Name: Hendrik Dreyer
Type: User
Company: James Cook University
Bio: Masters student in Data Science @ James Cook University. 23 Years experience as a software engineer developing software products in C/C++ on Linux/Windows
Location: Melbourne, Australia
Blog: jcu.edu.au
Hendrik Dreyer's Projects
This project investigates the relationship between the physical attributes of television appliances (screen size, power consumption and technology) and their associated energy ratings.
Utilizing the Caspio cloud based development environment I designed, coded, tested and rolled-out AutoSalesReports (ASR). ASR is a cloud based BI solution for the Australian automotive industry. ASR is a fully integrated CRM and Saleslog system, with advanced BI reporting capabilities. Microsoft SQL Server was used as the cloud database. Extensive Java scripting was used to facilitate a large portion of ASR's functionality in Caspio.
Do black vehicles have a greater mean wheel diameter than white vehicles? The underlying, safe assumption in this regard is that there is no significant difference in mean wheel diameter between black vehicles and white vehicles (null-hypothesis). The R-code can be used as is. Although, the analysis and interpretation of the statistical test is really what this exercise was about.
Carrier Synchronization under Doppler shift for Mobile Communication
Cheat Sheets
Concepts of data, information, knowledge and wisdom are the core building blocks of data science. Russell Lincoln Ackoff (1989) was the first to systematically arrange these terms into a hierarchy – referred to variously as the DIKW hierarchy, Knowledge pyramid, Knowledge hierarchy and Information hierarchy – in his article ’From Data to Wisdom’
Personal website
This project implements a wrapper to the Naive Bayes classifier in R. Feature selection is done with a prune later scheme. The UCI Mushroom dataset is classified for poisonous and non-poisonous mushrooms.
Implementation of simplistic python app, which calculates basic statistics on a given dataset
This paper reports on a statistical investigation that was performed on a given SAS VA dataset, which contains birth data from 671 neonates with birth weight less than 1500g. The investigation seeks to understand if a statistical relevant relationship exists between any of the predictive variables in the dataset and the survival rate of low birth weight babies (null-hypothesis).
This report explains how a given dataset is manipulated, augmented and consumed by Tableau. Five questions, as anticipated in a management meeting scenario, are asked about the dataset. To answer the five questions a rationale is given for the design decisions that underlies four visualizations.
This report, through meticulous dimensionality reduction of a complex dataset, seeks to cast new light on how we perceive our exodus from this realm, which we call “reality”. The dataset in this analysis was freely obtained from the Australian government.
This project is based on the well-known PageRank algorithm developed by Google to help in ranking web pages. A similar simplistic version of it is utilized here to rank images based on similarity