Data science consultancy can cover any phase of a data value extraction process. Main consulting areas are: data acquisition and integration, data anonymization, data exploration and transformation, semantic metadata generation, machine learning, deep learning, data visualization, data workflows implementation and management
A Data Science consulting service is requested whenever a data application (or part of it) has to be developed, whereas Technical consulting is mostly involved when an application (or part of it) has already been developed and needs to be adapted to run on HPC resources to improve performances or scale up.
This service aims to facilitate the application of Data Science methods and best practices and their uptake by the companies.
Depending on the level of expertise of the customer, Data Science consulting may be limited to the suggestion of the most suitable tools, or to the implementation of a specific phase in the data analytics process (e.g. Machine Learning), up to the implementation of the whole process of data value extraction (business understanding, data acquisition and exploration, quality assessment, data transformation, visualization and analysis, model building, model evaluation, deployment).
This consulting service is cross-domain and over heterogeneous data sources, comprising textual, audio and video data.
The service model includes consultations, training and collaboration on projects.
In particular, customers are supported in the pre-processing phase with anonymisation techniques, outlier detection, missing data estimation, features extraction, dimensionality reduction, structuring and cleaning data.
Support in the data analytics phase is provided through (guidance in the) application of clustering techniques (partitioning methods, hierarchical clustering, fuzzy clustering, density-based clustering, model-based clustering), association rules, sequential patterns, graph analysis, predictive modelling (decision trees, random forests, neural networks, DNN, KNN, SVM, naive Bayes, regression, regression trees, symbolic regression), econometric models, production of indicators, text mining, semantic annotation, keyframe extraction, speech recognition, image classification.
Support is also provided in visualization, interactive virtual environment creation, rendering.
- Implementation of a whole data value extraction process for precision agriculture: weather forecasting at high resolution (data generation) and integration with field data, machine learning models generation and testing, data and results visualization
- Predictive maintenance for an automotive components manufacture based on data collected by onboard devices
- Virtual environments connected to real time production data (digital twin)
- Forecasting models for time series in manufacturing processes
- Data analytics for simulation data in industry
- Analysis of audience data for broadcasters