Python, Data analysis and Data science.

What is python?

In the world of evolving technology, a simple question must be answered simply. Python is a general purpose language, i.e. its a language that can be used to solve a variety of problems easily. Python is a high level language, which means, it allows users to write programs with independance with a type of computer and it is also user-friendly. Python is multi paradigm, in essence, it supports various types of programming style based of any particular model of computation.these multi-paradigm styles include procedural, object-oriented and functional programming.

Released in 1991 by Guido van Rossum, Python is designed so that it is easily readable by the user, as its language constructs and Object Oriented approach allows user to write clear and indented programs for big projects.

Other than being general-purpose, high-level and multi-paradigm language, python includes other different features:

1. Integratable:

The data values from languages like C, C++, Java etc. can be use in the application of Python.

2. Graphical User Interface Support:

Python can be used to develop graphical user interfaces.

3. Standard library:

Python comes with a large standard library which allows users to choose from a broad range of modules and add functionality as per the precise need of the user.

4. Extensibility:

Python can use languages like C and C++ for their compilers and run time environments.

5. Multi Platform Compatible:

Python has the ability to run on different operating platforms such as Windows, Linux, Unix, and Macintosh.

With all these features and more, Python packages include wide range of functionalities like Graphical user interfaces, Web frameworks, Multimedia, Databases, Test frameworks, Automation, Web scraping, Documentation, System administration, Scientific computing, Text processing, Image processing.

What is data analysis?

As wikipedia says, "Data analysis is a process of inspecting and modeling data with the aim of discovering useful information, conclusions, and decision-making supports. Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data are collected and analyzed to answer questions, test hypotheses or disprove theories."

What is data science?

According to wikipedia, "Data science is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science."

Data Analysis vs Data Science

Both data science and data analysis significantly overlap, but they are also differentiating and unique on their own.

The biggest difference is that, data analyst curates useful comprehensions from known data. They handle the daily occurring data to answer factual questions to be answered.

While data scientist deals more with the theoreticals, they try and predict the future statistics and frame these predictions in new questions.

In the situations where the lines between Data scientists and Data analysts get blurred, the advantages that Python bestows on data science can also be enjoyed by data analysis. As a similarity, both require a knowledge of software engineering, competent communication skills, basic math knowledge, and an understanding of algorithms. Also, both require knowledge of programming languages such as R, SQL, and Python.

Why is Python required for data analysis?

1.Flexibility

You want to try something new and creative that’s never done before? Then Python is the best for it. It’s ideal for developers who script apps and websites.

2.Learnable

Since Python focuses on simplicity and readability, it has a gradually and relatively low learning curve. It makes Python an ideal tool for beginner programmers as Python offers of using lesser lines of code to accomplish tasks than one needs when using other languages.

3.Open Source

Python is open source, that is, it’s free. Python uses a community-based model for development. Python is designed to be portable and also run on multiple environments such as Windows and Linux. There are many open-source Python libraries that can just be used without much fuss such as Data manipulation, Data Visualization, Statistics, Mathematics, Machine Learning, and Natural Language Processing.

4.Well-Supported

Python is largely used in academic and industrial areas, and hence it means that there are many of useful analytic libraries available. And whenever Python users need help can always turn to Stack Overflow, mailing lists, and user-contributed code and documentation. And the popularity that Python gains, more users contribute information on their own user experience, therefore more support material is available free of cost.

This creates a self maintaining spiral of acceptance by a growing number of data analysts and data scientists and so its popularity increases even more.

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