Why Especially Python for Data Science ?

Python is the choice of information researchers. Currently, Python plays an important role in your daily exercises because it has a wide range of open source libraries and everything is free. The work of data scientists involves many interrelated activities, including:

  • Accessing and converting data
  • Computer statistics
  • Creating visual reports on this data
  • Configuring Predictive and Descriptive Models
  • Estimate models based on additional data
  • Integrating models into production systems

If the data scientist wants to do some temporary analysis of the data, he doesn't write the Java code; Java is too complicated for a data scientist to start programming. It has its own syntax and semantics, and each time it is possible to develop a program, no one can find the required syntax or error. As a consequence, the pig and the hive have been developed, however, in addition we have Python parallel, in which you do not need to compile large amounts of code.
In Python all you need to remember is indentation. Whenever code is written, it is necessary to take care of the gap. If the indentation (spacing) is not correct, the program fails. If you run the "for loop", you will see a few inches inside any "for loop" of the "for loop". All lines of code must have the same indentation or in one line.

 SciPy

 SciPy (pronounced "sigh") is a scientific python that allows for scientific analysis. It is a Python based biosystem of open source software for mathematics, science and engineering. We have all done differentiation, equation and so on. In math, in school and in college. Currently, how is this done on computers? This can be done in Octave as well, but Python provides us with SciPy, which can make these types of operations much easier. On Python Nam, which coordinates some libraries, the Sci Library, Matplotib, iPython, Sympi and Pandas, and each has its own character.

If you have any questions? Mention them in the Observations area, we'll make it clear! ..

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