Fundamentals For Data Crunching

 Data Crunching :    


                                   Data Crunching Compute Image & Photo (Free Trial) | Bigstock
Data Crunching can be defined as by using some tools or algorithms we are transforming the form of data. 

Data Structures : Data Structures is a particular way of sorting and organizing data in a computer so that it can be used efficiently. Some well known data structures are :
  • Array
  • Lists
  • Tree
  • Heap
  • Hashing
  • Graph

Algorithm : Algorithm is a step - by - step procedure for calculations or you can tell it is a sequence of program instructions designed to compute a particular result.

Some of their Applications :
  • Facebook Graph Search
  • Dropbox manage to upload some large files instantly and to save a lot of storage space.
  • Shazam App
  • Cluster Manager etc...
Array :

                C Arrays (With Examples)
 
It is contiguous area of memory consisting of equal size elements indexed by contiguous integers. It has constant time to add/remove an element at  the end and has Linear time to add/remove an element at an arbitrary location. Indexing of array starts from 0 to len(array) -1 where len(array) gives length of array.

Big OH : It is one of the way of measuring time complexity and space complexity. Less will be the space and time complexity of the program , more optimized and efficient your program is.

Example :

1) a = 0
    b = 0
    for i in range(0,n):              -O(n)
         a = a+rand();                 -O(1)
    for j in range(0,m):            -O(m)
         b = b+rand();                 -O(1)

Here in this program the time complexity is O(m+n) 


2) a = 0
    for i in range(0,n):           - O(n)
    for j in range(n,i,-1):        -O(n)
            a = a+i+j
 
Here in this program the time complexity is O(n**2)

3) a = 0
    i = N
    while(i > 0):
          a += i
          i /= 2

Here in this program the time complexity is O(logn) 

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