Introduction:

What comes across your mind when you hear the term Data Science? It is Python. One of the most common terms simultaneously used with Data Science. Python is one of the top-notch and widely used programming languages that gained popularity in recent years.

But what makes python so popular in the IT development sector and technology?

Python is an open-source and high-level language that is easy to interpret. Today, Python could be seen as one of the widely accepted languages in the field of Data Science. Python has various libraries and functions, which makes it easy to work with scientific, mathematics, and statistics functionalities.

Moreover, the reason why Python is one of the most preferred languages by data scientists for various data science-related projects/applications is that anyone without any coding background can learn and grasp this language in a very small period.

Provided that most of the major companies are looking for expert Python programmers who can work in various Scientific Research and development and other IT-based sectors, AI and ML technologies, developing websites, software components, etc. Considering the tremendous growth of AI and ML technologies, the demand for python programmers is insanely increasing day by day.

Data science has recently evolved as one of the most desired technology, with exposure to tremendous top-tier job opportunities that forecast future advancement. It is the domain of study comprehended of maths, statistics, specialized programming skills, and emerging technologies like Artificial Intelligence and Machine Learning to generate the hidden insights of an organization intending to procure relevant information and subsequently make decisions for the organization.

Major topics of python:

Now there are various concepts of Python programming language that you need to be familiar with before you jump into the world of Data Science. These are as follows:

Python Fundamentals:

It comprises basic knowledge of Python installation, knowing about constants, variables, identifiers, and keywords, writing codes in Python programming language regardless of the Python IDE software used, and executing Python programs.

Python Functions:

A function is a block of organized code that runs when called upon; It contains parameters, also called arguments, which are the information passed into it and returns the data as a result; This information is contained inside the parenthesis.

We use def keyword to define a function.

Consider the following example where we first define a function and then call it:

The output obtained for the above code looks like this:

Mentioned below are a few types of Python functions that are majorly required for Data Science discussed below:

Zip () Function:

Python's zip() function creates an iterator after taking two or more Iterables or containers which returns a single iterator object; This object is also called a zip object.

If iterators passed into the function have different lengths, then the length of the new iterator created is decided by the remaining items contained in the earlier iterator.

Enumerate() Function:

The Enumerate function in Python is used to assign or pair index or position values to the values in an iterable (remember, index values start at 0).

Once those index values are paired with the iterable values, you can decide whether to turn it into a dictionary where the index values will now serve as a key for the values in the iterable.

Counter () Function:

The counter () function is used to count objects that occur in a given data set or data source. This counter () is an integer variable whose value is initially zero. The object produced is stored in the form of a dictionary.

Range () Function:

The Python range() function is used to return an immutable sequence of numbers with an initial default value of 0t, increments by 1, and ends at a specific number.

User-defined () Function:

Functions that we define ourselves to do a particular specific task are referred to as user-defined functions. These functions help us to decompose a large program into small segments, which makes the program easy to understand, maintain and debug.

Python Operators:

Python Operators are used to perform operations on variables and values initiated in a program. There are various types of python operators in python listed below:

• Arithmetic operators
• Assignment operators
• Comparison operators
• Logical operators
• Identity operators
• Bitwise operators

Strings:

A string in Python is a sequence of characters surrounded by either single quotes or double quotes or triple quotes. Since computers do not understand characters, they store characters encoded in ASCII characters and manipulated as a mixture of 0s and 1s. The quotes enclosing the characters allow us to create a string.

Given below is the syntax for creating and printing a string in Python:

Consider the following example:

Output obtained:

Classes & Objects:

Every variable in Python is an object of a type. And since the variables are dynamically stored in Python frameworks and libraries, therefore, we do not use data types for declaration. This is what makes Python a dynamic and highly interpreted language. Moreover, there is no compilation step required in Python as the debugging & development processes in Python are very fast.

A Class is a group of objects that share common characteristics and properties. An object itself can be made a user-defined data type with the help of a class.

The syntax for defining a class is given below:

Consider the following example of a class definition:

An object is an identifiable and basic run-time entity that contains its characteristics. Each object is associated with the type of class within which it is created. Objects are the variables of class type and hence, this type is what explains the object.

Consider the following example where an object is created to access the attributes of the class:

The output obtained is:

Positional & Keyword arguments:

While studying Python functions, we commonly refer to some programming words like arguments and parameters.

Arguments are the variables passed to the function when a function is called upon. Whereas the parameters are those variables used at the time of function definition. In other words, we can also say that the values that parameters contain while defining a function are called arguments.

Depending upon the criteria for passing the arguments to the parameters, there are two types of arguments:

• Positional arguments: Arguments passed in order one after the other. They are declared by the name itself.
• Keyword arguments: Arguments passed after being assigned to the parameters irrespective of the order of passing. They are declared by a name and a value.

During a function call in Python, Keyword arguments are supposed to follow the positional arguments, otherwise, Python raises an error.

Lists:

A List in Python is a built-in data structure used to store the sequence of several types of data. in Python. The data is represented in square brackets to declare a list.

Lists are ordered and the values in lists are mutable, which means that the values can be changed anytime and then updated, and hence, is one of the reasons why Lists are so commonly used and preferred.

Consider the following example of a list:

The output of the above list printed looks like this:

Tuples:

A Tuple in Python, unlike Lists, stores the collection of immutable Python objects, which means that the values once entered at the time of declaration cannot be changed at later stages. The values in Tuple are separated by a comma and enclosed in parenthesis.

The output obtained:

Dictionaries:

A Dictionary in Python is used to store an unordered sequence of data in a key-value pair format. The data entered is of mutable structure like Lists and is defined into keys & values. The statements given below highlight the syntax of declaring a dictionary.

Here, the keys are immutable Python objects like integers, strings, or tuples, which must be unique and comprise only a single element. Whereas, Values can be of any type of Python object like lists, tuples, integers, strings, etc.

Let us consider an example by creating a dictionary file and printing its values:

That is how the output looks:

List Comprehension:

List comprehension is a distinguishing feature in Python which allows us to write complex codes in a single code itself. Moreover, this tool simplifies the code containing for and if loops to be more easily readable with a sophisticated syntax.

All the major topics of python required for Data Science have been briefly discussed above. However, some other Python-based technologies need to be separately studied before you step into the world of Data Science. These are as follows:

• Machine Learning
• Deep Learning

Python Frameworks required:

The four most-important Python libraries are NumPy, Pandas, Matplotlib, and Scikit-learn. They have been briefly discussed below:

NumPy

Numpy is a popular Python library specifically developed to handle a variety of mathematical and statistical operations. Many features of the Pandas library are based on this library.

Pandas

Pandas is another popular Python library designed precisely to work with data and its architecture. Various types of Data Science projects are performed involving this Python library.

Matplotlib

Matplotlib is a data visualization and plotting library which can easily generate object-oriented charts and graphs from the data initially provided.

Scikit-learn

Scikit-Learn is widely recognized for its usage in machine learning-based projects and tasks.

Use of python in Data Analytics:

While analyzing data, we need a comparatively strong command of programming as in businesses, the amount of data is extremely heavy, which Excel itself can't handle. Therefore, we need Python for data analytics as it has in-build libraries which help to manage and create data structures very easily and quickly.

Kockpit is an emerging data analytics company that provides Microsoft Power Bi consulting services and Business intelligence solutions to various companies on different platforms. They analyze their insights and produce hidden patterns and result-based strategies.

Conclusion:

In this blog, we have discussed why Python is an important tool for data science and the major topics to be studied before digging into Data Science.

Thank you for reading, and kindly share your feedback.