data from Dirty to Clean This course will help you learn how to check and clean your data using spreadsheets and SQL as well as how to verify and report your data cleaning results. 🔹 https://lnkd.in/gyEHxbNt 7. Share Data Through the Art of Visualization In this course, you’ll learn how to visualize and present your data findings as you complete the data analysis process. 🔺 https://lnkd.in/g2-QW2sN 8. Analyze Data to Answer Questions In this course, you’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways. 🔹 https://lnkd.in/gv5V49rJ 9. Get Started with Python 🔺 https://lnkd.in/gcSYp8VW 10. Go Beyond the Numbers: Translate Data into Insights 🔹 https://lnkd.in/gBC3G6jd 11. The Power of Statistics 🔺 https://lnkd.in/ggK_DMsM 12. Regression Analysis: Simplify Complex Data Relationships 🔹 https://lnkd.in/gSE-RS3D #data #datanalytics #datascience #dataengineering

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Hii Connection SQL PROJECT  Project Overview  This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore, clean, and analyze retail sales data. The project involves setting up a retail sales database, performing exploratory data analysis (EDA), and answering specific business questions through SQL queries. This project is ideal for those who are starting their journey in data analysis and want to build a solid foundation in SQL.  Objectives  Set up a retail sales database: Create and populate a retail sales database with the provided sales data.  Data Cleaning: Identify and remove any records with missing or null values.  Exploratory Data Analysis (EDA): Perform basic exploratory data analysis to understand the dataset.  Business Analysis: Use SQL to answer specific business questions and derive insights from the sales data.  Project Structure  1. Database Setup  Database Creation: The project starts by creating a database named p1_retail_db.  Table Creation: A table named retail_sales is created to store the sales data. The table structure includes columns for transaction ID, sale date, sale time, customer ID, gender, age, product category, quantity sold, price per unit, cost of goods sold (COGS), and total sale amount.  Findings  Customer Demographics: The dataset includes customers from various age groups, with sales distributed across different categories such as Clothing and Beauty.  High-Value Transactions: Several transactions had a total sale amount greater than 1000, indicating premium purchases.  Sales Trends: Monthly analysis shows variations in sales, helping identify peak seasons.  Customer Insights: The analysis identifies the top-spending customers and the most popular product categories.  Reports  Sales Summary: A detailed report summarizing total sales, customer demographics, and category performance.  Trend Analysis: Insights into sales trends across different months and shifts.  Customer Insights: Reports on top customers and unique customer counts per category.  Conclusion  This project serves as a comprehensive introduction to SQL for data analysts, covering database setup, data cleaning, exploratory data analysis, and business-driven SQL queries. The findings from this project can help drive business decisions by understanding sales patterns, customer behavior, and product performance. 

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What is SQL (Part 1) SQL (Structured Query Language) is a powerful tool for managing and analyzing relational databases. It is essential for extracting and manipulating data stored in SQL databases. Here’s how SQL can be effectively used in data analysis: 1. Data Retrieval : SQL queries are used to retrieve specific data from one or more tables. Basic queries involve using the `SELECT` statement to specify the columns you want to view:    ```    SELECT column1, column2    FROM table_name    WHERE condition;    ``` 2. Data Filtering : Use the `WHERE` clause to filter data based on certain conditions. This allows you to focus on relevant subsets of data:    ```    SELECT *    FROM table_name    WHERE column_name = 'value';    ``` 3. Data Aggregation : SQL provides aggregation functions like `SUM`, `AVG`, `COUNT`, `MIN`, and `MAX` to compute summary statistics:    ```    SELECT column_name, COUNT(*)    FROM table_name    GROUP BY column_name;    ``` 4. Data Grouping : Use the `GROUP BY` clause to aggregate data across different categories. Combined with aggregation functions, it helps in summarizing and analyzing grouped data:    ```    SELECT category, AVG(sales)    FROM sales_table    GROUP BY category;    ``` 5. Data Joining : SQL allows you to combine data from multiple tables using `JOIN` operations. This is useful for analyzing data from different sources:    - INNER JOIN : Returns rows with matching values in both tables.      ```      SELECT a.column1, b.column2      FROM table1 a      INNER JOIN table2 b ON a.common_column = b.common_column;      ```    - LEFT JOIN : Returns all rows from the left table and matched rows from the right table.    - RIGHT JOIN : Returns all rows from the right table and matched rows from the left table.    - FULL JOIN : Returns rows when there is a match in one of the tables. To know more about our trainings, please visit our website www.pinpoint-hr.com for more information please send an email to [email protected]

