SynergisticIT’s Post

𝗡𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝘆 𝗦𝗸𝗶𝗹𝗹𝘀 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝟰. 𝗦𝗤𝗟 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 SQL is an excellent tool for retrieving and manipulating data effectively. Read more: https://buff.ly/3l1f6sC #datascience #dataanalyst #dataanalystjobs #datascienceskills #skills #TechSkills #datasciencecareer #upskill #SynergisticIT

𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗖𝗿𝘂𝗰𝗶𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝟰. 𝗦𝗤𝗟 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 Query databases like a pro! SQL is your tool for retrieving and manipulating data efficiently. Read more: https://buff.ly/3l1f6sC #skills #datascience #DataScienceSkills #techskills #datasciencecareer #dataanalyst #dataanalystskill #SynergisticIT

𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗧𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗔 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝟰. 𝗦𝗤𝗟 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 SQL is the language of databases. Analysts use it to retrieve, manage, and manipulate data efficiently. Read more: https://buff.ly/3l1f6sC #dataanalyst #datascience #skills #keyskills #DataScienceSkills #datasciencecourse #datasciencetraining #SynergisticIT

𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀? 𝟯. 𝗦𝗤𝗟 𝗦𝗸𝗶𝗹𝗹𝘀 SQL is a widely used language for querying, manipulating, and analyzing data in databases. Read more: https://buff.ly/3l1f6sC #dataanalysis #dataanalyst #skills #skillset #datascience #SynergisticIT

𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗧𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗔 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝟰. 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗜𝗻 𝗦𝗤𝗟 Proficiency in SQL is important as it is the language of databases. Analysts utilize it to retrieve, manage, and manipulate data efficiently. Read more: https://buff.ly/3l1f6sC #skills #skillset #dataanalyst #essentialskills #becomeadataanalyst #datascience #SynergisticIT

𝗧𝗵𝗲 𝗠𝗼𝘀𝘁 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟯 𝟱. 𝗦𝗤𝗟 A standard language used to communicate with databases. Knowing SQL lets you update, organize, and query data stored in relational databases. Read more: https://buff.ly/3l1f6sC #datascience #DataAnalyst #dataanalysis #skills #techskills #DataScienceSkills #DataAnalysisSkills #SynergisticIT

7️⃣ SQL Concepts You Should Know For Data Science - 🎇 1) Understanding of Basic Commands   Knowledge of basic #commands builds the foundation for lifelong learning. Otherwise, you will just be memorizing the facts without understanding how they fit together. Some of the most commonly used SQL commands are as follows SELECT & FROM: to retrieve the attributes of #data from the mentioned table. SELECT DISTINCT: it eliminates duplicate rows and displays only the unique records. WHERE: it filters the #record and shows only the ones that satisfy the given condition. AND, OR, NOT: not execute the query when the #condition is not True. While, AND and OR are used to apply multiple conditions. ORDER BY: it sorts the data in ascending or descending order GROUP BY: it groups identical data. HAVING: data aggregated by Group By can be further filtered out here. Aggregate functions: #aggregate functions like COUNT(), MAX(), MIN(), AVG(), and SUM() are used to perform operations on the given data. 2) Case When    It is a really powerful and flexible statement in SQL used to write complex conditional statements. It offers the functionality of the IF.THEN.ELSE statements. 3) Subqueries   As a data scientist, the knowledge of #subqueries is essential as they need to work with different tables and the result of one query may be used again to further restrict the data in the main query. It is also known as the nested or the inner query. 4) Joins   SQL #joins are used to combine the rows from multiple tables based on the logical relationship between them. 5) Stored Procedures   Stored procedures allow us to store multiple SQL statements in our #database to use them later on. It enables reusability and can also accept the parameter values when called. It enhances the performance and it is easier to make any modifications with it. 6) String Formatting   We all know that the raw data needs to be cleaned to increase overall productivity resulting in quality decision-making. #String formatting plays a huge role in this context and it involves manipulating the strings to remove irrelevant things 7) Window Functions   Window functions are similar to the aggregate functions but it does not cause your rows to collapse into a single row after the calculation. Instead, the rows retain their separate identities. #dataanalytics #sql #datascience #dataanalyst #sqlqueries

