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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

Career Opportunities to Write Algorithms

Many participants in the Analytics seminar expressed opportunity in preparing algorithms for predictive analytics.
opportunities

You Need Algorithms Why

Using these algorithms, businesses can make better data-driven decisions by extracting actionable patterns and detailed statistics from large, often cumbersome data sets.

Many business people small to big expecting some kind of algorithms. So that they can save their precious time in predictive analytics.

As per IBM What are Good Benefits of Right Algorithm

  • Transform data into predictive insights to guide front-line decisions and interactions. 
  • Predict what customers want and will do next to increase profitability and retention. 
  • Maximize the productivity of your people, processes and assets. 
  • Detect and prevent threats and fraud before they affect your organization. 
  • Measure the social media impact of your products, services and marketing campaigns. 
  • Perform statistical analysis including regression analysis, cluster analysis and correlation analysis.

Summary

Algorithm making is a step by step process. The key advantages are useful to end users and taking less time in processing of application.

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