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Chapter3_AnalyticsDataScienceArtificialIntellience.pdf

Chapter 3 Slides

▪ Opening Vignette

▪ SiriusXM

▪ Nature of Data

▪ DIWK

▪ Data source reliability

▪ Data content accuracy

▪ Data accessibility

▪ Data security and privacy

▪ Data richness

▪ Data consistency

▪ Data currency

▪ Data granularity

▪ Data validity

▪ Data relevancy

▪ Unstructured

▪ Structured

▪ Categorical

▪ Numerical

▪ Data preprocessing steps – Figure 3.3

▪ Regression

▪ 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

▪ Line

▪ Bar

▪ Pie

▪ Scatter

▪ Histogram

▪ Gantt

▪ Pert

▪ Geographic

▪ 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.

▪ Storytelling

▪ 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