sidhu177 Goto Github PK

followers: 14.0 following: 71.0 repos: 102.0 gists: 0.0

Name: Sid Ramesh

Type: User

Company: Tiger Analytics

Bio: Student of Science | Exploring Knowledge Work

Twitter: sidhu177

Location: Toronto

Blog: https://twitter.com/sidhu177

Sid Ramesh's Projects

agile_data_code_2

Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

algorithms

Minimal examples of data structures and algorithms in Python

anagramic

This repository is to create a Website that takes in a word and lists the anagrams.

A few starter examples of ansible playbooks, to show features and how they work together. See http://galaxy.ansible.com for example roles from the Ansible community for deploying many popular applications.

ansible-tuto

Ansible tutorial

awesome

:sunglasses: Curated list of awesome lists

awesome-awesomeness

A curated list of awesome awesomeness

awesome-aws

A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.

awesome-bigdata

A curated list of awesome big data frameworks, ressources and other awesomeness.

awesome-cheatsheet

:beers: awesome cheatsheet

awesome-courses

:books: List of awesome university courses for learning Computer Science!

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-dataviz

:chart_with_upwards_trend: A curated list of awesome data visualization libraries and resources.

awesome-etl

A curated list of awesome ETL frameworks, libraries, and software.

awesome-javascript

🐢 A collection of awesome browser-side JavaScript libraries, resources and shiny things.

awesome-public-datasets

An awesome list of high-quality open datasets in public domains (on-going).

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-r

A curated list of awesome R packages, frameworks and software.

beyond-leetcode-sql

Analysis of SQL Leetcode and classic interview questions. Common pitfalls, anti-patterns and handy tricks are discussed. Sample databases are provided.