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Roadmap to Becoming an SQL Expert 🔥 ➡️ Step 1: Understand the Basics Learn what a database is and why it’s important. Familiarize yourself with basic SQL concepts: tables, rows, columns. ➡️ Step 2: Start Writing Simple Queries Learn to use SELECT to retrieve data from a table. Practice using WHERE to filter data. Use ORDER BY to sort your results. ➡️ Step 3: Dive Deeper into Queries Understand how to use JOIN to combine data from multiple tables. Learn about different types of joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN. Practice using GROUP BY and aggregate functions like COUNT, SUM, AVG, MIN, MAX. ➡️ Step 4: Modify Data Learn to insert new data into tables with INSERT. Understand how to update existing data with UPDATE. Practice removing data with DELETE. ➡️ Step 5: Master Advanced SQL ▪️ Explore subqueries and how to use them in complex queries. ▪️ Learn about window functions for advanced data analysis. ▪️ Understand the use of indexes to speed up your queries. ➡️ Step 6: Database Design ▪️ Learn about normalization and how to design efficient databases. ▪️ Understand the concepts of primary keys and foreign keys. ➡️ Step 7: Practice, Practice, Practice ▪️ Use online platforms like SQLZoo, LeetCode, or HackerRank to practice SQL problems. ▪️ Work on real-world projects or datasets to solidify your knowledge. ➡️ Step 8: Learn About Database Management Systems (DBMS) ▪️ Understand the differences between popular DBMS like MySQL, PostgreSQL, SQL Server, and Oracle. ▪️ Learn about database backup, recovery, and security. ➡️ Step 9: Optimize and Tune Your Queries ▪️ Learn about query optimization techniques. ▪️ Understand how to read and use execution plans. ➡️ Step 10: Stay Updated and Network ▪️ Join SQL communities, forums, and LinkedIn groups. ▪️ Follow SQL experts and keep up with the latest trends and updates in SQL and database technologies. ➡️ Bonus Tip: Get Certified ▪️ Consider getting SQL certifications from reputable organizations to validate your skills. I am also in Journey of finding data analyst job so let's connect and start practicing together ❤️ Follow Ritik Singh 🔥 --------------------------------------- #SQL #DataAnalysis #Database #SQLJourney #LearnSQL #SQLBeginner #SQLExpert #DataScience #TechLearning #SQLRoadmap #Coding #TechEducation #SQLCommunity #DatabaseManagement #DataSkills #SQLQueries #TechTips #CareerGrowth #TechAdvice #ContinuousLearning

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Hey Data Analyst Enthusiasts, Let's Exploring the Five Pillars of SQL 🚀 Today's Topic : SQL Commands 🌟 Pillars of SQL: DDL, DML, DQL, and TCL 🌟 SQL (Structured Query Language) is the backbone of database management, enabling us to interact with relational databases. Understanding the key SQL command categories - DDL, DML, DQL, DCL, and TCL is essential. 🔹 DDL (Data Definition Language): DDL commands define the structure of the database and its objects. Key commands include: CREATE: Build new tables, views, indexes and other objects. ALTER: Modify existing database objects. DROP: Delete objects from the database. TRUNCATE: Remove all records from a table, but not the table itself. 🔹 DML (Data Manipulation Language): DML commands deal with the manipulation of data within the database. Essential commands are: INSERT: Add new records to a table. UPDATE: Modify existing records. DELETE: Remove records from a table. MERGE: Combine insert, update, and delete operations. 🔹 DQL (Data Query Language): DQL is primarily focused on querying the database to retrieve data. The core command is: SELECT: Fetch data from one or more tables. 🔹 DCL (Data Control Language): DCL commands are essential for managing database security and access control. GRANT : To give users specific privileges on database objects REVOKE : To remove previously granted permissions from a user or group of users. 🔹 TCL (Transaction Control Language): TCL commands manage transactions within the database, ensuring data integrity and handling rollbacks. Important commands include: COMMIT: Save all changes made during the transaction. ROLLBACK: Undo changes since the last commit. SAVEPOINT: Set a point within a transaction to which you can later roll back. Understanding these command categories helps you write efficient and effective SQL queries, maintain data integrity, and manage databases proficiently. 🚀 Feel free to share your experiences or ask questions about SQL commands in the comments! Let's learn and grow together. 👥 Connect with me for more insights on database management and SQL best practices! #SQL #DataScience #DatabaseManagement #Programming #TechSkills #DataDefinition #DataManipulation #DataQuery #DataControl #TransactionControl

