Archive for July 2015

How to install Python on your Computer? [Tutorial]

Data science is all about making sense of the data that we have. And for that purposes, two widely used languages are Python and R. So let's start with Python!



As every other high level programming language, your machine needs an interpreter to read the code (.py) and understand it. And for us to code (to create the .py file) any text editor would do the job but Python being an indentation-sensitive language, it's better to use some editor that would take care of the indentation part and also highlighting the built-in keywords so that the interface would look great. A software that does this job is called an IDE (integrated development environment) and for python there are many such IDEs.

A typical programmer being lazier than an average human being should always look for one package that has all these - an interpreter, an IDE and much more - so just one click should install everything related to python on your machine and there's such an application package called "Anaconda".




Whether you are running Windows, Linux or Macintosh - Jump in here and download your appropriate package!

Double-click the downloaded Anaconda setup and proceed with installation. You are done once the installation is finished.

Few things to be noted:

1. Anaconda comes with a huge set of Python packages which you primarily require for your data analysis and scientific calculations.
2. Windows & Linux users - You don't need to set the environment path to access python from any directory but Mac users might need to set the path (export PATH=~/anaconda/bin:$PATH)
3. Anaconda has a huge list of FAQs so check them if you have any trouble in getting this work.
4. After installation just open your command prompt or terminal and type spyder and if the spyder IDE opens, you're perfectly done with installation.

Happy pythoning!!!

Sunday, July 19, 2015
Posted by Netbloggy

Hello World!

We, as an average internet user consume a lot of data from the web but the data that we (knowingly) publish online can be relatively NEGLIGIBLE except our daily Facebook posts.  But just imagine that if we are in a universe where everyone is like us - just not caring about online contribution but just consuming data - at some point of time there wouldn't be any new data for us to consume. 

Hence to deviate from the mass crowd and to become an online contributor, here's an attempt (Pushed by my professor from Praxis Business School).

Hello World!

Monday, July 6, 2015
Posted by Netbloggy

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