Booz Selection

Question 1

What is the key question for 1. Describe?

Answer 1

How do I develop an understanding of the content of my data?

Question 2

What is the key question for 1. Describe | Processing?

Answer 2

How do I clean and separate my data?

Question 3

What is the key question for 1. Describe | Processing | Filtering?

Answer 3

How do I identify data based on its absolute or relative values?

Question 4

What is the key question for 1. Describe | Processing | Imputation?

Answer 4

How do I fill in missing values in my data?

Question 5

What is the key question for 1. Describe | Processing | Dimensionality Reduction?

Answer 5

How do I reduce the number of dimensions in my data?

Question 6

What is the key question for 1. Describe | Processing | Normalization & Transformation?

Answer 6

How do I reconcile duplication representations in the data?

Question 7

What is the key question for 1. Describe | Processing | Feature Extraction?

Answer 7

Really depends on the domain of the information. Variety of methods.

Question 8

For 1. Describe | Processing | Filtering, If you want to add or remove data based on its value, start with:

Answer 8

Relational algebra projection and selection

Question 9

For 1. Describe | Processing | Filtering, If early results are uninformative and duplicative, start with:

Answer 9

Outlier removal, Exponential smoothing, Gaussian filter, Median filter

Question 10

For 1. Describe | Processing | Imputation, If you want to generate values from other observations in your dataset, start with:

Answer 10

Random sampling, Markov Chain Monte Carlo (MC)

Question 11

For 1. Describe | Processing | Imputation, If you want to generate values without using other observations in your dataset, start with:

Answer 11

Mean, Statistical distributions, Regression models

Question 12

For 1. Describe | Processing | Dimensionality Reduction, If you need to determine whether there is multi-dimensional correlation, start with:

Answer 12

PCA and other factor analysis

Question 13

For 1. Describe | Processing | Dimensionality Reduction, If you can represent individual observations by membership in a group, start with:

Answer 13

K-means clustering, Canopy clustering

Question 14

For 1. Describe | Processing | Dimensionality Reduction, If you have unstructured text data, start with:

Answer 14

Term Frequency/Inverse Document Frequency (TF IDF)

Question 15

For 1. Describe | Processing | Dimensionality Reduction, If you have a variable number of features but your algorithm requires a fixed number, start with:

Answer 15

Feature hashing

Question 16

For 1. Describe | Processing | Dimensionality Reduction, If you are not sure which features are the most important, start with:

Answer 16

Wrapper methods, Sensitivity analysis

Question 17

For 1. Describe | Processing | Dimensionality Reduction, If you need to facilitate understanding of the probability distribution of the space, start with:

Answer 17

Self organizing maps

Question 18

For 1. Describe | Processing | Normalization & Transformation, If you suspect duplicate data elements, start with:

Answer 18

Deduplication

Question 19

For 1. Describe | Processing | Normalization & Transformation, If you want your data to fall within a specified range, start with:

Answer 19

Normalization

Question 20

For 1. Describe | Processing | Normalization & Transformation, If your data is stored in a binary format, start with:

Answer 20

Format Conversion

Question 21

For 1. Describe | Processing | Normalization & Transformation, If you are operating in frequency space, start with:

Answer 21

Fast Fourier Transform (FFT), Discrete wavelet transform

Question 22

For 1. Describe | Processing | Normalization & Transformation, If you are operating in Euclidian space, start with:

Answer 22

Coordinate transform

Question 23

What is the key question for 1. Describe | Aggregation?

Answer 23

How do I collect and summarize my data?

Question 24

For 1. Describe | Aggregation, If you are unfamiliar with the dataset, start with:

Answer 24

basic statistics: Count, Mean, Standard deviation, Range, Scatter Plots, Box plots

Question 25

For 1. Describe | Aggregation, If your approach assumes the data follows a distribution, start with:

Answer 25

Distribution fitting

Question 26

For 1. Describe | Aggregation, If you want to understand all the information available on an entity, start with:

Answer 26

“Baseball card” aggregation

Question 27

What is the key question for 1. Describe | Enrichment?

