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awesome-interview-questions

:octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:

big-data-management-in-sap-hana

Enterprise applications have become quite complex and demanding in the past years and many applications require processing of analytical transactions or running reports, while many users consume and update the data. The growth in capacity of main memory enabled a completely new database paradigm called in-memory columnar database. SAP High Performance Analytics Appliance (HANA) was made with the goal of combining analytical and transactional data into the same database. This is made possible with three main factors namely, multicore parallel processing, availability of faster and cheaper main memory and the use of columnar architecture. The columnar database uses effective compression techniques, maximum parallelization of database kernels and specialized data structures to support the complete data lifecycle which involves modeling, provisioning and consumption. In this paper, we delve into the scenario for the emergence of SAP HANA, its architecture and advantages. We will also provide an in-depth analysis of in-memory columnar database technology.

bluff

bluff---cs-masters-project

Bluff with AI

bluff-1

A CLI bluff card game implemented in Erlang

broadcast-low-exponent-rsa

The aim of the project is to study RSA algorithm and possible attacks on the algorithm in detail. The project deals with solving a mathematical attack called ‘Broadcast Low Public Exponent’ attack on RSA cryptosystem by implementing Chinese Reminder Theorem. The implementation was successful by getting the plain text as the output.

bs

cheat-in-java

The card game cheat implemented in java. Can either be automated or player input

coding-interview-prep

coursera-ml-class

data_science_projects

easy-application

397 software engineering companies that are easy to apply to

edgeseo

guide-dog-training-outcome-prediction

* Built a logistic regression model that predicted the training outcome for a given guide dog data and trainer data. * This project helps to determine the key features that can increase the dog training success rates and also helps to match the right dog with the right trainer. * Worked on Recursive Feature Elimination to analyze and rank the features. * Implemented normalization and feature extraction using TF-IDF, Word2Vec and CountVectorizer algorithms * Improve the accuracy of the model by 30% . * For more details on Guide Dog Prediction: https://www.ibm.com/blogs/think/2017/07/watson-guiding-eyes/ Technologies: Scala (spark), Python (Jupyter notebooks included)

hackerrank

hackerrankqns

helloworld

My first repository helloWorld

interview

Everything you need to kick ass on your coding interview

interviews

Everything you need to know to get the job.

inverted-index

lessons-learned-data-science-interviews

Lessons learned the hard way through over 30+ data science interviews

michelin

multi-keyword-ranked-search-over-encrypted-cloud-data

pyliar

The game "Liar" (aka "BS") implemented in python for an AI course in HUJI, includes reinforcement learning agents

python-cheatsheet

Basic cheatsheet for Python

reinforcejs

Reinforcement Learning Agents in Javascript (Dynamic Programming, Temporal Difference, Deep Q-Learning, Stochastic/Deterministic Policy Gradients)

resources_for_data_science

resume

skatbot

Final Project for CS229 @ Stanford

software-reverse-engineering---wise-folder-hider