Statistical analysis provides the basis of any data science project. Through statistical techniques, we are capable of gaining a deeper understanding of the data: its strengths, its weaknesses and its outliers. Through careful construction, a statistical model can offer important insights into the interaction between known variables, as well as identifying new ones. These models then provide a prime platform to gain further understand and answer deep questions regarding the processes in play within the data.
The first step of any project is to gather and process data, whether it be a simple spreadsheet or a complex time series. The data must then be cleaned up, outliers determined and their origins investigated. We are capable of producing both the software for gathering and processing the data in an automated way, suitable for production environments. When the data set is complete, its size, quality and distribution will then determine the optimal approach to handle the analysis of the data. Understanding the underlying processes is also essential and we strongly encourage a close interaction between our team and your in-house experts. Our team is capable of handling data related both to business and science.
Answering questions in a statistical context inherently implies the creation of a statistical model. The construction of an appropriate model is dependent on both the goal of the project, as well as the available input data. Different methods must be applied to extrapolate a time series rather than verifying a hypothesis. The required complexity of the model may also strongly depend on the underlying processes that lead to the generation of the data, making the interaction with the customer essential. The optimal model is often found by pooling our expertise. When the data set becomes very large and the underlying processes are not fully understood, machine learning methods may also be a valuable tool.
Analytics and visualization
An appropriate model can be an end point in itself. Especially in statistical projects however, we often want to answer concrete questions. The results of the statistical analysis will be provided in a detailed report, including reasoning, graphs and conclusions regarding your questions. When requested, the analytical code used to analyze the data can be provided in R or Python. Our staff has a wide scientific background and can often include field-specific knowledge within the analysis. Advanced data visualizations and live analysis tools can also be made within the report or delivered as software, which can be tied in directly with the data gathering workflow.
Want to learn how to apply statistics in-house? We provide both beginner and advanced workshops tailored to your applications.
Want to bring statistical analysis to production? We can tie our deep learning models into existing data systems and even help you set up the necessary hardware.