SQL is the heart to data professionals! Gathering insights from data or gathering data both are important, but what interests data engineers more is to gather quality data to build insights for business. My perspective is to gather quality data before gathering insights out of it, and that's why I enjoy working as a data engineer. 👨💻Working with SQL is the utmost important part of data engineers day to day activities. ➡️Be it to store, analyze, consume for modelling or just use it to engineer it, SQL plays a vital role in the data domain. Here's an interesting and extensive mindmap on SQL to follow. 🔥 🔰This comprises of wide range of concepts from functions to joins , from order of execution to some advance concepts. ♐It's an amazing handy well crafted cheat sheet curated with informative and interesting topics to explore, learn and excel SQL. Few of the major concepts to focus while preparing for interviews includes - -> Aliases - To rename columns or outcomes -> Group By - Group identical data arranging into different segments -> Order By - Sort data in ascending or descending order -> Joins - To join two or more tables to get common attributes -> Functions - To perform various operations on data -> Where - Filter the records based on the specific condition -> Subqueries - Nested queries used to perform complex operations 📊Have a look at the more shared resources to explore more👇 - 📍SQL Tutorial by Tutorialspoint : https://lnkd.in/dka4_dFX 📍Free SQL for Data Analysis course by Mode on Udacity : https://lnkd.in/dBhveAC9 📍Data Analysis using sql and excel by Gordon S. Linoff : https://lnkd.in/diGj8sVN 📍SQL on Big Data - Technology, Architecture, and Innovation: https://lnkd.in/dyhahYqh 📍SQL cheatsheet by Data36.com : https://lnkd.in/dsnE4V7M Hope this helps you to get on to the right path in this complicated data journey with enormous data and skills! #databases #dataengineering #computing #bigdata #data #sql #analytics #engineering

🚀 Eager to Unlock Data Insights! 📊🔍 Dear LinkedIn Community, Thrilled to share my passion for transforming raw data into valuable insights! 💡 Mastering advanced SQL is just the beginning of my journey. Let's delve into the skills that set me apart: ✅ SQL Mastery: Subqueries, CTEs, Views, Temporary Tables: Expert in crafting complex queries with mini-queries, temporary memory tables, and specialized data views. 📊📝🔍 ✅ Data Management Proficiency: ETL, Data Warehouse, OLAP, OLTP Basics: Grounded in Extract, Transform, Load (ETL) processes, data storage nuances, and the distinctions between OLAP and OLTP. 📚💡🏦 ✅ Code Efficiency and Reusability: Custom Functions, Stored Procedures: Crafting personalized functions and routines to enhance SQL code reusability and efficiency. 🧩💻🔄 ✅ Advanced Analysis with Window Functions: OVER, ROW_NUMBER, RANK, DENSE_RANK Mastery: Utilizing advanced tools for intricate calculations and detailed analysis within datasets. 📊🔍🧮 ✅ Data Engineering Essentials: Indexes, Triggers, Events, User Accounts & Permissions: Expertise in critical elements for robust data engineering solutions. 🛠️🔐💼 ✅ Real-World Application: Real Dataset (1M+ Records): Applied knowledge in practical projects with large datasets, building a strong foundation for real-world data challenges. 📈🏢💼 Excited about new opportunities! If you're looking to discuss SQL, data analysis, or explore exciting projects together, feel free to reach out. Let's connect and learn from each other! 👥🚀 A big shoutout to #Dhaval Patel sir, #Hemanand Vadivel sir, and codebasics for an amazing learning experience. 🙏 #DataAnalysis #SQL #DataEngineering #LinkedInLearning #DataInsights #CareerGrowth #Thankful

SQL Series Day-4 💡 Exploring Aggregate Functions in SQL: Power of Data Summarization 💡   In the ever-expanding landscape of database management, aggregate functions serve as the cornerstone for extracting meaningful insights from raw data. These powerful functions enable us to summarize, analyze, and derive valuable statistics from datasets, empowering us to make informed decisions and uncover hidden trends. Understanding Aggregate Functions⏭️👇 Aggregate functions in SQL are specialized functions that operate on sets of values and return a single value as output. Unlike standard scalar functions, which operate on individual rows, aggregate functions perform computations across multiple rows, enabling us to derive summary statistics and metrics from datasets. Commonly Used Aggregate Functions: ✅SUM(): Calculates the sum of values within a dataset, facilitating totalization and aggregation of numerical data. ✅AVG(): Computes the average or mean value of a dataset, providing insights into central tendency and distribution. ✅COUNT(): Determines the number of rows or values in a dataset, it can work on both numeric and non-numeric data. ✅MIN() and MAX(): Identify the minimum and maximum values within a dataset, enabling identification of extremes and boundaries. ✅GROUP_CONCAT(): Concatenates values from multiple rows into a single string, useful for aggregating textual data. it is particularly useful for combing related data in a compact format. Aggregate functions in SQL serve as indispensable tools for summarizing, analyzing, and interpreting data across diverse domains and industries. Let's continue to learn and grow together as a community of data professionals 😊. #DataEngineering #SQL #DatabaseDevelopment #DataManagement #dataengineers #dataanalytics #data #sqltips #dataanalyst #datacareer #datamanagement #DataAnalysis #AggregateFunctions