It's an open-source language, and data professionals started creating tools for it to complete data tasks more efficiently. Here, I'll introduce the most important Python libraries and packages that you have to know as a Data Scientist. In my previous article, I introduced the Python import statement and the most important modules from the Python Standard Library. In this one, I'll focus on the libraries and packages that are not coming with Python 3 by default. At the end of the article, I'll also show you how to get (download, install and import) them.
Python is a popular high-level object-oriented programming language which is used widely by a huge number of software developers. Guido van Rossum designed this in 1991, and Python software foundation has further developed it. But the question is, with dozens of programming languages based on OOP concepts already available, why this new one? So, the main purpose to develop this language is to emphasize code readability and scientific and mathematical computing (e.g. Python's syntax is very clean and short in length.
To step into the world of Python for Data Science, you don't need to know Python like your own kid. Just the basics will be enough. If you haven't yet started with Python, we suggest you read An Introduction to Python. To gear up with Python for Data Science, we suggest Anaconda. It is a freemium open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing.
Just because you have a "hammer", doesn't mean that every problem you come across will be a "nail". The intelligent key thing is when you use the same hammer to solve what ever problem you came across. Like the same way when we intended to solve a datamining problem we will face so many issues but we can solve them by using python in a intelligent way. In very next post I am going to wet your hands to solve one interesting datamining problem using python programming language. Before stepping directly into python packages let me clear you a doubt which is rotating in your mind right now.
It's never been easier to get started with machine learning. In addition to structured massive open online courses (MOOCs), there are a huge number of incredible, free resources available around the web. Here are a few that have helped me. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems.