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One of the most trending Buzz words in IT industry these days is Data Analytics.

“Analytics is the process of obtaining an optimal and realistic decision based on existing data.”

True to our eyes, it has made its place because it’s certainly –

  1. In-demand.

  2. Highly payed skill

  3. Can be called as future of IT industry with its base in every sector we can image about. Since each business wants to improve its decision making capabilities.

  4. Lucrative field.

Analytics is broad term, which at lower levels starts from data to derive some basic information out of it (Descriptive Analysis).  Reports generated on the basis of these results(reports data) is accessed  to bring out the prospective agents of business development (Predictive Analysis) and the probability* of future events based on historical raw data. Now,  using the outcome of later two we can have recommendations(Prescriptive Analysis). This emerging technology enables us to make various models which recommend one or more courses of action — and showing the likely outcome of each decision.

Note * : I hope here you can clearly understand the role of probability in Predictive Analytics. It requires pure mathematics(Stats) with machine learning algorithms.

Hope you got a basic understanding of Data analytics. It just doesn’t end here. We have more classifications and examples.

Please visit the place again for more updates.

For more in-depth study please read the following –

 

Python Notes – Part I

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Build-in types

 

Numeric Types Precision  Internally Literal Creation
integers Have unlimited precision i = 5
floating point System dependent precision values check – sys.float_info implemented using double in C f = 5.5
complex numbers real + img implemented as floating point number  c = 2j

 

  • c = 2j  Yields an imaginary number (a complex number with a zero real part) add to an integer or float to get a complex number with real and imaginary parts.

 

Operations on Numeric Types

 

Operation Result
x + y sum of x and y
x - y difference of x and y
x * y product of x and y
x / y quotient of x and y
x // y floored quotient of x and y
x % y remainder of x / y
-x x negated
+x x unchanged
abs(x) absolute value or magnitude of x
int(x) x converted to integer
float(x) x converted to floating point
complex(re, im) a complex number with real part re, imaginary part im. im defaults to zero.
c.conjugate() conjugate of the complex number c
divmod(x, y) the pair (x // y, x % y)
pow(x, y) x to the power y
x ** y x to the power y

Strings *

  • Methods that use dot notation only work with strings.
  • On the other hand, len() and str() can work on other data types