How do I start learning Python for Data Science?

06-Aug-2021

Python is a very high-level programming language. It is easy to learn and easy to use. It supports most of the paradigms namely functional, Object Oriented, procedural, and imperative. If you are new to Python then don’t worry, we are going to share the overview of it in this post and try to make you familiar with it. We are also going to make you aware that increasingly it is being accepted as a tool for Data Science.

If you want to start a career in Data Science then you can start it in Data Science with a Python certification course and there will be no looking back. Python makes data science a pleasant experience and Python as a tool is literally indispensable for Data Science. There are other options too, however, Python is the most popular one. 

So, dive into the post to learn more about Python for Data Science.

What are Python programming language paradigms?

Before we go all out for Python you should know a bit about programming languages and paradigms a bit. There are many programming languages in the world which are nice and dependable. However, Python is a language that supports a maximum number of paradigms.

Paradigm referred to here is nothing but a style of a programming language. The following are some of the most common programming paradigms used in the world:

Declarative

In this paradigm, the focus is not on ‘how’ but 'what you want to achieve. The control of the flow is not controlled by the programmer instead the outcome of a logic describes the flow. Python can be used under this paradigm, however, it is not strictly a declarative paradigm language.

Imperative

The imperative is a paradigm that is the reverse of the above paradigm. In this paradigm, code is written in a fashion that changes the state of the machine as per the instructions. In this style of coding, the emphasis is on ‘how’ and not ‘what’. As it is one of the oldest paradigms, Python and many other programming languages support it.

Structured

A structured or modular approach is followed in this paradigm. The code is broken into modules and then the modules are processed as per the need. In this style, the focus is on the process and not on data. This is somewhat close to the imperative paradigm and Python can handle this too

Functional

The functional paradigm has its roots in the declarative programming paradigm. In this paradigm, the coding is done in a way that resembles a complex mathematical function. A set of values are returned after a set of input parameters are provided to the function. This is a prominent paradigm in use for machine learning, speech modeling, etc. and Python does support this paradigm massively. 

Object-Oriented or OO

This paradigm is highly popular. Various object variables are created and the required data is fed into these objects and then these objects can be called upon to execute the purpose of the code. It is data-centric and the objects contain the necessary methods and with the help of these methods, the data is executed. OO is as well a part of the imperative paradigm. Python supports this paradigm as well.

How multiple paradigms help a Python programming language?

As a coder or programmer to make the most out of a language you need the language to be more flexible so that you can program for multiple problems of the real world. It gives more power to you as a programmer to execute the job at hand and get fruitful results. 

Python supports, as stated earlier, as many as four paradigms and is ideal for many applications. You can not only do Data Science but also design many other applications. 

Python is a multi-purpose language and you can blame it on the fact that it supports multiple paradigms. As far as examples are concerned you can code in procedural programs for areas such as scientific and engineering fields. A client-server application or a real-time application would be an example of the OO paradigm. 

If a program does not support multiple paradigms then you will be restricted to code for a single or fewer domain of real-world problems. And it will limit you. However, with Python, you can do a lot.

How Python is used in data science?

Data Science is a new field of science where it is used to get meaningful insights from data in the form of information. There are tons and tons of data and we cannot decipher too much out of the raw data. It is Data Science that helps us use the raw data, process it, and predict or describe it in an understandable format. You can for starters get a data science certification to understand it in depth. In a nutshell, we will see how the duo of Python and Data Science complement each other.

Python is an important tool to get things done. The data is collected by various means and then cleaned for usage using this programming language. You can do it using other languages too but the ease and power at the same time make Python the preferred choice for Data Scientists. 

The various broad steps in Data Science are:

• Data collection 
• Data cleaning
• Data exploration
• Analysis and visualization using Python
• Modeling using Python
• Obtaining results

In the above steps right from data cleaning to getting results you will need Python. A python is an excellent tool for visualization. You can have complex graphs in multiple layers and colors to visualize your data. It not only has complicated graphs but also it can be used for simpler and basic graphs as well making it a truly versatile language. 

The accuracy of your modeling also depends upon the language you are using as a tool in Data Science. Python is a highly accurate language and that makes it highly popular among data scientists. If you model it right with correct dependent and independent variables placed rightly then bleak are the chances for Python to throw garbage. But then that is where one has to excel as far as this branch of science is concerned. 

The power of the language is both in accuracy and speed. It can do complex modeling in no time. Python is a free language and it is supported by a whole community of people who continuously monitor and upgrade it as per the guidelines of the community. We are going to know how you can start learning Python for Data Science course in the upcoming section.

Where can I learn Python for data science?

Learning Python is relatively easy, as compared to other languages. Blame it on indentation and whitespaces which leads to easy to read and maintain the code. 

The initial step would be to find a source for getting know-how in data science with a python certification course, with this course you can have the best of Data Science as well as an understanding of Python from the perspective of usage under this field.

The various steps to learn it are in the following:

Learn the fundamentals of Python

Go for a textbook for Python and Data Science and start reading and exercising it. When you have a good understanding join a community such as Kaggle. Doing so will let you be with like-minded people and also increase the chances of exposure to industry-level projects.

Make small projects in Python

Hands-on learning is the most vital part of any field of science. Without a practical implementation or in other words with only theoretical knowledge it is not going to work out in any field of science and engineering.

Understand the libraries of Python

Libraries are an integral part of Python. If you do not master these libraries then you can’t do data science by using Python. You can master Pandas, Matplotlib, and NumPy, the three most common and highly used libraries in Python.

Build up a Portfolio in Python

Go for data wrangling, visualization, and machine learning projects. A good portfolio will help you immensely in the long run. It will be helpful in your current scope too. 

Master algorithms

You should be knowing as many algorithms as possible. After the proper visualization and analysis, you should be aware of which algorithm to run to model it using Python.

By doing the above steps right in Python you will be able to master the language and get your hands strong in its use in data science.

A Data Science with Python course can help you immensely in getting all the above steps right. 

Python is a vast programming language. They have a huge community of people worldwide who contribute to it and help it grow. You can start using Python for free. Get the IDLE editor and start coding for free as well. In the starting go for simple codes and projects just to start understanding the language. Slowly increase the complexity of your programs. And yes, have the paradigms in your mind while coding.

You need to be clear with the functional paradigm to understand the implementation of Python in Data Science. And if you are through with it then it should be easier for you to master it.

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