Python for data analysis - the obvious choice

Python, as we all know, is a general-purpose programming language that is fast becoming more and more popular for doing data science. Companies worldwide are using Python to generate insights from their data in order to get a competitive edge.

According to a 2013 survey, 40 percent of respondent data scientists are using Python in their day-to-day work as well as many other programmers in all fields who have made Python one of the top ten most popular programming languages in the world. These include, among others, mammoths like Google, NASA, and CERN.

Python is relatively easy to learn due to its inherent readability and simplicity. It also provides a huge number of dedicated libraries currently available for almost any field. (72,000 of them in the Python Package Index (PyPI)). This fact explains the popularity of Python for data analysis among data scientists. 

It is of course a free, open-source software. This means that almost anyone can write a library package in order to extend its functionality. Talking about Python data science has been an early user of these libraries, particularly Pandas.

Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. It is used for everything from importing data from spreadsheets to processing weather data. Pandas’ powerful dataframes put almost every common data manipulation at your disposal. 

When considering Python data science is not the only field to benefit from the libraries, as can be seen from the (random) packages list below:

• SciPy offers tools and techniques for analysis of scientific data.
• Statsmodels focuses on tools for statistical analysis.
• Scilkit-Learn and PyBrain are machine learning libraries that provide modules for building neural networks and data preprocessing.
• SymPy – for statistical applications
• Shogun, PyLearn2 and PyMC – for machine learning
• Bokeh, d3py, ggplot, matplotlib, Plotly, prettyplotlib, and seaborn – for plotting and visualization
• csvkit, PyTables, SQLite3 – for storage and data formatting

Everywhere you hear people talk about big data, AI, machine learning. These are the buzzwords of 2017 and probably 2018 too. They all depend on efficient handling, mining and manipulating data.  Therefore python for data analysis is the obvious choice;  taking a python training course is the indisputable way.

Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data.

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