Python, Data analysis and Data science.

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Python, Data analysis and Data science.

• A guide to what python, data analysis and data
  science is, and how they relate.

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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 on any particular model of
  computation.these multi-paradigm styles include
  procedural, object-oriented and functional
  programming.

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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 used in the application of Python.
2. Graphical User Interface Support
Python can be used to develop graphical user
interfaces.
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.
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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
needs 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 a 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.

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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.

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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.

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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.
  

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Why is Python required for data analysis?

• 1.Flexibility
• You want to try something new and creative
  thats never done before? Then Python is the best
  for it. Its ideal for developers who script apps
  and websites.
• 2.Learnable
• Since Python focuses on simplicity and
  readability, it has gradually and relatively low
  learning curve. It makes Python an ideal tool for
  beginner programmers as Python offers of using
  less lines of code to accomplish tasks than one
  needs when using other languages.

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3.Open Source
Python is open source, that is, its 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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The world of data science is rapidly expanding.
And python being a multi-paradigm language, is
able to handle everything from website designing
to running of embedded systems. With a gist of
python, data analysis and data science anyone can
do it.

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• With regards,
• Technology Moon