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-clean-and-analyze-employee-exit-surveys

In this project, we'll work with exit surveys from employees of the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. In this project, we'll play the role of data analyst and pretend our stakeholders want to know the following: Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer? Are younger employees resigning due to some kind of dissatisfaction? What about older employees? Below is a preview of a couple columns we'll work with from the dete_survey.csv: ID: An id used to identify the participant of the survey SeparationType: The reason why the person's employment ended Cease Date: The year or month the person's employment ended DETE Start Date: The year the person began employment with the DETE Below is a preview of a couple columns we'll work with from the tafe_survey.csv: Record ID: An id used to identify the participant of the survey Reason for ceasing employment: The reason why the person's employment ended LengthofServiceOverall. Overall Length of Service at Institute (in years): The length of the person's employment (in years)

airflow-docker

airflow_hw

analytics_vizualization

analyzing-cia-factbook-data-using-sqlite-and-python

analyzing-nyc-high-school-data

churn_model

churn model with usage of neural network

exploring-ebay-car-sales-data

exploring-ebay-car-sales-pandas-and-numpy

exploring-hacker-news-posts

In this project, we'll work with a data set of submissions to popular technology site Hacker News. Hacker news is a site started by the startup incubator Y Combinator, where user-submitted stories (known as 'posts') are voted and commented upon, similar to reddit. Hacker News is extremely popular in technology and startup circles, and posts that make it to the top of Hacker News' listings can get hundreds of thousands of visitors as a result.

linux-git2

ml_auto_cleaning_choosing_different_methods_of_ml

nla2020_ozon

Numerical linear algebra course in Ozon masters, Fall 2020

predicting-car-prices-knn

Predicting Car Prices using KNN

profitable-app-profiles-for-the-app-store-and-google-play-markets

For this project, we'll pretend we're working as data analysts for a company that builds Android and iOS mobile apps. We make our apps available on Google Play and the App Store. We only build apps that are free to download and install, and our main source of revenue consists of in-app ads. This means our revenue for any given app is mostly influenced by the number of users who use our app — the more users that see and engage with the adds, the better. Our goal for this project is to analyze data to help our developers understand what kinds of apps are likely to attract more users.

solutions

Solutions for projects.

star-wars-survey

visualizing-earnings-based-on-college-majors-graphs-diagrams

We'll be working with a dataset on the job outcomes of students who graduated from college between 2010 and 2012. The original data on job outcomes was released by American Community Survey, which conducts surveys and aggregates the data. FiveThirtyEight cleaned the dataset and released it on their Github repo. Each row in the dataset represents a different major in college and contains information on gender diversity, employment rates, median salaries, and more.

visualizing-the-gender-gap-in-college-degrees-diagrams

Continuing working with graphs and diagrams, exploring the gender gap in colleges