Data Science as a Service
Data science as a service (DSaaS) is a form of outsourcing that involves the delivery of information gleaned from advanced analytics applications run by data scientists at M-Connect Solutions for its corporate clients for their business use.
Combination of applied mathematics and statistics to data turn vast amounts of data into new insights and new knowledge with confrontation of too much data in a large scale organization, Data Science service helps to unlock the potential about your operations, performance, customers and competitors. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.
- Data Preparation
- Data Ingestion
- Statistical Modelling
- Algorithm Development
- Insight Generation
- Insight Deployment
Statistical Modelling and Algorithm Development
M-Connect Solutions implements statistical methodologies, machine learning or a blend of both these prevalent practices to create analytical models on big data. The statistical modeling service spans:
- Data Interpretation
- Date Explanation
- Date Presentation
Our insight deployment process focuses on applying the analytical results into the routine decision-making process and automate it.
- Write back of insights to current processes and systems
- Gathering results of analytics implementation through feedback loop
- Advancement of analytics processes according to feedback loop
- Predictive processes and systems’ automation using analytics-driven insights
Data Preparation and Ingestion
The exciting work of analytics doesn’t work well until the data is ready for meaningful analysis. So, data preparation is an inevitable step in the data science process. Data can be streamed in real time or ingested in batches.
When data is ingested in real time, each data item is imported as it is emitted by the source. When data is ingested in batches, data items are imported in discrete chunks at periodic intervals of time.
The Insight Generation process basically revolves around collection and organization of data. It uses knowledge extractors to generate various relations, predictions, correlations, benchmarks, outlier identifications, and optimizations.
Uses available domain ontology and human domain expert to reason over the extracted knowledge and generate candidate insights.
Why Choose Us?
- STRATEGICALLY PLANNED
We explore what’s possible with data and make a plan.
- ACCURATE CONSULTING
Tailored solutions with our data science service as per your questions.
We respect your business. We will never contact your business competitors/clients.
- DATA SECURITY
We understand the confidentiality of your data and will never disclose to unwanted stake holders.
We can help get your project developing. get started now
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