Data mining | Data modeling | Predictive forecasting | Deep & machine learning
Predictive maintenance | Reports automation | Big data
Spatial data (GIS) | Geocomputation | Web mapping
R programming | Code optimizations & improvements | Consulting
Data visualization | Shiny | Non-standard graphs and charts
Members of our team have 10+ years of experience in effective processing, programming and statistical analysis of various datasets. We offer a complete solution for every step of data processing: starting from data cleaning, detecting outliers and erroneous data, up to creating or improving predictive modeling solutions.
Almost every data science solution rely on the knowledge about the analyzed process, and how well can it be re-defined by the language of statistics. Our team proved in numerous (scientific and business) projects the robustness of dealing with complex statistical problems and turned it into effective solutions. We also help you to interpret and explain obtained results.
There's no better way to make use of data than showing it in a proper way. Often use of a standard charts is not enough to present essential information that your data contains. Therefore non-standard and highly-customizable graphs are our specialty. The best visualization techniques ease the interpretation of obtained results and help you to understand the data.
Analysis of spatial data is one of our greatest specialty. Our projects keep the best standards of GIS software. We are deeply involved in spatial data analyst community and contribute in mapping software development. After spending more than hundreds of hours on teaching GIS we know that sometimes even a simple (static or interactive) map is worth more than any word, table and chart...
Cutting edge statistical modeling techniques may help to improve efficiency of production process by reducing failures, optimizing production process, finding and eliminating bottlenecks, etc... Thus, a proper analysis of predictive maintenance may clearly reduce overall spendings.
Many documents created on a daily, monthly or annual basis are fully reproducible. Save your time by automatizing the entire process of creating report in a one-click-solution. We couple narrative text and code into elegantly formatted output that is finally saved as PDF, MS Word or HTML document.
Past & Future Climate Projections in Poland with bias correction according to CMIP-5 simulations; Data clustering
Data processing & machine learning of observational, satellite, radar, and numerical weather prediction data
GIS mapping with the use of R+Quantum GIS solutions. Customizing map layouts and GIS-related calculations based on GPS measurements
We do believe that the best solution does not have to cost a lot.
Therefore all of our projects are done to the highest standards of the R programming language.
Thanks to R (and partly other languages like Python, C++ and Fortran) we provide an easily scalable cutting-edge technology with all benefits of open source software.
High applicable potential of R is confirmed by its popularity for (statistical) data analysis. Click below to see more details:
Founder of "IQ Data". Earn his Ph.D in atmospheric sciences, where flood of data and different storage standards are met on a daily basis. Started R programming around 2009, but still does not forget about efficient Fortran coding (where necessary). Linux & FOSS enthusiast. Specialized in data analyst solutions focused on predictive modeling. Author of numerous impact-factored scientific papers on topics related to statistical and numerical modeling in meteorology, energy sector & evaluation of risk assessment.
Earn his Ph.D in geoinformatics for a work coupling machine learning algorithms with Geographical Information Systems (GIS). Currently developing spatial algorithms in the Space Informatics Lab (Univ. of Cincinnati). Active member of a R-GIS community and author of numerous scientific papers and books (including Geocomputation with R) related to spatial data analysis and modeling.
Former biology PhD Student. Currently a guest scientists in Bundesinstitut für Risikobewertung and previously engineer in National Research Institute. Specialized in classical statistics and business data mining for medical and food safety research. Author of numerous scientific papers in the field of biology and reports in the area of Pest Risk Assessment.