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120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

austin-analysis

Recommending neighborhoods in Austin, Texas. What is the problem you are attempting to solve? As an immigrant and traveler, I know how hard it is to find a good neighborhood to live in, especially in an unfamiliar area or under a time restraint. Not many people love to do deep research. Also, one of the most popular questions on different forums is: “which area should I live with my family”. In this project, my goal is to assist people in recommending neighborhoods in Austin that meet their personalized needs. The application can collect user preferences by answering simple questions and then a list of recommended neighbors will be returned. How is your solution valuable? My solution helps people guide through their search for a new home. This application will take into different user preferences such as accommodating family needs (certain schools or level crime) and their budget. Not only will this allow people to save time, but also make smarter decisions. For those looking to buy a house as additional investment, the application will also have a feature that predicts house values. What is your data source and how will you access it? I have 16 years of public crime reports from the Austin police department. This dataset contains a record of incidents that the Austin Police Department responded to and wrote a report. Next is the real estate market. I am going to collect all addresses in Austin and surrounding areas from the County appraisal district (they are free to export) and going to use them to collect information about houses from the Zillow library (PyZillow) with free open Zillow API. Of course, there will be no personal information

austin-crime-eda

Performed EDA of a dataset that contains crime reports from the Austin, TX police department.

data-science--cheat-sheet

Cheat Sheets

datasciencecoursera

Data Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-with-tensorflow

docs

TensorFlow documentation

docs-l10n

end-to-end-time-series

This repository hosts code for my Time Series videos part of playlist here - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

ibm-employee-attrition

Using supervised learning techniques built models to predict IBM Employee Attrition.

introtohadoopandmr__udacity_course

:elephant: Source code for assignments of Udacity course "Introduction to Hadoop and MapReduce"

mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

nastikas.github.io

predicting-house-prices

Designed a linear regression model to predict house prices and examine additional factors that may affect house prices.

python-deep-learning-cookbook

Python Deep Learning Cookbook, published by Packt

stanford-cs231

Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).

stanford-tensorflow-tutorials

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

thinkful

timeseriesclassification