Give this my spark pc version 
“C:\Users\PCS>echo %SPARK_HOME%
C:\Program Files\spark\spark-3.5.0-bin-hadoop3\spark-3.5.0-bin-hadoop3
“ repeat this tutor based on this version oky 
Setting up Spark in Scala with VS Code (Step-by-Step)
This guide details setting up a Spark development environment using Scala in VS Code. We'll use sbt as the build tool and focus on a simplified local setup for learning and experimentation.
1. Install Prerequisites:
Java Development Kit (JDK): Ensure you have a JDK 8 or 11 installed. Spark 3 can run with Java 17, but there might be dependency conflicts, so sticking with 8 or 11 is generally safer. Verify with java -version.
Scala: Download and install Scala from https://www.scala-lang.org/download/.
sbt (Scala Build Tool): Install sbt from https://www.scala-sbt.org/download.html.
Apache Spark: Download a pre-built Spark distribution from https://spark.apache.org/downloads.html. Choose a version compatible with your Hadoop distribution if you plan to use one. Extract the archive to a convenient location (e.g., ~/spark).
VS Code: Download and install VS Code from https://code.visualstudio.com/.
2. VS Code Extensions:
Metals: This extension provides Scala language support, including code completion, debugging, and more. Install it from the VS Code extensions marketplace.
3. Project Setup:
Create a new directory: Create a directory for your Spark project (e.g., my-spark-project).
Initialize sbt: Navigate to the project directory in your terminal and run sbt new scala/scala-seed.g8. This creates a basic Scala project structure.
Rename the project: Rename the hello directory inside your project to something more descriptive (e.g., my-spark-app).
Update build.sbt: Open the build.sbt file and add the Spark dependency:
     name := "my-spark-app"

version := "0.1.0-SNAPSHOT"

scalaVersion := "2.13.8" // Or a compatible version

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "3.2.1" % "provided", // Use the Spark version you downloaded
  "org.apache.spark" %% "spark-sql" % "3.2.1" % "provided"  // Add other Spark modules as needed
)
   
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The provided scope ensures that Spark jars are not packaged with your application since they will be available on the cluster.
4. Write Spark Code:
Open the src/main/scala/Main.scala file (or create a new Scala file within the src/main/scala directory of your project if it doesn't exist). Replace the existing code with your Spark code. Example:
     import org.apache.spark.sql.SparkSession

object Main {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder
      .appName("My Spark Application")
      .master("local[*]") // Run locally with all available cores
      .getOrCreate()

    val data = Seq(1, 2, 3, 4, 5)
    val distData = spark.sparkContext.parallelize(data)

    val sum = distData.reduce(_ + _)
    println(s"Sum of numbers: $sum")

    spark.stop()
  }
}
   
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5. Run Your Spark Application:
Open your project in VS Code.
Open a terminal within VS Code.
Import the build into Metals: Run the command sbt metalsEnable in the integrated terminal. Wait for Metals to finish indexing.
Run your application from sbt: In the integrated terminal, run sbt "runMain Main" (replace Main with your object name if different).
6. Troubleshooting:
Dependency Conflicts: If you encounter dependency issues, check your Spark and Scala versions for compatibility.
SPARK_HOME: If Spark is not found, you might need to set the SPARK_HOME environment variable to the directory where you extracted Spark. You can do this within sbt in your build.sbt file:
     envVars := Map("SPARK_HOME" -> "/path/to/your/spark")  // Replace with your actual Spark path
   
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WINUTILS.EXE (Windows): If you're on Windows and encounter errors related to winutils.exe, you'll need to download winutils.exe for your Hadoop version and place it in a bin directory within a Hadoop installation directory. Then, set the HADOOP_HOME environment variable accordingly.
This setup will allow you to develop and run basic Spark applications locally. For more advanced scenarios like connecting to a cluster, you'll need to configure your application accordingly. Remember to consult the Spark documentation for details specific to your environment and use case.