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Power Bi Training : Beginning to Advance Level Audience: 1.    Fresher who wants to clear Interview. 2.    Business Analyst. 3.    Data Analyst 4.    Data Scientist 5.    Data Engineers 6.    MIS   Call & WhatsApp at- +91-8252140245,+91-7485041347 to get more updates. Demo Session 16-07-2024 8 PM IST  1. Column Chart 2. Stacked Column Chart 3. Pie Chart 4. Donut Chart 5. Funnel Chart 6. Ribbon Chart 7. Include & Exclude 8. View Data & Export 9. MAP 10. Filled Map 11. Map With Pie Chart 12. Map Formatting 13. Background Change in Map 14. Creating A Simple Table 15. Formatting a Table 16. Conditional Formatting in Table 17. Change Aggregation in Table 18. Creating A Simple Matrix 19. Conditional Formating in Matrix 20. Hirearchies in Matrix 21. SubTotal and Total in Matrix 22. Number Formatting in Matrix and Table 23. Line Chart 24. Drill Down in Line Chart 25. Area Chart 26. Line vs Column Chart 27. Scatter Plot 28. Water Fall Chart 29. Tree Map 30. Guage Chart 31. Number Card 32. Text Card 33. Date Card 35. Multi Row Card 36. Filter on Visual 37. Filter on Page 38. Filter in all Page 39. Drill Through 40. Text Slicer 41. Format Text Slicer 42. Date Slicer 44. Slicer for Number 45. Insert Objects 46. Index 47. BookMark Action 48. DrillThrough Action 49. Text Functions in Power Query Merge Split and Trim Upper, Lower and Proper Add Suffix and Prefix LEFT, RIGHT AND MID EXTRACT TEXT WITH DELIMITER 50 Date Function in Power Query Year Quater,Month,Day Difference between Dates,Earliest and Latest Name of Day and Name of Month Day of Week/Month/Year & Week of Month/Year Extract Date from Date & Time Calculate Age in 2 Button Clicks Which Day of Year,Quarter,Month your date of Birth Is 51 Appending Files and Tables in Power BI Append Multiple CSV Files from a Folder Append Multiple excel tables/Sheets from Single file Append Excel Table with different number of Columns Append multiple excel files from a Folder Append different data source files in Power Query 52. Merging Files and Tables in Power BI-[ Vlookup Without Formula] Merge Data from multiple Excel Files/Workbooks in Power Query Merging Table/Sheets Merge Data from different Data Sources in Power Query (Power BI) Merge Data having multiple matching Columns or multiple Criteria in Power Query 54. Columns from Examples and Conditional Column in Power Bi Column from Examples on Splitting Text Column From Examples on Merging Text Column from Examples on Alphanumeric Data Conditional Column on one Column Conditional Column on two Columns Conditional Column Comparing two column values 55. Important Topic in Power BI Fill Down and Fill Up Grouping Transpose UnPivot Replace Error and Values Keep Rows and Remove Rows Add, Remove ,GoTO Columns 56 DAX Function RELATED Day Month QUARTER Year Weeknum WEEKDAY Today() Now() Datediff DATEADD STARTOFMONTH ENDOFMONTH EoMonth UPPER Lower LEFT RIGHT Mid Trim Len Concatenate Search SUBSTITUTE If IFERROR And OR 57. Dashboard 58. Power BI Service

