Effective data analysis starts with good data collection and/or selection. Conducting this requires good comprehension of all data types and their multiple sources. Furthermore, structuring that properly allows the ease of its visualization under different charts and describes all the results with adequate and efficient descriptive statistics measures.
This course starts with key points in designing a smart data collection process, sampling best approach, validating the quality of the information stored for analysis, and understanding all the visualization possibilities and their corresponding descriptive statistical KPIs. Moreover, this course explores all techniques and tools for comprehensive data analysis, prior to kicking off any work or even a career in the world of data. The course also serves as a primer to any Machine Learning course/program.
In addition, this course is designed to make participants have a clear and complete understanding of data structuring for efficient data analysis, of profiling different groups scientifically by analyzing data smartly and efficiently, and of appropriately manipulating several technology tools now in the market.
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).
Applied Data Analysis is the foundation for all Machine Learning and Artificial Intelligence (AI) practitioners. It is prerequisite knowledge that is applicable in all industries and data related functions.