It all begins with effective data collection and/or selection. This requires a good understanding of various data types and their sources. Proper organization makes it easier to describe results using appropriate and efficient descriptive statistical measures. This workshop focuses on important aspects of creating a smart data collection process, selecting the best sampling approach, validating the quality of stored information for analysis, and identifying corresponding descriptive statistical KPIs. 

For all machine learning solutions, data analytics is essential to kick-start a career in a data-driven world. One cannot claim to apply AI analytics without a deep knowledge of data analytics.

Data scientists often encounter non-conforming data when analyzing multiple groups and when tracking the same group over various periods. It's also expected to have insufficient data for analysis. In such cases, data analysis techniques should focus on statistical science related to "dependent" samples and "non-parametric" tests as alternatives to parametric ones (Data Analysis for Professionals). Determining the sample size is a common question when designing an analytical project. Should we consider the Big Data solution where all the data is included? This course will address these questions, discussing their advantages and disadvantages.