Quantitative Research

No one would doubt that the current business reality does not allow businesses to ignore insights extraction from their own data. Hypotheses are formed fast and demand even faster justification. The problem is that it's usually hard or even impossible to have huge massives of data at a researcher's disposal. However, even in this case, an experienced one can extract useful information that will lead a business to actionable insights saving tons of money and hours that could be spent on bad decisions made without data analytics.

The research has been done for an apparel resale C2C marketplace within 45 hours after data has been received from the project team.

This particular research has been done for business stakeholders, though preserving its academic-grade quality. The majority of analytics has been done in R statistic language (similar to Python in this case) to acquire advanced tools for statistics and econometrics alongside useful data analytics and visualization tools.

The resulting research summarises several dozens of data insights and provides interpretation accompanied by potential moves to exploit newly acquired info.