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Data Analyst Required Knowledge

🔍 Seeking Opportunities as a Data Analyst! 🔍 SQL forms the backbone of my expertise in data analysis. From crafting intricate queries to optimizing database performance, my proficiency in SQL enables me to extract actionable insights from complex datasets. Here are key SQL topics excel in: Basic SQL Queries: Retrieving and filtering data efficiently using SELECT, WHERE, ORDER BY, and LIMIT clauses. Joins: Utilizing INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN to merge data from multiple tables seamlessly. Aggregation Functions: Applying SUM, AVG, COUNT, MIN, and MAX to analyze and summarize data across large datasets. Subqueries: Employing scalar, correlated, and nested subqueries to perform complex data manipulations and retrievals. Grouping and Having: Using GROUP BY and HAVING clauses to segment data and apply aggregate functions conditionally. Indexes: Understanding the importance of indexes (clustered and non-clustered) for optimizing query performance and data retrieval. Normalization: Designing databases in compliance with 1NF, 2NF, and 3NF to ensure data integrity and efficient storage. Transactions and Locks: Implementing transactions with ACID properties and managing database concurrency through locking mechanisms. Stored Procedures and Functions: Developing reusable code blocks with stored procedures and functions to streamline data processing tasks. Performance Tuning: Implementing optimization techniques such as indexing strategies and query restructuring to enhance database performance. I'm eager to leverage my SQL skills and passion for data analysis to contribute effectively to your organization's success. Let's connect to discuss how my expertise can add value to your team's data initiatives! #DataAnalysis #SQL #DataAnalytics #DatabaseManagement #JobSearch

If you know SQL then don't only apply for Data Analyst roles, Consider these options too: - 👇 1. Business Analyst:- SQL skills help in extracting and analyzing data from databases to support business decisions and reporting. 2. Business Intelligence:- SQL is essential for querying databases and creating dashboards and reports that provide insights into business performance. 3. Risk Analyst:- SQL can be used to retrieve and analyze data to identify and assess risks, helping in creating risk mitigation strategies. 4. Product Analyst:- SQL helps in analyzing product performance and user behaviour by querying large datasets to derive actionable insights. 5. Operations Analyst:- SQL is useful for managing and analyzing operational data to improve efficiency and optimize processes. 6. Market Analyst:- SQL enables the analysis of market data, trends, and customer insights, aiding in strategic decision-making. 7. Research Analyst:- SQL helps in gathering and analyzing data from various sources to support research projects and reports. 8. Database Administrator:- SQL is fundamental for managing databases, performing queries, and ensuring data integrity and security. 9. Financial Analyst:- SQL is used to extract financial data, perform complex calculations, and generate financial reports for analysis. P.S. Please repost if you find this information useful. ♻ For related posts, please follow 💻 Rishabh Chauhan . . . . #dataanlyst #sql #Analyst #DataScience #Mysql

When I'm hiring a data analyst, here's what SQL I'd expect candidates with 1-2 years of experience to know: 1/ JOINS. Why? Because after a bit of time working with data, you'll be expected to look at the bigger picture. Creating analysis that is well-rounded and captures different angles. Selecting data from one table won't be enough. 2/ CTEs and TEMP Tables. As the volume of data you are required to work with increases, you need to understand how to write efficient queries and split up more complicated tasks into easy-to-manage pieces. CTEs and temp tables allow you to handle more complex tasks. 3/ Window functions. As your analytical mind expands, you'll need to write more complex code. Calculating, ranking, totalling (cumulative and non-cumulative), as well as slicing and dicing data. RANK, DENSE_RANK, and PARTITION BY are some examples. 4/ Data Cleaning and Transformation You need to know how to clean and transform data using CASE statements, data type conversions, and string manipulations. As data is very rarely clean, you need to enhance the quality of your work. 5/ Subqueries It's not a deal-breaker, but it's still good to know how and when to use them. You should know how to use subqueries to retrieve data from one table or result set and use it within another query. This is essential for complex filtering, joining, and aggregating tasks but can be inefficient if not used correctly. TL;DR: - JOINs. - CTES and TEMP Tables. - Window Functions. - Data cleaning and transformation. - Subqueries. If you found this interesting, consider following me, Tom Mitchell, for more content like this.

