PREDICT 402DL – Introduction to Predictive Analytics and Data Collection

I teach this course at Northwestern University. You may also refer to for complete details if you are a student. These are posted for other analysts so that they may become interested in learning more about the MSPA (Master of Science in Predictive Analytics) program in Northwestern.

The first three chapters provides a working definition of analytics, states the importance of analytics across the orgnization for competing in business, provides tools to assesss common attributes of analytically competitive businesses, and helps rank the stages of analytic competition

In chapters 4 and 5 the book helps (1) identify analytic techniques used to analyze internal busienss processes, (2) select the appropriate ananlytic applications for a given internal businesss processes, (3) identify analytic techniques used to analyze external business processes, and (4) select the appropriate analytic applications for a given external businesss processs.

In the third part of the book, chapters 6-9 provides tools to assess the analytic capabilities of an organization, methods to distinguish at what stage an organization is in analytic competition, walks through how organizations walk through various stages in becoming an analytic competitor, compares the roles of analytic executives, analytic professionals, and analytic amateurs, explains the six elements of BI architecture and finally specifies the relationship among the six elements of BI architecture.

In the first two chapters, the authors walk us through

• Organize the components of the business analytics model.
• Assess the role of data in the business analytics model.
• Classify the different types of links between business analytics and strategy.
• Recognize the types of analytic information available to inform the three disciplines outlined.

In Chapter 3,

the discussions are around

• Compare and contrast lag and lead information.
• Distinguish how lead versus lag information can be used in the development and management of a new
business process.
• Distinguish how lead versus lag information can be used to optimize existing processes.
• Assess each of the business processes listed on the three disciplines.
• Classify key performance indicators into their suggested business functions.

In chapter 4, the topics are covered on

• Apply a strategy mapping process to match analytic techniques to information requirements.
• Explain the difference between data, information, and knowledge.
• Evaluate the importance of each of the analyst competencies.
• Evaluate the advantages and disadvantages of different types of analytic reports.
• Formulate business examples of when the use of data-driven versus data mining versus explorative analytic
methods would be appropriate.
• Compose effective business requirement documents.

In chapters 5 and 6, the concepts covered are:

• Explain the relationship between components in a data warehouse.
• Identify business systems that may generate data.
• Organize the steps in the extraction transformation loading (ETL) process.
• Propose potential sources of poor quality data.
• Evaluate the effects of poor quality data.
• Identify potential sources of data in an organization.
• Assess the relationship between the usability and the availability of data.

Interestingly, to have better organized approach, we cover:

• Evaluate the benefits and limitations of data collection methodologies.
• Evaluate the benefits and limitations of data collection modalities.
• Apply the fundamentals of survey design to develop an effective survey.

from Chapters 5,7,8


• Explain the importance of sampling in analytics.
• Compare and contrast different sampling techniques.
• Assess the impact of missing data on the analytic process.
• Appraise the benefits and limitations of different data imputation techniques.

are covered from Chapters 3,4,6,and 10.

From Summer 2012, we have this additional books that are added to support the topics on visualization.  Chapters 1-3, 5, and 6

Now You See It: Simple Visualization Techniques for Quantitative Analysis