Software Companies to work in Magdeburg

Magdeburg is being the Capital city of Saxony-anhalt with two Universities namely  Otto-Von-Guericke Universitat and Magdeburg Stendal University of Applied science.
     Due to the presence of the universities, many companies located surrounded by University and other parts of the city. Two major research Institutes SAP UCC and Fraunhofer.

Below are the few companies where students can get internships, student jobs and permanent jobs. But most of the companies ask for German proficiency so it is better to learn German language at least A2 .

• Cosomo Consulting
• Quinsol AG
• Fraunhfer
• icubic
• SAP UCC
• SciFlow
• jobboerse

Best places to visit in Croatia

Croatia  is one of the famous country in  Central Europe to visit because of its coast line , Zagreb is the Capital city of Croatia. Below are the wonderful places to visit, one who visits Europe should never miss these places. Split and Dubrovnik are the well known places in Game of Thrones.

1. Zagreb
2. Zadar
3. Split
4. Dubrovnik
5. Splitvice national park

If you are visiting Zadar and if you are brave enough try for Bungy jump which is 65 Kilometers from Zadar and it cost 80 Euros.

Also, in Dubrovnik Scuba diving will be awesome experience if you are interested and it cost 67 Euros.

Frequently asked SQL Queries

1. Select the second largest salary from the employee table.

> select max(salary) from employee
 where salary <  (select max(salary) from employee);

Frequently asked Top 10 Unix interview questions

1. List 10 commands that you use daily in your work?
   1. ps -ef : To check the process status
   2. top -c : to check memory and CPU details
   3. df -h : To check the mount point details
   4. sed :  string editor
       options: n -> To edit particular line 'n'
                     'nq' -> To quit after reading 'n' number of lines.
                     'np' -> To display n lines
                       5. scp : Secured copy between two hosts
   6. grep : string matching
       options: i -> To ignore case
                     v -> To reverse the matching
                     e -> Extended grep, to give more options
                     n -> display line numbers
                     c -> Count lines containing pattern
                     l -> Display file name
                     f -> Taking pattern from file
   7. du -hs : To check the disk usage of files and directories.
   8. gzip/gunzip : Top zip the files or directories
   9. netstat : to check the network statistics
  10. iostat : To check disk usage

2. How run a job in the background and how to bring it to foreground?
    To run job in background:jobname &
    To list all the jobs running in background: jobs
    To bring job from background to foreground: fg job_name/job_number

3. How to kill a process?
    kill -9 job_id
4. How to search for a string in vi editor?
     /string and press 'n' for next
5. how to create a child process?
    fork(); it creates one child process so total two processes will be running, parent and child.

6.  How to create an empty file or a file with 0kb?
     touch file_name

7. How    

Silhouette Coefficient for K-means cluster using R Programming

1.   Import the dataset from file .csv or .txt into Data frame

           >(dataframe <-  read.csv("F:\\mydata\\data.csv"))

            Above command imports the dataset into data frame, which can be used for clustering or classification etc.

2. Ignore the first column if it contains the headings

>s0  <- shipment1[-1,] 
or while reading the file, we can choose an option to choose header or not by giving header option to true or false



>read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)

3. Convert all the character data types into numeric data

         >library( taRifx )

        >s00 <- japply( s0, which(sapply(s0, class)=="character"), as.numeric )

4. Perform the K-Means clustering for K=2……n

        >library(“cluster”)

       >s0_cluster <- kmeans(s00,3,20)

Here, First argument is data frame, second if the number of clusters and third is the number of iterations.

5. Find the Silhouette Co-efficient

       >sil <- silhouette(s0_cluster$cluster, dist(s000))

Here, S0_cluster$cluster is the cluster label, and second argument dist(s00) is the distance matrix for the dataset.

6. Plot the graph using the plot function

      

      >plot(sil, main ="Silhouette plot - K-means")

      Below is the graph for the plot.