Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.

Required Skills for a Data Scientist
Programming: Python, SQL, Scala, Java, R, MATLAB.
Machine Learning: Natural Language Processing, Classification, Clustering, ...
Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries.
Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera.

Data Science Components
Statistics: Statistics is the most critical unit of Data Science basics. ...
Visualization: Visualization technique helps you to access huge amounts of data in easy to understand and digestible visuals.
Machine Learning: ...
Deep Learning: ...
Discovery: ...
Preparation: ...
Model Planning: ...
Model Building:
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Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best language used by data scientist for various data science projects/application. Python provide great functionality to deal with mathematics, statistics and scientific function.

The full-fledged programming nature of Python makes it a perfect fit for implementing algorithms. Its packages rooted for specific data science jobs. Packages like NumPy, SciPy, and pandas produce good results for data analysis jobs.

In short, understanding Python is one of the valuable skills needed for a data science career. Though it hasn't always been, Python is the programming language of choice for data science. ... In 2018, 66% of data scientists reported using Python daily, making it the number one language for analytics professionals.

As it stands now, Python is one of the most widely used programming languages in the field and most of the data scientists use python for data science. This dynamic language is easy to learn and read, so it's an optimal choice for beginners. ... Simply put, the demand for experts with Python skills is on the rise.

Top 13 Python Libraries Every Data science!
NumPy
SciPy
BeautifulSoup
Scrappy
Pandas
Matplotlib
Plotly
Seaborn
Scikit Learn
PyCaret
TensorFlow
Keras
PyTorch
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PHP is a server side scripting language. that is used to develop Static websites or Dynamic websites or Web applications. PHP stands for Hypertext Pre-processor, that earlier stood for Personal Home Pages. PHP scripts can only be interpreted on a server that has PHP installed.

PHP (recursive acronym for PHP: Hypertext Preprocessor ) is a widely-used open source general-purpose scripting language that is especially suited for web development and can be embedded into HTML.

PHP (PHP: Hypertext Preprocessor) is a scripting language that helps people make web pages more interactive by allowing them to do more intelligent, complex things. ... A website programmed with PHP can have pages that are password protected.

PHP is considered a backend scripting language. PHP scripts are usually written on a server that the front-end, or client side code would interact with. Php, Hypertext preprocessor is used for backend.

The best PHP frameworks bellow are ordered by their popularity, technical specifications, and the ability to increase development efficiency:
Laravel. When people talk about PHP frameworks, one of the names that tend to pop up most often is Laravel.
CodeIgniter.
Symfony.
Laminas Project.
Phalcon.
CakePHP.
Yii.
FuelPHP.
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