Chapter 3 Slides
▪ Opening Vignette
▪ Nature of Data
▪ Data source reliability
▪ Data content accuracy
▪ Data accessibility
▪ Data security and privacy
▪ Data richness
▪ Data consistency
▪ Data currency
▪ Data granularity
▪ Data validity
▪ Data relevancy
▪ Data preprocessing steps – Figure 3.3
▪ Correlation versus regression
▪ Simple versus multiple regression
▪ Figure 3.14 process flow for developing regression models
▪ Most important assumptions in linear regression
▪ Logistic regression
▪ Time-series forecasting
▪ To ensure that all departments are functioning properly.
▪ To provide information.
▪ To provide the results of an analysis.
▪ To persuade others to act.
▪ To create an organizational memory (as part of a knowledge management system).
▪ Data visualization
▪ the use of visual representations to explore, make sense of, and communicate data
▪ Which chart should you use?
▪ Visual analytics is a recently coined term that is often used loosely to mean nothing more than information visualization. What is meant by visual analytics is the combi- nation of visualization and predictive analytics.
▪ Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that the information can be digested at a single glance and easily drilled in and further explored.
▪ 1. Monitoring: Graphical, abstracted data to monitor key performance metrics.
▪ 2. Analysis: Summarized dimensional data to analyze the root cause of problems.
▪ 3. Management: Detailed operational data that identify what actions to take to re-solve a problem
▪ Review the Chapter highlights
▪ Review the key terms
▪ Complete the weekly homework