We conduct research on any subject in such areas as business, finance, marketing, and economy. Our research is characterized by two qualities. One of them is professionalism and adherence to the best standards in academia. And another is data driven approach. The latter means that any hypothesis, any conclusion or finding will have strong support in empirical data and will be proved beyond doubt by accompanying graphs, the results obtained through rigorous statistical testing, and other methods of proving the correctness of reasoning in empirical study.
Tell us about your problem and provide with data, if necessary, and we'll give you back well-supported by empirical facts answers to all important questions you may have. And no one is going to dispute the soundness of your approach.
We professionally analyze data. If you haven't yet amassed the data to carry out an analysis or have them unorganized, perhaps in many different formats and sources, don't worry. We can do that for you as well. Not only do we gather and organize data for our clients, we also validate them to make sure that all individual observations are logical and that all relations between the records are preserved.
With our analyses you'll know what the best ways are to solve the problems you may be facing. And you'll have all available information to make informed, winning decisions.
We develop and implement, in R or Python, statistical/econometric models belonging to a wide spectrum of models typically used in business and finance. This particularly includes predictive models, classification models, and scoring models. Both classical as well as artificial intelligence techniques can be employed to get best results. We also love working on non-standard problems that requires different approaches to be involved.
You can also hire us to review an existing model and to identify the possible directions of its improvement. We also consult in the field of empirical model application and performance (identification of the best model, estimation techniques, validation of a model's assumptions etc.).
Still not sure your problem fits well into our profile? Send us a message. We'll help you make the right decision.