Signal: Understanding What Matters in a World of Noise
Many people assume that, if they know how to use data analysis software, this means that they are skilled data analysts. Knowledge of data analysis tools and possession of data analysis skills, however, are not the same. Even expert users of analytical tools frequently have little or no knowledge of fundamental yet powerful data analysis techniques.
Do you have the nagging sense that signals in your data—the things that matter most—might be slipping by unnoticed? Do you ever wonder if you're wasting time tracking the wrong metrics or tracking the right metrics in the wrong ways? The data analysis that takes place in most organizations produces only a small fraction of its potential for useful insights. This is because most of the people who do this work have never been trained in data exploration and analysis beyond the basics, if at all. When presented with a new data set, do you know how to get the lay of the land—the context that's necessary for analytical insights? Do you know how to separate signals from the noise?
At any given point in time, only a small portion of any organization's data is useful for decision making. The rest is noise. Increases in data volume, velocity, and variety are actually more a problem than a benefit unless you know how to find and decipher signals buried in that growing haystack.
In this advanced data exploration and analysis course, Stephen Few takes participants beyond the basic skills that are taught in his books Show Me the Numbers and Now You See It to the next level of statistical and data visualization skills that are required for signal detection. Only the signals matter.
This workshop is for:
- Those whose work involves frequent data analysis
- Those who have already read the books Show Me the Numbers and Now You See It or have taken the courses associated with these books
This course covers:
- The importance of data analysis to support decision making
- The role and responsibilities of a data analyst
- The difference between signals and noise
- Getting the lay of the data landscape by examining key types of variation and relationships
- Variation within categories
- Variation within measures
- Variation across space
- Variation through time
- Relationships between measures
- Relationships between categories
- Relationships among multiple variables
- The fundamentals of Statistical Process Control (SPC)
- Graphs for signal detection
- Documentation for signal detection
- Dashboards for signal monitoring