Making Data Science & AI explainable, provable, and transparent
Produce more explainable models, while maintaining a high prediction accuracy.
Designed to ensure there is no compromise of users’ data security and confidentiality
Enable business users to understand, appropriately trust, and effectively manage the data for AI.
Investing in Design methodologies
Using next generation Data Science algorithms like “Transfer Learning” and “Active Learning”.
Training the AI systems with right data
Making systems which are trained to tame the bias.
Consulting (Our Offerings)
Business UnderstandingUnderstanding the project objectives and requirements from a business perspective and converting it into a data science problem
Data UnderstandingStarts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
Data PreparationThe data preparation phase covers all activities to construct the final dataset from the initial raw data.
ModelingModeling techniques are selected and applied. Since some techniques like neural nets have specific requirements regarding the form of the data, there can be a loop back here to data prep.
EvaluationOnce one or more models have been built that appear to have high quality based on whichever loss functions have been selected, these need to be tested to ensure they generalize against unseen data and that all key business issues have been sufficiently considered. The end result is the selection of the champion model(s).
DeploymentGenerally this will mean deploying a code representation of the model into an operating system to score or categorize new unseen data as it arises and to create a mechanism for the use of that new information in the solution of the original business problem.
Our focus on value creation for the customer, domain expertise and global delivery model blended with great innovation and operational excellence are the key reasons for exceeding customer expectation in almost all the projects and work we undertake.
Our leaders have more than 15 years of experience in delivering Business Intelligence and analytics projects with cutting edge tools.
We have a very healthy pool of experts and SMEs in this domain
A sharp research focus with dedicated R&D division
Dedicated teams for various domains and tools
Cutting-edge decision analytics and data mining tools – Python, R, SAS, SQL, Big Data tools, Tableau and other UI tools.
Social media analytics (SMA) is a method of gathering and analysing data from social media sites and blogs in order to make business decisions. This method goes beyond standard tracking or a simple study of retweets or ‘likes’ to create a more comprehensive understanding of the social consumer.
Emerging India Analytics uses advanced tools and techniques to harness the potential insights social media data can offer.
Social Media Intelligence
Social Media Monitoring
Social Competitive Analysis
Social Media Sentiment Analysis