Text mining is a key analytical tool for business models, where the theme is to stay updated with the knowledge to gain actionable data-driven acumens for key decision‐making. Most of the available data is in unstructured textual data stored in a diverse format e.g. MS Office, PDF, JSON, HTML and/or XML. Our text mining data science workflows implement statistical and linguistic methodology for enrichment and tagging keywords to extract structured features. The structured features are further investigated for identifying patterns or visualizations to answer your business questions.
Exploratory Data Analytics
Exploratory data analytics have capabilities to shed light on the trend for increased understanding of the landscape of opportunities and/or inefficiencies in for the specific interrogation. The analytical phase for a specific question starts by collecting and mining data provided by the patron or collected strategically by our consultants from public domain. Further analytical and visualization techniques are used from open-source platforms as KNIME, R, Python, Gephi or from commercial analytical software.
Predictive analytics is implemented for the business questions where the requirement is to predict trends and patterns based on existing dataset. The analytical life cycle runs from data collection, modeling, statistical evaluations and deployment of the predictive model. Our consultants are well equipped with the emerging machine learning algorithms e.g. Random Forest, Neural Networks, SVM, Decision Tree and Deep Learning methods or complex ensemble learning algorithms.
Network analytics finds its roots in graph theory and provide a new dimension to your data-driven solutions for your question. It is designed to evaluate relationships and interconnection between the nodes and edges. Our team provides a custom solution by capturing regulatory motifs and analyzing the network structure.