Data Science and Machine Learning: for more informed, agile, and optimized business decisions
The interest in Data Science has grown exponentially over the past few years; due to the birth of Big Data , and the development of novel fields such as Machine Learning and Deep Learning. We employ a team of Artificial Intelligence specialists — capable of creating a variety of AI models and automated solutions — which lies at the heart of these rapidly growing fields.
We can help your business harness these capabilities all the way from conception, to development, and even implementation.
Our “DnA Sprint” serves as an accelerator for your data initiatives, identifying business opportunities which can be leveraged by Data & Analytics (DnA). Our brief engagements (2 to 4 weeks) lay the groundwork by identifying essential needs and creating a step-by-step action plan based on our expertise and Data Science methodologies.
How we do it
This step consists of understanding your business and the performance indicators which signal success. These indicators are used to assess the expected gains associated with the adoption of certain solutions, and help steer the project towards more fruitful ends.
This is one of the most important phases of the process, in which the available data is studied and analyzed in order to determine the potential work to be realized.
The data preparation phase is essential, as it is when we improve upon and make adequate the available data. This then allows us to successfully perform exploratory analyses and create predictive models.
Modelling is the phase in which the fruits of our labor begin to take shape. Here, we will extract understandings from our exploratory analyses and create predictive models that are capable of actually automating your enterprise.
During the evaluation/valuation phase, the objective is to validate the results obtained from the previous steps; and guarantee that the proposed action plan will lead to the desired results.
Making the completed solution available in the actual production environment.