The Power of Ten

Investing in learning something new is both rewarding, and a means to stay relevant. I regularly make a point to present something to my audiences that is both unique and useful in their line of work. Data Science is a vast field, and a lot of its amazing capabilities and tools sometimes is left unexplored. Occasionally, I try to bring to light some interesting libraries, packages, and functions that can prove to be of tremendous help to the Data Science Community.

Here is a compilation of five articles which touch upon a different aspect of Data Science altogether. Each article discusses ten important tools that are lesser known but highly useful in the Data Science domain. Hopefully, you'll learn a thing or two from every article.

10 Lesser Known Python Libraries for Data Science

Python is a fantastic language and has time and again proved its prowess both in developer job roles and data science positions across industries. One of the reasons for its success and popularity is its set of robust libraries that make it so dynamic and fast.

In this article, I discuss some of the Python libraries for data science tasks other than the commonly used ones like pandas, scikit-learn, and matplotlib. Although libraries like pandas and scikit-learn are the ones that come to mind for machine learning tasks, it's always good to learn about other exciting Python offerings in this field.

The ten libraries covered in this article are as follows:

10 Free tools to get started with Data Visualisation-Easily & Instantly.

If you are just getting started in Data Visualisation, have no experience in art or graphic design, don’t want to code and want to begin instantly making graphs or maps, then this article is just for you. This article tries also bring to light tools other than the popular one’s tools like Tableau Public, PowerBI, and Google Charts, which are used quite commonly in the Data Science ecosystem.

So, here are ten freely available tools that let beginners start building beautiful visuals immediately.

All of these data visualization tools are available for free but may have a premium version if you want to upgrade and access more options, especially the cloud services.

10 Competitive Data Science Platforms other than Kaggle

Kaggle is a well-known platform for Data Science competitions. It is an online community of more than 1,000,00 registered users consisting of both novice and experts. However, apart from Kaggle, there are other Data Mining Competition Platforms worth knowing and exploring. Here is a brief overview of some of them.

10 Python image manipulation tools

Our world today is full of data, and images form a significant part of this data. However, to be put to any use, these images need to be processed. Image Processing is thus the process of analyzing and manipulating a digital image primarily aimed at improving its quality or for extracting some information from it, which could then be put to some use.

Typical tasks in image processing include displaying images, necessary manipulations like cropping, flipping, rotating, etc., Image Segmentation, Classification and feature extractions, Image restoration, and Image recognition. Python becomes an apt choice for such Image processing tasks. This is due to its growing popularity as a scientific programming language and the free availability of many State of the Art Image Processing tools in its ecosystem.

10 Simple hacks to speed up your Data Analysis in Python

Tips and Tricks, especially in the programming world, can be handy. Sometimes a little hack can be both time and life-saving. A minor shortcut or add-on can sometimes prove to be a Godsend and can be a real productivity booster. So, here are some of my favorite tips and tricks that I have used and compiled together in the form of this article. Some may be relatively known, and some may be new, but I am sure they would come in pretty handy the next time you work on a Data Analysis project.