A combination of Methodology, Statistics and Technology makes up the Redwood Data Science Stack.
1. redwood data science methodology
The methodology we adopt is the globally recognised CRISP-DM model.
At Redwood, our seasoned Data Science professionals understand how businesses want to use their data and then arrive at solutions which make that happen. We even suggest more ways in which your organization can be leveraging their data.
Our approach is to give you the best solution within a reasonable time frame (and by that we mean weeks and not years!) We aim to be inventive but our solutions will be on point and simple to grasp.
VISUAL REPRESENTATION OF THE CRISP-DM MODEL
2. redwood data science statistical stack
The techniques we use are cutting edge, and our penchant for research and drive for excellence means that we are constantly adopting the most up to data techniques and technology.
AN OVERVIEW OF SOME OF OUR STATISTICAL SKILLS
Linear Regression
Logistic Regression
Decision Tree
Random Forest
Support Vector Machines
K-nearest neighbours
K-means clustering
Association Analysis
Gradient Boosting Machines
Neural Networks
Naive Bayes Classification
Natural Language Processing
3. redwood data science technology stack
The technology we use during the solutioning and deployment phase of our projects are the best in class.
a glimpse at some of the projects we have worked on
internet of things
Anamoly detection and Fault prediction carried out on unsupervised time stamped data collected from sensors of a manufacturing plant.
oil and gas
Created a Predictive Model to optimise yield and draw correlations among all possible variables within semi structured data available on three types of oil wells.
workforce modelling
Created an Attrition Model using Multi Variate Analysis to determine factors that would result in lower attrition and higher productivity amongst staff of a mid size pharma company.