Exam 2 - Chapter 4

Question 1

Guest speaker

Answer 1

2 questions

Question 2

THE ROLE OF THE ANALYST

- Transform data into ______

Answer 2

Transform data into Information

Question 3

THE ROLE OF THE ANALYST

- Master the ______ and _____ needed to provide insights from data

Answer 3

Master the technologies and tools needed to provide insights from data

Question 4

THE ROLE OF THE ANALYST

- _____ a deep understanding of the ________ _______ and _______

Answer 4

Maintain a deep understanding of the business environment and requirements

Question 5

THE ROLE OF THE ANALYST

- _____ ______ with data engineers and provide them with _______ and ____ ___

Answer 5

Maintain relationships with data engineers and provide them with requirements and data needs

Question 6

THE ROLE OF THE ANALYST

- Be able to select the _______ _______ method based on ______ _____ and ______ ______

Answer 6

Be able to select the appropriate analytical method based on business objectives and data structures

Question 7

pyramid of the role of the analyst

Answer 7

top - the business - receiver of information and knowledge
middle - the analysts - providers of methodology
bottom - the data warehouse - provider of data

Question 8

Requirements for analysts can be summed up into 3 major competencies:

Answer 8

1. Business Competencies
2. Method Competencies
3. Data Competencies

Question 9

Business Competencies



Answer 9

Business competencies

 Understand the business requirements and desired insights

Question 10

Method Competencies



Answer 10

Method Competencies

Understand and be able to apply the correct analytical tools and procedures

Question 11

Data Competencies



Answer 11

Data Competencies

Understand the data you have, the data you need, and how to bridge the gap

Question 12

SELECTING THE ANALYTICAL METHOD

Question 1-3

Answer 12

Question 1: Determine with the process owner whether the quantitative analytical competencies or the data manager and report developer competencies are required.
Question 2: Determine whether hypothesis‐driven analytics, or data‐driven analytics can be expected to render the best decision support.
Question 3: Determine whether the data‐driven method has the objective of examining the correlation between one given dependent variable and a large number of other variables, or whether the objective is to identify different kinds of structures in data.

Question 13

EXAMPLES OF DATA – ORIENTED COMPETENCIES

Answer 13

Ad Hoc Reports
Manual Reports
Automated Reports
      On Demand
      Event Driven
Self-service Reporting

Question 14

HYPOTHESIS TESTING (STATISTICAL METHODS) – EXAMPLE QUESTIONS

Answer 14

Comparing data to a benchmark
Comparing data to each other
Are there specific factors about the line that impact the average fill level of the bottles

Question 15

DATA MINING STEPS WITH TARGET VARIABLES

Answer 15

Step 1: Create the models
Step 2: Evaluate the models
Step 3: Use the models on new data

Question 16

Classifies objects

Answer 16

Classifies objects into a set of pre-specified object classes (or categories) based on the values of relevant object attributes (features) and objects’ class labels

Question 17

BENEFITS OF CLASSIFICATION

Answer 17

Identifying the class by a single or a small number of data attributes (e.g., gender, age) is manageable by human decision makers, but not when the number of attributes or the number of instances is large.
Estimating/predicting the class or category of action recipient supports time and cost-effective decision making

Question 18

MOTIVATING BUSINESS QUESTIONS, COSTS AND BENEFITS

- How do we identify mobile phone service CUSTOMERS who are likely to churn (switch to another courier)?

Answer 18

MOTIVATING BUSINESS QUESTIONS, COSTS AND BENEFITS
Churn or not: classes of customers
Which customers: identified by customers’ attribute-value information – e.g., age, income, gender, services subscribed, service utilization, etc.

Question 19

MOTIVATING BUSINESS QUESTIONS, COSTS AND BENEFITS

- so what?

Answer 19

Potential actions – increase or decrease customer service for customers likely to churn
Costs – increased service cost or loss of loyal customers
Benefits - reduced churn rate or service cost

Question 20

DATA MINING WITH NO TARGET VARIABLES

Answer 20

Data reduction models
    Principal Component Analysis (PCA)
Clustering models
    K-Means
Market basket models
    Association Rule Mining

Question 21

An object (e.g., a customer) has a list of variables (e.g., attributes of a customer such as age, spending, gender etc.)

Answer 21

An object (e.g., a customer) has a list of variables (e.g., attributes of a customer such as age, spending, gender etc.)

Question 22

When measuring similarity between objects we measure similarity between _____ of objects.

Answer 22

When measuring similarity between objects we measure similarity between variables of objects.

Question 23

object

- We use distance function to ____ [1 sentence] _____ [2 sentence]

Answer 23

We use distance function to measure dissimilarity between variables. Thus the further the distance between any two objects the more dissimilar they are.

Question 24

A distance matrix :

Answer 24

A distance matrix can be created with objects as indexes and distance between objects as elements.

Question 25

WHY DO WE CARE?

Answer 25

Clusters are groups of similar entities They make exhibit very different behavior This may give us the ability to train and apply classification / regression models to separate clusters an increase overall accuracy