Python provides us with many styles of coding.
In a way, it is pretty inclusive.
One can come from any language and start writing Python.
However, learning to write a language and writing a language in an optimized way are two different things.
In this series of posts named Python Shorts, I will explain some simple but very useful constructs provided by Python, some essential tips and some use cases I come up with regularly in my Data Science work.
Learning a language is easy. Whenever I start with a new language, I focus on a few things in below order, and it is a breeze to get started with writing code in any language.
Operators and Data Types: +,-,int,float,str
Conditional statements: if,else,case,switch
Loops: For, while
Data structures: List, Array, Dict, Hashmaps
Define Function
However, learning to write a language and writing a language in an optimized way are two different things.
Visualizations are awesome. However, a good visualization is annoyingly hard to make.
Moreover, it takes time and effort when it comes to present these visualizations to a bigger audience.
We all know how to make Bar-Plots, Scatter Plots, and Histograms, yet we don’t pay much attention to beautify them.
This hurts us - our credibility with peers and managers. You won’t feel it now, but it happens.
Recently I started up with a competition on kaggle on text classification, and as a part of the competition, I had to somehow move to Pytorch to get deterministic results. Now I have always worked with Keras in the past and it has given me pretty good results, but somehow I got to know that the CuDNNGRU/CuDNNLSTM layers in keras are not deterministic, even after setting the seeds.
With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning. For those who don’t know, Text classification is a common task in natural language processing, which transforms a sequence of text of indefinite length into a category of text. How could you use that?
To find sentiment of a review. Find toxic comments in a platform like Facebook Find Insincere questions on Quora.
Graphs provide us with a very useful data structure. They can help us to find structure within our data. With the advent of Machine learning and big data we need to get as much information as possible about our data. Learning a little bit of graph theory can certainly help us with that.
Here is a Graph Analytics for Big Data course on Coursera by UCSanDiego which I highly recommend to learn the basics of graph theory.
Recently Kaggle master Kazanova along with some of his friends released a “How to win a data science competition” Coursera course. You can start for free with the 7-day Free Trial. The Course involved a final project which itself was a time series prediction problem. Here I will describe how I got a top 10 position as of writing this article.
Description of the Problem: In this competition we were given a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company.
