Name: Stefan Cornelißen
Type: User
Company: Performics
Bio: loves to optimize processes to reduce efforts or resource input, passionate about the CIFL-approach, how to work smarter not harder, big data is also for SME
Location: Düsseldorf
Blog: datatoolbox.de
analysis-of-ab-landing-page-test-results---udacity-project
This work is based on a project from the Data Analyst Nanodegree of Udacity. The project includes an analysis of an AB-Landing-Page-Test results. The significance of the results has been tested by bootstrapping and linear regression.
communicate-data-findings-growth-opportunities-for-fordgobike-udacity-project
Exploration of FordGoBike Data, San Francisco, to identify growth opportunities among different user groups. This Udacity project aims to demonstrate exploring and explanation data analysis skills. The grouping is defined by the user type (customer or subscriber), gender and age. The analysis is based on the amount and duration of past rides. At the moment of analysis, the dataset contains bike sharing rides from June 2017 till January 2019.
explore-weather-trends---udacity-project
This work is based on a project from the Data Analyst Nanodegree of Udacity. It shows my approach to analyze weather trends in the given project. The dataset has been given. The approach of analysis is rather basic and underlies therefore certain conditions and limitations (mentioned at the end of the project).
investigate-a-dataset---no-shows-medical-appointments-brazil-udacity-project
This work is based on a project from the Data Analyst Nanodegree of Udacity. It analyzes the characteristics of no-shows for medical appointments in Brazil and the effectiveness of SMS-reminders. The dataset has been sourced from Kaggle.
wrangle-and-analyze-data-udacity-project
This Udacity project aims to practice a typical data wrangling process. This includes gathering data from a variety of sources, such as CSV-files, TSV-files and an API: The Twitter-API. Then its cleanly- and tidiness will be assessed in order to correct quality issues, converting the data to a tidy dataset. This happens according to the rules of tidiness https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html. Once quality and tidiness is assured, the sources will be combined to one or more master datasets before finally analyzing and visualizing it. Note: Assessing and wrangling the entirety of the dataset would require more time than provided by Udacity. They, therefore, required to find at least eight quality and two tidiness issues to be assessed and cleaned. Keep in mind, there may be more issues still present.
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
Django
The Web framework for perfectionists with deadlines.
Laravel
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
Alibaba Open Source for everyone
D3
Data-Driven Documents codes.
Tencent
China tencent open source team.
