Large-scale data analysis is the process of running data analysis techniques against big data repositories. It is quite a challenging task both from the analytical and the infrastructural point of view. However, it allows employing massive amounts of data and therefore providing right intervention to the right patients at the right time.
Our task was to estimate the global effect of a certain business decision onto the certain groups of the customers.
We have built propensity models to describe the hidden relation between drugs and diseases on one hand and the treatment outcome on the other.
Large scale analytics employs Spark and Hadoop ecosystems to efficiently manage and query bid data repositories. On top of this infrastructure, we have used a number of state-of-the-art techniques, such as statistical analysis,deep learning, cluster analysis, and advanced data visualization to get the insight into the data internal relations.
PCA
K-means
Decision trees
Linear models
PageRank
Digital filters
DTW
Deep learning
Probabilistic graphical models
CART
ensembles
unsupervised sound segmentation
recurrent models
bayesian approach
probabilistic programming
hmm
alexnet
vgg
vae
PCA
TF-IDF
LDA
SVM
Naive bayes
word2vec
attention models
Hi, we are Sciforce - a company where the integration of various branches of science builds up a powerful force to create robust software solutions. Working at the intersection of Computer Science with other technical, natural and humanitarian sciences let us go beyond traditional IT services and become both technical and scientific forces to our customers.