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Power Bi Training : Beginning to Advance Level Audience: 1.    Fresher who wants to clear Interview. 2.    Business Analyst. 3.    Data Analyst 4.    Data Scientist 5.    Data Engineers 6.    MIS   Call & WhatsApp at- +91-8252140245,+91-7485041347 to get more updates. Demo Session 18-07-2024 8 PM IST  1. Column Chart 2. Stacked Column Chart 3. Pie Chart 4. Donut Chart 5. Funnel Chart 6. Ribbon Chart 7. Include & Exclude 8. View Data & Export 9. MAP 10. Filled Map 11. Map With Pie Chart 12. Map Formatting 13. Background Change in Map 14. Creating A Simple Table 15. Formatting a Table 16. Conditional Formatting in Table 17. Change Aggregation in Table 18. Creating A Simple Matrix 19. Conditional Formating in Matrix 20. Hirearchies in Matrix 21. SubTotal and Total in Matrix 22. Number Formatting in Matrix and Table 23. Line Chart 24. Drill Down in Line Chart 25. Area Chart 26. Line vs Column Chart 27. Scatter Plot 28. Water Fall Chart 29. Tree Map 30. Guage Chart 31. Number Card 32. Text Card 33. Date Card 35. Multi Row Card 36. Filter on Visual 37. Filter on Page 38. Filter in all Page 39. Drill Through 40. Text Slicer 41. Format Text Slicer 42. Date Slicer 44. Slicer for Number 45. Insert Objects 46. Index 47. BookMark Action 48. DrillThrough Action 49. Text Functions in Power Query Merge Split and Trim Upper, Lower and Proper Add Suffix and Prefix LEFT, RIGHT AND MID EXTRACT TEXT WITH DELIMITER 50 Date Function in Power Query Year Quater,Month,Day Difference between Dates,Earliest and Latest Name of Day and Name of Month Day of Week/Month/Year & Week of Month/Year Extract Date from Date & Time Calculate Age in 2 Button Clicks Which Day of Year,Quarter,Month your date of Birth Is 51 Appending Files and Tables in Power BI Append Multiple CSV Files from a Folder Append Multiple excel tables/Sheets from Single file Append Excel Table with different number of Columns Append multiple excel files from a Folder Append different data source files in Power Query 52. Merging Files and Tables in Power BI-[ Vlookup Without Formula] Merge Data from multiple Excel Files/Workbooks in Power Query Merging Table/Sheets Merge Data from different Data Sources in Power Query (Power BI) Merge Data having multiple matching Columns or multiple Criteria in Power Query 54. Columns from Examples and Conditional Column in Power Bi Column from Examples on Splitting Text Column From Examples on Merging Text Column from Examples on Alphanumeric Data Conditional Column on one Column Conditional Column on two Columns Conditional Column Comparing two column values 55. Important Topic in Power BI Fill Down and Fill Up Grouping Transpose UnPivot Replace Error and Values Keep Rows and Remove Rows Add, Remove ,GoTO Columns 56 DAX Function RELATED Day Month QUARTER Year Weeknum WEEKDAY Today() Now() Datediff DATEADD STARTOFMONTH ENDOFMONTH EoMonth UPPER Lower LEFT RIGHT Mid Trim Len Concatenate Search SUBSTITUTE If IFERROR And OR 57. Dashboard 58. Power BI Service

