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Data Science & Applied Statistics

Statistics. At Scale

Statistics is the art of drawing conclusions from data. Modern computing techniques allow crunching huge amounts of data, and using advanced, powerful algorithms to draw stronger conclusions than previously possible. This amalgamation of databases, algorithms and statistics is “data science”.

Data science is changing the world. Existence of an ever-increasing amount of data, newer means of gathering it, and newer means of drawing conclusions and then taking decisions from it are changing every industry. To do this, you need a team of top-notch statisticians, mathematicians and computer scientists. Oneirix Labs is that team.

Oneirix Labs merges a century's worth of accumulated statistical wisdom with large scale data processing and advanced computing, to achieve great results using modern data science.

We are experts at applying diverse statistical models to various uncertain phenomena. We fundamentally understand how uncertainty can arise from partial knowledge, a continuously changing environment, or due to the underlying chaotic process itself.

Furthermore, our deep knowledge of engineering as well as enterprises makes us appropriate partners to implement your next advanced statistical algorithm; be it sampling strategies for quality checks, pricing of products, HR processes, or core engineering models.

EXAMPLE PROJECTS

Public Transport Optimization

Public transport systems generate large amounts of data. But the data may have missing gaps. For example, we may have information about point-to-point trips, but no information about transfers. Is it possible to estimate the missing data? And what use may we put this to?

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Data Science for Oil & Gas

A large number of sensing and actuation equipments in an oil & gas field produce tremendous amounts of data. Drawing inferences from this data requires a mixture of smart statistics and physics computation.

Merging statistics & physics for oil & gas

Psychometric Testing

If we know the difficulty level of questions, we can evaluate the ability of test takers. Conversely, if we know the ability levels of test takers, we can estimate the difficult level of each question — by checking who finds the question to be difficult.

What if we do not know either?

Demand Modeling & Pricing

Demand is complex. Not only does it change with price, it changes due to thousands of other factors: competition, season, state of the economy, sentiment and so forth. Is it possible to build models of demand behavior from historical data?

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