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120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

analyzing-employee-exit-surveys

In this project, I cleaned, analyzed, and plotted employee exit survey data to find out whether employees are resigning due to job dissatisfaction, and whether younger employees are more dissatisfied than older employees.

analyzing-music-by-decade

I analyze Billboard number-one singles from each decade (1950s - present) to generate insights about how music has changed over time, and if songs that go number-one have similar attributes.

analyzing-star-wars-surveys

exploratory data analysis based on survey results

clean-code-ml

:bathtub: Clean Code concepts adapted for machine learning and data science

course-nlp

A Code-First Introduction to NLP course

coursera_capstone

if you truly want to know whether NYC is better than San Francisco

ctci-python

working through Cracking the Coding Interview 6th Edition, in Python

d-alvear

database_creation

create SQL database for MLB data

discoverify

Discoverify uses signal processing and audio features from the Spotify-API to recommend songs. An offshoot of my Friendshipify app.

ecommerce-analytics

fitbit-project

flask-friendship-playlist

AKA Friendshipify. This is the repository for my multi-user music recommender that analyzes the music taste of two people, finds the similarities between their taste, and generates a playlist of songs based on that similarity.

hackernews_project

I compare which types of HackerNews posts are more popular

isl-python

Chapter by Chapter notes, exercises and code for a variety of machine learning books using Python

modelling-credit-risk

I use logistic regression and random forests to build a model that can predict whether or not a borrower will pay off their loan

nlp-predicting-upvotes

I experiment with linear regression and random forests to build a model that can predict, using NLP methods, the number of upvotes a HackerNews article will receive based on the headline.

nyc-vs-sf

A refactoring of my old Coursera project; I wanted to answer, based on neighborhood venue types, whether Manhattan or San Francisco offers more "bang for your buck." I compared rent prices and venues per neighborhood for both cities. I used k-means clustering analysis and leveraged data using the Foursquare API.

optimal-price-for-airbnb-rentals

I explore using the K Nearest Neighbors algorithm with AirBnB data

portfolio_projects

This is my portfolio of data science projects. Descriptions of each project can be found in the README as well as links to their repositories and notebook viewer.

predicting-bike-rentals

I used decision trees and random forests to build a model that predicts the number of bike rentals on a given day

sql-business-analysis-project

answer business questions using SQL

tensorflow

Repo of notebooks where I followed tensorflow tutorials.

visualizing_earnings_from_major

answer questions about job outcomes of college graduates