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Showing posts with the label genai data analytics

SQL Queries Code Interviews QA 50 plus

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Here are SQL-focused interview questions with only the relevant SQL code: 1. Find the second highest salary from an Employee table. SELECT MAX(Salary) AS SecondHighestSalary FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees); Using ROW_NUMBER(): WITH RankedSalaries AS (   SELECT Salary, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS Rank   FROM Employees ) SELECT Salary AS SecondHighestSalary FROM RankedSalaries WHERE Rank = 2; --- 2. Write a query to calculate a running total of sales. SELECT   OrderID,   OrderDate,   Amount,   SUM(Amount) OVER (ORDER BY OrderDate) AS RunningTotal FROM Orders; --- 3. Retrieve customers who placed no orders using a LEFT JOIN. SELECT c.CustomerID, c.CustomerName FROM Customers c LEFT JOIN Orders o ON c.CustomerID = o.CustomerID WHERE o.OrderID IS NULL; --- 4. Write a query to find the top 3 highest salaries. SELECT DISTINCT Salary FROM Employees ORDER BY Salary DESC LIMIT 3; Using DENSE_RANK(): WIT...
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SpaceTech Insurance Dataset Creation and Agile Workflow Planning

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  Source Code here in kaggle:  SpaceTech_Insurance_Agile_Strories_Dataset V1.0   When we have private agile user stories, we can able to refine the COT / TOT framework related prompt responses more and lot refined and easier eye catching solutions. Concept Overview: SpaceTech Insurance Dataset Creation and Agile Workflow Planning Business Context SpaceTech insurance is an emerging field addressing risks associated with space exploration, satellite launches, and orbital operations. Insurance companies require robust datasets and workflows to assess risks, underwrite policies, manage claims, and ensure regulatory compliance. By creating an agile dataset and workflow planning approach, insurers can streamline operations, improve decision-making, and adapt to the dynamic nature of the SpaceTech industry. Business Research Market Demand: Increased reliance on satellites for communication, GPS, and Earth observation. Growing investments in private space exploration companies li...
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Introduction to genai data analytics and it is usecases

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Introduction to Generative AI for Data Analytics Generative AI (GenAI) is a subset of artificial intelligence that focuses on creating new content, ideas, or insights by leveraging existing data. In the context of data analytics, GenAI can augment traditional methods by generating predictions, insights, or simulated data that improve decision-making, trend analysis, and operational efficiency. Here’s an elaboration with practical examples: --- 1. Data Synthesis for Training and Testing Use Case: Creating synthetic datasets when real-world data is sparse, incomplete, or sensitive. Example: A healthcare analytics company generates synthetic patient records using GenAI to train predictive models without violating privacy laws like HIPAA. The synthesized data mimics real-world scenarios and maintains statistical fidelity to actual patient data. --- 2. Predictive Analytics Use Case: Forecasting trends or events based on historical data patterns. Example: In retail, GenAI predicts future dem...
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Introduction - GenAI Data Analytics using Python

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Introduction to Python and Data Analytics: Details This module provides a foundation in Python programming and introduces participants to the field of data analytics. It's designed for beginners with no prior experience in Python or analytics. --- Objective Familiarize participants with Python programming basics. Introduce the core concepts of data analytics. Enable participants to explore, clean, and prepare datasets. --- Content Outline 1. What is Data Analytics? What is Data Analytics? Data analytics is the process of examining raw data to identify trends, patterns, and insights to aid in decision-making. It involves using statistical techniques, tools, and software to transform data into actionable knowledge. --- Key Components of Data Analytics 1. Data Collection Gathering data from various sources like databases, APIs, sensors, and surveys. 2. Data Cleaning Preparing and correcting the data for analysis by handling missing values, removing duplicates, and correcting inconsist...
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