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Why Attend
Effective data analysis begins with accurate data collection and selection, which requires a solid understanding of various data types and their diverse sources. Properly structuring this data ensures seamless visualization across different chart types and enables the use of efficient descriptive statistical measures to summarize results.
This course focuses on the essentials of designing a robust data collection process, selecting optimal sampling techniques, validating data quality, and exploring visualization options alongside their corresponding descriptive statistical KPIs. Participants will also gain insight into advanced techniques and tools for comprehensive data analysis, laying the groundwork for a successful career in the field of data or as preparation for Machine Learning courses or programs.
This course is designed to provide participants with a clear understanding of data structuring for efficient analysis, scientific profiling of different groups through smart data examination, and practical experience with current technology tools available in the market.
Course Methodology Each statistical tool or methodology used during the course is supported by its own case study with step-by-step outputs that go in parallel with multi stage analysis. In addition to group discussions, all analysis tools are detailed and demonstrated with sequential screen shot applications on comparative technologies (EXCEL – STATISTICA and SAS – R and Python). Course Objectives By the end of the course, participants will be able to:
Data visualization and descriptive statistics
Comparing two groups
Comparing multiple groups
Simple regressions
Data analysis project best practices
