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SQL Query: 3 Methods for Calculating Cumulative SUM

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SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

Data analysis report these are example queries to use on final data

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ApplyAnalytics@twitter The role of data analysis will come into picture, once you have cleaned and filter the raw unstructured data. The next stage is called analysis. Your success of data analysis project is based preparing highly informative final report. Tip:  What could you investigate with data To prepare analysis report, you need to ask some intelligent questions. These are example questions you can use. Based on your questions, you  need to prepare SQL queries to get the desired report or dashboard from your final data or cleaned data.  The report or dashboard should be such that it should improve client business. Let us use some case study on world bank data, what are the questions come into mind:  How much (in USD) is spent on healthcare in total in each country?  How much (in USD) is spent per capita in each country?  In which country is the most spent per person?  In which country is the least spent?  What is the average for each