Interview Coding Challenge Data Science Step 1 of the Data Scientist Interview process. Follow the instructions below to complete this portion of the interview. Please note, although we do not set a time limit for this challenge, we recommend completing it as soon as possible as we evaluate candidates on a first come, first serve basis... If you have any questions, please feel free to email support@TheZig.io. We will do our best to clarify any issues you come across. Prerequisites: A Text Editor - We recommend Visual Studio Code for the ClientSide code, its lightweight, powerful and Free! (https://code.visualstudio.com/) SQL Server Management Studio (https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017) R - Software Environment for statitistal computing and graphics. You can download R at the mirrors listed here (https://cran.r-project.org/mirrors.html) Azure - Microsoft's Cloud Computing platform. You can create an account without a credit card by using the Azure Pass available at this link (https://azure.microsoft.com/en-us/offers/azure-pass/) Git - For source control and committing your final solution to a new private repo (https://git-scm.com/downloads) a. If you're not very familiar with git commands, here's a helpful cheatsheet (https://services.github.com/on-demand/downloads/github-git-cheat-sheet.pdf) 'R' Challenge For each numbered section below, write R code and comments to solve the problem or to show your rationale. For sections that ask you to give outputs, provide outputs in separate files and name them with the section number and the word output "Section 1 - Output". Create a private repo and submit your modified R script along with any supporting files. Load in the dataset from the accompanying file "account-defaults.csv" This dataset contains information about loan accounts that either went delinquent or stayed current on payments within the loan's first year. FirstYearDelinquency is the outcome variable, all others are predictors. The objective of modeling with this dataset is to be able to predict the probability that new accounts will become delinquent; it is primarily valuable to understand lower-risk accounts versus higher-risk accounts (and not just to predict 'yes' or 'no' for new accounts). FirstYearDelinquency - indicates whether the loan went delinquent within the first year of the loan's life (values of 1) AgeOldestIdentityRecord - number of months since the first record was reported by a national credit source AgeOldestAccount - number of months since the oldest account was opened AgeNewestAutoAccount - number of months since the most recent auto loan or lease account was opened TotalInquiries - total number of credit inquiries on record AvgAgeAutoAccounts - average number of months since auto loan or lease accounts were opened TotalAutoAccountsNeverDelinquent - total number of auto loan or lease accounts that were never delinquent WorstDelinquency - worst status of days-delinquent on an account in the first 12 months of an account's life; values of '400' indicate '400 or greater' HasInquiryTelecomm - indicates whether one or more telecommunications credit inquires are on record within the last 12 months (values of 1) Perform an exploratory data analysis on the accounts data In your analysis include summary statistics and visualizations of the distributions and relationships. Build one or more predictive model(s) on the accounts data using regression techniques Identify the strongest predictor variables and provide interpretations. Identify and explain issues with the model(s) such as collinearity, etc. Calculate predictions and show model performance on out-of-sample data. Summarize out-of-sample data in tiers from highest-risk to lowest-risk. Split up the dataset by the WorstDelinquency variable. For each subset, run a simple regression of FirstYearDelinquency ~ TotalInquiries. Extract the predictor's coefficient and p-value from each model. Store the in a list where the names of the list correspond to the values of WorstDelinquency. Load in the dataset from the accompanying file "vehicle-depreciation.csv". The dataset contains information about vehicles that our company purchases at auction, sells to customers, repossess from defaulted accounts, and finally re-sell at auction to recover some of our losses. Perform an analysis and/or build a predictive model that provides a method to estimate the depreciation of vehicle worth (from auction purchase to auction sale). Use whatever techniques you want to provide insight into the dataset and walk us through your results - this is your chance to show off your analytical and storytelling skills! CustomerGrade - the credit risk grade of the customer AuctionPurchaseDate - the date that the vehicle was purchased at auction AuctionPurchaseAmount - the dollar amount spent purchasing the vehicle at auction AuctionSaleDate - the date that the vehicle was sold at auction AuctionSaleAmount - the dollar amount received for selling the vehicle at auction VehicleType - the high-level class of the vehicle Year - the year of the vehicle Make - the make of the vehicle Model - the model of the vehicle Trim - the trim of the vehicle BodyType - the body style of the vehicle AuctionPurchaseOdometer - the odometer value of the vehicle at the time of purchase at the auction AutomaticTransmission - indicates (with value of 1) whether the vehicle has an automatic transmission DriveType - the drivetrain type of the vehicle

Some rust practice; simple Pokedex using a csv, using "inquire" for most of the functions

A Python script that leverages the power of the langchain package to create a CSV agent. This agent can answer questions about a CSV file using the OpenAI API. The code provides a user-friendly interface built with Streamlit, allowing users to upload a CSV file and inquire about its content.

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A simple command-line Expense Tracker built using Node.js, Inquirer and Commander.js. Track your expenses, filter by category or month, and export to CSV!

A web application that allows you to upload a CSV file and then inquire about properties in your area.

Консольное приложение для управления личными финансами: счета, доходы/расходы, история транзакций и текущие балансы. Поддерживает добавление и удаление транзакций с суммой/датой/описанием, показывает сводки и аналитику по счетам, а также экспортирует транзакции в CSV. Навигация реализована через интерактивный CLI на базе inquirer.

This Python program simplifies initial patient assessments in a medical office. It asks about health status and, if unwell, inquires about specific bodily systems and their associated symptoms. Responses are logged in a "patient_symptoms.csv".

Utilizes speech recognition and synthesis to create an interactive data exploration assistant. Enables users to verbally interact with the program, read data from CSV files, inquire about column names, and visualize data distribution through distribution plots.

Stanford CS106A Code In Place Final Project Code: This program helps international high school applicants inquire about the test admission policy for the Fall 2021 admission cycle based on a data scraped from a downloaded CSV file. Source: FairTest The National Center for Fair and Open Testing, List Compiled by Sarah Loring de Garcia

Clustered Data Warehouse Suppose you are part of a scrum team developing data warehouse for Bloomberg to analyze FX deals. One of customer stories is to import deals details from files into DB. Knowing that each 5 seconds around 100000 deal took place and stored in a file shared via a shared folder, the requested performance is to be able to import the file in less than 5 seconds. Request logic as following : File format is CSV contains the following fields (Deal Unique Id, From Currency ISO Code "Ordering Currency", To Currency ISO Code, Deal timestamp, Deal Amount in ordering currency). Validate row structure. Valid rows should be stored in table/document, with reference to source file name . Invalid rows should be stored into another table/document, with reference to source file name. The DB contains another table to maintain accumulative count of deals per Ordering Currency "Columns : Currency ISO Code, CountOfDeals ", so upon completion of importing process the system should increase count of deals per currency. System should not import same file twice. No rollback allowed, what every rows imported should be saved in DB. Technical Specs : Access to DB should be through JPA. For DB type, you can select between (MySql or MongoDB) Provide a web interface for uploading files and inquire about results "using filename" following web applications 3 tier architecture. Spring Batch is not allowed. Deliverables should be ready to work including : Workable deployment including sample file. Deployment steps including sample data of 100K records, contains all invalid/valid records that the system handles. Maven or Gradle project is required for full source code. Proper error/exception handling. Proper Logging. It is essential to follow TDD and include unit testing of your code and provide it as part of the assignment, noting that this will be one of the major assessment points, minimum code coverage should be 70%. It is preferred to deliver via github or bitbucket repository. Provide deployment as a Vagrant or Docker is a plus.