Data Science and Big data with Python

In today’s tech world terms like data science, data mining, big data, analytics, machine learning are quite familiar but they are at times seems to be intangible. However, it is not so difficult to understand the realm of data science and fields associated with this.

Data science is a huge area covering data collection, sorting and cleaning to analysis, visualization and presentation. Data science could be applied to analyse language, find out new products performance from a customer or for predictive analysis. Big data essentially means handling data a very large scale. Big data which requires more than a single computer to store normally uses big data parsing libraries and analytics.

Here comes the value of a programming language like Python who has become one of the most popular programming languages in the field of data science. Python’s growing popularity could be seen from its data science libraries. Here are a few of them.


Developed by data scientists with knowledge of R and Python, Pandas has become one of the most popular data science libraries. It now has a large community of scientists and analysts. Its popularity has grown mainly due to its array of built-in-features like the capacity to read data from multiple sources, and to create large matrixes or tables (data frames) extracting data from these sources and finally present an aggregated analysis based on the questions asked.


Agate is a much newer data library developed with an aim to enhance journalism. It can analyse and compare spreadsheets. It can quickly run some statistics on a database. Agate learns quickly and has a lesser amount of dependencies than Pandas and has some really nice features to do charting and viewing which comes in handy for presentation.


Bokeh is the best tool to create visualizations of the completed dataset. It is compatible with Agate, Pandas and other data libraries and pure Python. Bokeh helps to create stunning charts, graphs and visualization of every type without much coding.

Using Map-Reduce when working with Big data

MapReduce is a technique which permits you to map the data applying a particular attribute or filter and then reduce those data by using either a transformation or aggregation technique. Almost all the data science libraries have some default MapReduce functionality. Python can communicate to these services and software and pull out the results for advanced reporting and presentation.


Apache’s Hadoop is the most used libraries for MapReduce when working with large datasets. Hadoop applies cluster computing for a much faster data processing of large data sets.

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