PHP vs PYTHON ?

What Should I Learn ?

A Beginner Guide for opting the first programming language

PHP and Python both are popular programming languages used for web development. PHP vs Python battle has become an exciting one, both have strong reasons to favor their language against other one. Most of the people are opting Python over PHP.


Below listed out few key features of both languages :

• Python is a object oriented programming language used for rapid applications development. PHP is a Hypertext Pre Processor used to develop web applications or dynamic websites.

• Python will be used in data science, AI etc PHP is used in web application development

• PHP has less number of Frameworks whereas PHP has many Frameworks

• Python syntax of code is clear and concise whereas PHP has wide range of syntax

• Useful features of Python are Rapid Development, Dynamic Typing and simple code whereas PHP features are Open Source, Easy Deployment and Continual Improvements.

• Coming to Salaries of both tools : The average salary of Python Developer is $130,025 per year in US whereas average salary of PHP Developer is $90,000 per year in US

Some things that both the languages have in common :

• PHP and Python are easy to learn

• Both languages are accompanied by extensive and detailed documentation

• Both languages are open source

Which One is Better ?

My Opinion :Choosing one language among PHP and Python is difficult task because both of them are good on their way.

Without any doubt, Python is very easy to learn. Python can assure you a vast range of career opportunities but with PHP you can stuck on a specific domain. But using of PHP for backend is easy when compared with Python. But Python is easy programming language. Python provides powerful debugger compared to PHP. Both the languages are open source and free.

Choose your interested one and start working on it. No matter which one you choose, the learning process is not easy, practice with proper assistance. Finding proper assistance is difficult. Due to this reason lot of courses are being taught on major online training platforms like us to help learners to learn quickly and easily.

Are AI, data science and machine learning interrelated?

If yes what should one learn first AI, data science or ML?

Is it really mandatory to learn all these three technologies to impress interviewer?

This kind of confusion and questions that will rises when one take an approach to learn new skills.

Artificial intelligence is all about decision making on available data be it virtual personal assistants, self driving cars or examining medical samples. AI is about doing human tasks faster and with reduced error rate.

Machine Learning is subset of AI which makes applications more accurate in predicting outcomes without having to be specially programmed.

Data Science is not subset of machine learning but uses ML for data analysis and future predictions. Data science gets solutions to various business problems using AI tool.

Data Science is to insights, machine learning is to predictions and AI is to actions.

My suggestion is before you get started with learning AI, master your ML and Data Science skills. This will provide you great career in either of these.

Job openings for AI, ML and Data Science are rising day by day. The average salary for AI professional with 2 to 4 years of experience is 18 to 22 LPA in India, 4 to 8 years of experience is 25 to 50 LPA.

If you are hungry of skills then 2020 is best time to launch your career in AI, ML and Data Science to succeed in today’s data driven world.

SV Soft Solutions instructor led, job oriented AI, ML and Data Science courses that come with certification guaranteed might be good choice for ones career.

Which tools are necessary for Devops Engineer?

The most common DevOps tools are

1. Continuous Integration: Jenkins, Travis, TeamCity

2. Configuration Management: Puppet, Chef, Ansible, CFengine

3. Continuous Inspection: Sonarqube, HP Fortify, Coverity

4. Containerization: Vagrant, Docker

5. Virtualization: Amazon EC2, VMWare, Microsoft Hyper-V


Continuous integration tools are used to automate the testing and feedback process and build a document trail. These are used to immediately identify and correct defects in the code base. Configuration management tools are primarily used for tracking and controlling changes in the software. These extract infrastructure components from the code for automation and maintain the continuous delivery of software. Others tools help in standardizing builds, improve collaboration between developers and sysadmins, or monitor systems.