Week 2 Assignment

Chapter 3 Slides

Opening Example /Nature of Data

 Opening Vignette

 SiriusXM

 Nature of Data

 DIWK

Analytics readiness

 Data source reliability

 Data content accuracy

 Data accessibility

 Data security and privacy

 Data richness

 Data consistency

 Data currency

 Data granularity

 Data validity

 Data relevancy

Taxonomy of data

 Unstructured

 Structured

 Categorical

 Numerical

Art and Science of Data preprocessing

 Data preprocessing steps – Figure 3.3

Statistical modeling for business analytics

 This

Regression modeling for inferential
statistics
 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

reporting /Data Visualization

 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

Different types of charts

 Line

 Bar

 Pie

 Scatter

 Histogram

 Gantt

 Pert

 Geographic

 Which chart should you use?

Emergence of Visual Analytics/ Information
Dashboards
 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

Wrap Up

 Review the Chapter highlights

 Review the key terms

 Complete the weekly homework