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Power Bi Training : Beginning to Advance Level Audience: 1.    Fresher who wants to clear Interview. 2.    Business Analyst. 3.    Data Analyst 4.    Data Scientist 5.    Data Engineers 6.    MIS   Call & WhatsApp at- +91-8252140245,+91-7485041347 to get more updates.  1. Column Chart 2. Stacked Column Chart 3. Pie Chart 4. Donut Chart 5. Funnel Chart 6. Ribbon Chart 7. Include & Exclude 8. View Data & Export 9. MAP 10. Filled Map 11. Map With Pie Chart 12. Map Formatting 13. Background Change in Map 14. Creating A Simple Table 15. Formatting a Table 16. Conditional Formatting in Table 17. Change Aggregation in Table 18. Creating A Simple Matrix 19. Conditional Formating in Matrix 20. Hirearchies in Matrix 21. SubTotal and Total in Matrix 22. Number Formatting in Matrix and Table 23. Line Chart 24. Drill Down in Line Chart 25. Area Chart 26. Line vs Column Chart 27. Scatter Plot 28. Water Fall Chart 29. Tree Map 30. Guage Chart 31. Number Card 32. Text Card 33. Date Card 35. Multi Row Card 36. Filter on Visual 37. Filter on Page 38. Filter in all Page 39. Drill Through 40. Text Slicer 41. Format Text Slicer 42. Date Slicer 44. Slicer for Number 45. Insert Objects 46. Index 47. BookMark Action 48. DrillThrough Action 49. Text Functions in Power Query Merge Split and Trim Upper, Lower and Proper Add Suffix and Prefix LEFT, RIGHT AND MID EXTRACT TEXT WITH DELIMITER 50 Date Function in Power Query Year Quater,Month,Day Difference between Dates,Earliest and Latest Name of Day and Name of Month Day of Week/Month/Year & Week of Month/Year Extract Date from Date & Time Calculate Age in 2 Button Clicks Which Day of Year,Quarter,Month your date of Birth Is 51 Appending Files and Tables in Power BI Append Multiple CSV Files from a Folder Append Multiple excel tables/Sheets from Single file Append Excel Table with different number of Columns Append multiple excel files from a Folder Append different data source files in Power Query 52. Merging Files and Tables in Power BI-[ Vlookup Without Formula] Merge Data from multiple Excel Files/Workbooks in Power Query Merging Table/Sheets Merge Data from different Data Sources in Power Query (Power BI) Merge Data having multiple matching Columns or multiple Criteria in Power Query 54. Columns from Examples and Conditional Column in Power Bi Column from Examples on Splitting Text Column From Examples on Merging Text Column from Examples on Alphanumeric Data Conditional Column on one Column Conditional Column on two Columns Conditional Column Comparing two column values 55. Important Topic in Power BI Fill Down and Fill Up Grouping Transpose UnPivot Replace Error and Values Keep Rows and Remove Rows Add, Remove ,GoTO Columns 56 DAX Function RELATED Day Month QUARTER Year Weeknum WEEKDAY Today() Now() Datediff DATEADD STARTOFMONTH ENDOFMONTH EoMonth UPPER Lower LEFT RIGHT Mid Trim Len Concatenate Search SUBSTITUTE If IFERROR And OR 57. Dashboard 58. Power BI Service

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𝐒𝐩𝐚𝐫𝐤 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 🚀 🤵 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐞𝐫: You’re reading a 10 GB file in Spark. How Spark decides the number of partitions and tasks? 𝐌𝐞: Assuming the file block size 128 MB, we get 80 partitions (10 GB / 128 MB ~ 80). As each partition corresponds to a task. So, will have 80 tasks, one for each partition. 🤵 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐞𝐫: Suppose spark.default.parallelism and spark.sql.shuffle.partitions are both set to 200. What happens next, especially when transformations are applied? 𝐌𝐞: Here's how it works: 🔸 spark.default.parallelism controls the number of partitions for narrow transformations on RDDs, particularly for non-file sources (e.g., Kafka, RDD creation). Since we're reading from a file, the initial partitions are based on the file's block size, which gave us 80 partitions. 🔸 When we reach a shuffle operation (like groupBy() or join()), Spark uses spark.sql.shuffle.partitions, which is set to 200. After the shuffle, Spark repartitions the data into 200 partitions, creating 200 tasks for the next stage. 🤵 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐞𝐫: Let’s say you apply 20 transformations, and some of them are shuffle operations. How many stages & tasks per stage, will Spark create? 𝐌𝐞: If we have, say, 3 shuffle operations, Spark will divide the job into 4 stages (one for each set of transformations before and after each shuffle). The first stage will handle the 80 partitions with 80 tasks. Each subsequent stage (after a shuffle) will use 200 partitions and therefore run with 200 tasks. 🤵 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐞𝐫: So, can the number of partitions ever change while reading a file? 𝐌𝐞: No, the number of partitions cannot change during the file read process itself. However, once the data is loaded, we can explicitly change the partitions using methods like .repartition() or .coalesce(). 🤵 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐞𝐫: Sounds like you’ve got it. Any advice for optimizing partitions and tasks in Spark? 𝐌𝐞: Absolutely! The key is to tune spark.default.parallelism and spark.sql.shuffle.partitions based on your data size and cluster resources. Under-partitioning (too few partitions) can lead to uneven task distribution and skewed workloads, causing some tasks to take much longer than others. Over-partitioning (too many partitions) can create too much overhead, as managing and scheduling an excessive number of small tasks can slow down the job. ✅ Follow Pritam Saha for more Data Engineering posts. ****************** 𝗕𝗶𝗴𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: https://lnkd.in/gYFZacjF ****************** #Spark #pyspark #dataengineering #dataengineer #interviewquestions

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