At Trianz, we believe Data Science – the conversion of data into understandable and usable information – is a great step forward in the evolution of business intelligence. Data by itself is just data, but when we apply advanced analytics techniques, modern computer technology, and domain expertise, we have something you can profitably use. If you are among the legions of those who demand to look inside the crystal ball of Big Data to predict the future of your enterprise, you’ll want to take a look at our Data Science Service offerings.
Trianz’ data science consulting services can help you with a number of issues, e.g., you’re losing ground to the competition. Why? Or maybe you want to predict future earnings based on past performance, current economic conditions, or some other factors. Just tell us your needs, and we can work with you.
Trianz’ data scientists stand apart from pure statisticians because we also have computer programming expertise, a necessity for those who manage large datasets. Another difference is that we use business domain research, which helps us with our analyses.
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To manage all this data, our data science specialists use new tools and technologies including Hadoop, MongoDB, Map-Reduce, Natural Language Processing (NLP), columnar databases, NoSQL, Cassandra, Pig, Hive, and Impala, just to name a few. Businesses and organizations now have access to enormous amounts of data that come in as and/ or can be converted into a variety of forms such as key value pairs, documents, object notation, text, free-form language, columnar data, etc.
By combining our business intelligence, data warehousing, and Big Data expertise with advanced statistical techniques, we are able to provide meaningful added value to your analytics initiatives.
Our Science services focus on the following areas:
- Predictive modeling
- Advanced statistical analysis
- Machine learning
- Data mining
- Embedded analytics
- Unstructured Data Management
- Large data volumes
- Complex data integration
- Industry-specific research
- Business processes analysis