SQL for Data Analysis

Got an Interview tomorrow? Want to practice SQL & Python online? Applying to jobs every day is great, but remember to practice too! When you get an interview unexpectedly, you'll be well-prepared and ready to shine! Here are a some websites that can help you with practice and cracking the interviews: https://www.linkedin.com/posts/sql-analysts_got-an-interview-tomorrow-want-to-practice-activity-7218187268685983744-W5KC Like for more content like this ❤️

🔟 Data Analyst Project Ideas for Beginners 1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns. 2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics. 3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance. 4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights. 5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations. 6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness. 7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings. 8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings. 9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events. 10. Financial Analysis: Analyze a company’s financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

Best practices for writing SQL queries: 1- Filter Early, Aggregate Late: Apply filtering conditions in the WHERE clause early in the query, and perform aggregations in the HAVING or SELECT clauses as needed. 2- Use table aliases with columns when you are joining multiple tables. 3- Never use select *, always mention list of columns in select clause before deploying the code. 4- Add useful comments wherever you write complex logic. Avoid too many comments. 5- Use joins instead of correlated subqueries when possible for better performance. 6- Create CTEs instead of multiple sub queries, it will make your query easy to read. 7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability. 8- Never use order by in sub queries, It will unnecessary increase runtime. In fact some databases don't even allow you to do that. 9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.

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Essential SQL Topics for Data Analysts - Basic Queries: SELECT, FROM, WHERE clauses. - Sorting and Filtering: ORDER BY, GROUP BY, HAVING. - Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN. - Aggregation Functions: COUNT, SUM, AVG, MIN, MAX. - Subqueries: Embedding queries within queries. - Data Modification: INSERT, UPDATE, DELETE. - Indexes: Optimizing query performance. - Normalization: Ensuring efficient database design. - Views: Creating virtual tables for simplified queries. - Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many. Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include: - ROW_NUMBER(): Assigns a unique number to each row based on a specified order. - RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently. - LAG() and LEAD(): Access data from preceding or following rows within a partition. - SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows. Here you can find essential SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you need more 👍❤️ Hope it helps :)