If you know SQL then don't only apply for Data Analyst roles, Consider these options too:- 1. Business Analyst: SQL skills help in extracting and analyzing data from databases to support business decisions and reporting. 2. Business Intelligence: SQL is essential for querying databases and creating dashboards and reports that provide insights into business performance. 3. Risk Analyst: SQL can be used to retrieve and analyze data to identify and assess risks, helping in creating risk mitigation strategies. 4. Product Analyst: SQL helps in analyzing product performance and user behavior by querying large datasets to derive actionable insights. 5. Operations Analyst: SQL is useful for managing and analyzing operational data to improve efficiency and optimize processes. 6. Market Analyst: SQL enables the analysis of market data, trends, and customer insights, aiding in strategic decision-making. 7. Research Analyst: SQL helps in gathering and analyzing data from various sources to support research projects and reports. 8. Database Administrator: SQL is fundamental for managing databases, performing queries, and ensuring data integrity and security. 9. Financial Analyst: SQL is used to extract financial data, perform complex calculations, and generate financial reports for analysis. #dataanalyst #datascience #dataanalytics #sql #data

There are numerous data analyst job titles. Examples of data analyst job titles. Kindly follow my hashtag #DataTipsAndTricksWithChidubem for tips. #DataTipsAndTricksWithChidubem #DataAnalyst #DataAnalytics #BusinessIntelligence #MicrosoftExcel #MicrosoftPowerBI #SQL

Ever wondered 😕 is it even worth hiring a data analyst ⁉ 🤔 👨💻 Whether it's even suitable for your business type❓ What does a data analyst even do ‼ 👩💻 This short article will answer all your questions. https://lnkd.in/er7P8WQZ #recruitment #jobfair #jobopportunity #hiringalert #hiringnow #datascience #dataanalytics #dataanalyst #dataanalystjobs #hope4recruitment #manchesterjobs #london #londonjobs #graduatejobs #recruitmentagency #recruitmentagencyuk #blog

Data analyst Transitioners!🤗 We hope you find this helpful. #tech #techcareer #techtip #transitionschool #techtransition #data #portfolio #career #dataanalysis #dataanalytics #ukjobs #jobs #techjob

MNC SQL interview questions for data analyst& bussiness analyst ! 💼 1. Explain Order of Execution of SQL query 2. Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( 💡 majority struggle ) 3. Write a query to find the cumulative sum/Running Total 4. Find the Most selling product by sales/ highest Salary of employees 5. Write a query to find the 2nd/nth highest Salary of employees 6. Difference between union vs union all 7. Identify if there any duplicates in a table 8. Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question 9. LAG, write a query to find all those records where the transaction value is greater then previous transaction value 10. Rank vs Dense Rank, query to find the 2nd highest Salary of employee ( Ideal soln should handle ties) 11. Write a query to find the Running Difference (Ideal sol'n using windows function) 12. Write a query to display year on year/month on month growth 13. Write a query to find rolling average of daily sign-ups 14. Write a query to find the running difference using self join (helps in understanding the logical approach, ideally this question is solved via windows function) 15. Write a query to find the cumulative sum using self join (helps in understanding the logical approach, ideally this question is solved via windows function) 16. Optimize a query to find the latest transaction for each customer 17. Explore the benefits of CTEs (Common Table Expressions) in a real-world scenario "Please share your interview questions in the comments section." ➡️𝐉𝐨𝐛 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐫𝐞𝐟𝐞𝐫𝐫𝐚𝐥 : https://lnkd.in/gcw-ziZm MADHU THANGELLA connect with me for more updates ! #dataanalyst #bussinessanalyst

The top 3 SQL skills I look for when interviewing Data Analyst candidates: 1. CTEs. Subqueries are a problem. CTEs aren't hard to learn, but I really like to see candidates who already know & use them. 2. Window functions. This is table stakes for most Data Analyst roles now. You need to know how & when to use window functions. If you don't, you're getting leapfrogged by candidates who do. 3. Everything beyond the correct query results. I love when candidates talk about - scalability - readability - data quality - maintainability Writing a query to get the correct result is the easy part. There's so much more to writing good SQL though. What other SQL knowledge is must-have for analytics these days? #data #analytics #sql