What are the important topics in Python for data science?


As we all know, python is dominating the science world. It is believed that the reason behind this richness is that it is easy to learn and has a very healthy collection of data science libraries.

Python is a language that is not only used in the data science field but also for web development, Mobile development, and in some cases game development too, no wonder why we just said that python is ruling the science world in technology.

Also, if a person is using python language just only for data sciencewith python certification, it’s not necessary to have expertise in the same, but there are some important topics that one should have a grasp when dealing in data science with python certification.

The concepts given in this essay are not directly linked to the library, as we can only day they are the very basics to this language. For using Pandas, Matplotlib and others, one needs to have a good gold over the Python basics. These libraries think of you as you have a good hold in the python basics.

Now the basics of the Data Science with Python Certification are described below briefly

In Python, functions are considered to be the building blocks of the language therefore it makes it the most important basic to learn for Data Science with Python Certification. Zero or more arguments are taken as the inputs and it gives back a particular value. One can simply create a function using a def keyword.

The function is usually made to prioritize completing one task only, a function solving a number of tasks doesn’t fit in the definition of a proper function.

One can also give functions some describable names so that he or she can know what a function actually does without even knowing the code.

  • Keywords and positional Arguments

The parameters of a function are also specified while describing it. When a function is made, the values needed by the parameters are also pre-defined, the 8values of such parameters are known as the Arguments.

Now then, what are the Positional and the Keyword arguments? The value of the parameters which are described by name only is the Positional Arguments, whereas the Keyword arguments are those which have a name and a defined value too.

When a function is made, it is necessary to give the values of the positional argument, if not done then we will face an error. The value of the keyword argument is not specified, so it picks any default value.

  • Classes

The OOP (object-oriented programming) paradigm is built on the concept of having a particular type as an object in a different type. Tha other words, type is what gives the idea of the object.

Integers, dictionaries, lists, functions, etc are the different types of objects that we see in python. Therefore classes are used to define the kind of object.

  • Lists

A built-in structure that we have in Python is known as Lists. Some collected data points filled inside square brackets represent a List. Any type of data or any mixed form of data can be represented in your list.

They are so commonly used because they are mutable according to the needs. One can easily include and exclude items as per the need. Also mentioning that it is also specified that you can update or change the items in the list.

Other than these *args and *kwargs, dictionaries, list comprehensions, sets are also some basics that one should know about for Data Science with Python Certification.