This repository contains answers of the Shopify Summer 2022 Data Science Intern Challenge.

Overview

Data-Science-Intern-Challenge

This repository contains answers of the Shopify Summer 2022 Data Science Intern Challenge.

Summer 2022 Data Science Intern Challenge

Please complete the following questions, and provide your thought process/work. You can attach your work in a text file, link, etc. on the application page. Please ensure answers are easily visible for reviewers!

Question 1: Given some sample data, write a program to answer the following: click here to access the required data set

On Shopify, we have exactly 100 sneaker shops, and each of these shops sells only one model of shoe. We want to do some analysis of the average order value (AOV). When we look at orders data over a 30 day window, we naively calculate an AOV of $3145.13. Given that we know these shops are selling sneakers, a relatively affordable item, something seems wrong with our analysis.

  1. Think about what could be going wrong with our calculation. Think about a better way to evaluate this data.

Answer: The wrong average was calculated using this method: total of all order values/ number of order_values. This is wrong because the formula didn't consider the fact that an order can have multiple items. I have tried to explain the problem with code. Click Here to view it.

  1. What metric would you report for this dataset?

Answer: The correct approach would be to divide the total of all order_values by the sum of total_items. By following this method, we would consider the fact that an order can have multiple items.

  1. What is its value?

Answer: $357.92

Question 2: For this question you’ll need to use SQL. Follow this link to access the data set required for the challenge. Please use queries to answer the following questions. Paste your queries along with your final numerical answers below.

  1. How many orders were shipped by Speedy Express in total?

Answer: 54

  1. What is the last name of the employee with the most orders?

Answer: Peacock

  1. What product was ordered the most by customers in Germany?

Answer: Boston Crab Meat. This product was ordered 160 times in total.

Click here to check the sql queries.

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Seulki Park 70 Jan 03, 2023
PyTorch wrappers for using your model in audacity!

audacitorch This package contains utilities for prepping PyTorch audio models for use in Audacity. More specifically, it provides abstract classes for

Hugo Flores García 130 Dec 14, 2022
StarGAN2 for practice

StarGAN2 for practice This version of StarGAN2 (coined as 'Post-modern Style Transfer') is intended mostly for fellow artists, who rarely look at scie

vadim epstein 87 Sep 24, 2022
Code for the SIGGRAPH 2021 paper "Consistent Depth of Moving Objects in Video".

Consistent Depth of Moving Objects in Video This repository contains training code for the SIGGRAPH 2021 paper "Consistent Depth of Moving Objects in

Google 203 Jan 05, 2023
Combine Tacotron2 and Hifi GAN to generate speech from text

EndToEndTextToSpeech Combine Tacotron2 and Hifi GAN to generate speech from text Download weights Hifi GAN - hifi_gan/checkpoint/ : pretrain 2.5M ste

Phạm Quốc Huy 1 Dec 18, 2021
Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting Note: You can find here the accompanying seq2seq RNN forecas

Guillaume Chevalier 1k Dec 25, 2022
CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels Accurate pressure drop estimat

Alejandro Montanez 0 Jan 21, 2022
This repo implements a 3D segmentation task for an airport baggage dataset.

3D CT Scan Segmentation With Occupancy Network This repo implements a 3D superresolution segmentation task for an airport baggage dataset. Our final p

Christoph Reich 2 Mar 28, 2022
Contrastive Language-Image Pretraining

CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair

OpenAI 11.5k Jan 08, 2023
The missing CMake project initializer

cmake-init - The missing CMake project initializer Opinionated CMake project initializer to generate CMake projects that are FetchContent ready, separ

1k Jan 01, 2023
A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.

Awesome AutoDL A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awe

D-X-Y 2k Dec 30, 2022
Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset This repository provides a unified online platform, LoLi-P

Chongyi Li 457 Jan 03, 2023
A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

Benedek Rozemberczki 259 Dec 05, 2022
This repository contains code demonstrating the methods outlined in Path Signature Area-Based Causal Discovery in Coupled Time Series presented at Causal Analysis Workshop 2021.

signed-area-causal-inference This repository contains code demonstrating the methods outlined in Path Signature Area-Based Causal Discovery in Coupled

Will Glad 1 Mar 11, 2022
Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

DMT: Dynamic Mutual Training for Semi-Supervised Learning This repository contains the code for our paper DMT: Dynamic Mutual Training for Semi-Superv

Zhengyang Feng 120 Dec 30, 2022
Unsupervised Image to Image Translation with Generative Adversarial Networks

Unsupervised Image to Image Translation with Generative Adversarial Networks Paper: Unsupervised Image to Image Translation with Generative Adversaria

Hao 71 Oct 30, 2022
Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation"

CoCosNet Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation" (CVPR 2020 oral). Update: 202

Lingbo Yang 38 Sep 22, 2021
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling

⚠️ ‎‎‎ A more recent and actively-maintained version of this code is available in ivadomed Stacked Hourglass Network with a Multi-level Attention Mech

Reza Azad 14 Oct 24, 2022
GANfolk: Using AI to create portraits of fictional people to sell as NFTs

GANfolk are AI-generated renderings of fictional people. Each image in the collection was created by a pair of Generative Adversarial Networks (GANs) with names and backstories also created with AI.

Robert A. Gonsalves 32 Dec 02, 2022