Material for the Big Data Analytics exercise classes - INFOH515 - Big Data : Distributed Data Management and Scalable Analytics - Université Libre de Bruxelles

candralabcms

Automatically exported from code.google.com/p/candralabcms

computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

contoh_aplikasi_madm_dengan_metode_topsis

Aplikasi MADM dengan Metode TOPSIS untuk Kasus Pemilihan Lokasi Gudang

create-react-app

Set up a modern web app by running one command.

data-analytics-machine-learning-big-data

Slides, code and more for my class: Data Analytics and Machine Learning on Big Data

digital-foundry-demand-forcasting

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.