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Week 2 Assignment

Chapter 3 –discussion question #1-4 & exercise 12 

Chapter 4 – discussion question #1-5 & Case pg. 243 

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The Target story answer the two case questions on page 244 integrating concepts and examples from that case. 

The above assignments should be submitted in one-word document.Include an APA cover page and a reference page. 

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

Business 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

Chapter 4 Slides

Opening Example /Data Mining Concepts

 Opening Vignette

 Miami-Dade Police Dept.

 Data Mining concepts

 discovering or “mining” knowledge from large amounts of data.

Data mining applications

 Customer service

 Banking

 Retailing and logistics

 Manufacturing and production

 Brokerage

 Insurance

 Government and defense

 Travel

 Healthcare

Data mining process

 CRISP-DM SEMMA

Data Mining Methods

 Classification

 Estimating the True Accuracy of Classification Models

Classification Techniques

Decision tree analysis.

Statistical analysis.

Neural networks.

Case-based reasoning.

Bayesian classifiers.

Genetic algorithms.

Rough sets.

Data Mining software tools

Data mining privacy issues, myths, and blunders  Target Story

Wrap Up

 Review the Chapter highlights

 Review the key terms

 Complete the weekly homework

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