Knowledge and experience are key to our analytical processes. We apply well defined principles to make sure that every analytical process is efficient and effective. There may be cases where we have to set up an experiment or other research process to actually generate the data required for analysis.
Real data is often quite messy. There are many possible issues with real data sets such as bad formatting, trailing spaces, duplicates, empty rows, synonyms of different abbreviations, difference in scales, inconsistency in description, skewed distributions and outliers and missing values. Each of these issues can cause problems in data analysis and deserves attention in exploratory data analysis, which we perform before building any predictive models.More Details+
We perform essential Data Cleanup and Transformation.. Handling missing values for instance usually requires good understanding of the problem context and can take many alternative approaches. We also perform data transformation by mainly applying a mathematical function to each data value.More Details+
We normally perform principal component analysis, which seeks to find weighted averages of the variables to capture most of information which is measured by variance in the data. The newly generated weighted averages are known as principal components.More Details+
In our work, we follow major principles of graphical design which give to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.More Details+
We use a variety of software tools that help us master data management, metadata management, data quality and data analysis and visualization, such as:
There are a wide variety of sales, finance, supply chain, logistics, and transportation applications where the value, location and movement of objects are critical to the function of a business, and where there is need for data analytics.
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