Answer 27

How do I add new information to my data?

Question 28

For 1. Describe | Enrichment, If you need to keep track of source information or other user-defined parameters, start with:

Answer 28

Annotation

Question 29

For 1. Describe | Enrichment, If you often process certain data fields together or use one field to compute the value of another, start with:

Answer 29

Relational algebra rename, Feature addition (e.g., Geography, Technology, Weather)

Question 30

What is the key question for 2. Discover?

Answer 30

What are the key relationships in the data?

Question 31

What is the key question for 2. Discover | Clustering?

Answer 31

How do I segment the data to find natural groupings?

Question 32

For 2. Discover | Clustering, If you want an ordered set of clusters with variable precision, start with:

Answer 32

Hierarchical

Question 33

For 2. Discover | Clustering, ? If you have an unknown number of clusters, start with:

Answer 33

X-means, Canopy, Apriori

Question 34

For 2. Discover | Clustering, If you have text data, start with:

Answer 34

Topic modeling

Question 35

For 2. Discover | Clustering, If you have non-elliptical clusters, start with:

Answer 35

Fractal, DB Scan

Question 36

For 2. Discover | Clustering, If you want soft membership in the clusters, start with:

Answer 36

Gaussian mixture models

Question 37

For 2. Discover | Clustering, If you have an known number of clusters, start with:

Answer 37

K-means

Question 38

What is the key question for 2. Discover | Regression?

Answer 38

How do I determine which variables may be important?

Question 39

For 2. Discover | Regression, If your data has unknown structure, start with:

Answer 39

Tree-based methods

Question 40

For 2. Discover | Regression, If statistical measures of importance are needed, start with:

Answer 40

Generalized linear models

Question 41

For 2. Discover | Regression, If statistical measures of importance are not needed, start with:

Answer 41

Regression with shrinkage (e.g., LASSO, Elastic net), Stepwise regression

Question 42

What is the key question for 2. Discover | Hypothesis Testing?

Answer 42

How do I test ideas?

Question 43

For 2. Discover | Hypothesis Testing, If you want to compare two groups

Answer 43

T-test

Question 44

For 2. Discover | Hypothesis Testing, If you want to compare multiple groups

Answer 44

ANOVA

Question 45

What is the key question for 3. Predict?

Answer 45

What are the likely future outcomes?

Question 46

What is the key question for 3. Predict | Classification?

Answer 46

How do I predict group membership?

Question 47

For 3. Predict | Classification, If you have known dependent relationships between variables

Answer 47

Bayesian network

Question 48

For 3. Predict | Classification, If you are unsure of feature importance, start with:

Answer 48

Neural nets, Random forests, Deep learning

Question 49

For 3. Predict | Classification, If you require a highly transparent model, start with:

Answer 49

Decision trees

Question 50

For 3. Predict | Classification, If you have less than 20 data dimensions, start with:

Answer 50

K-nearest neighbors

Question 51

For 3. Predict | Classification, If you have a large dataset with an unknown classification signal, start with:

Answer 51

Naive bayes

Question 52

For 3. Predict | Classification, If you want to estimate an unobservable state based on observable variables, start with:

Answer 52

Hidden markov model

Question 53

For 3. Predict | Classification, If you don't know where else to begin, start with:

Answer 53

Support vector machines (SVM), Random forests

Question 54

What is the key question for 3. Predict | Regression?

Answer 54

How do I predict a future value?

Question 55

For 3. Predict | Regression, If the data structure is unknown, start with:

Answer 55

Tree-based methods

Question 56

For 3. Predict | Regression, If you require a highly transparent model, start with:

Answer 56

Generalized linear models

Question 57

For 3. Predict | Regression, If you have less than 20 data dimensions, start with:

Answer 57

K-nearest neighbors

Question 58

What is the key question for 3. Predict | Recommendation?

Answer 58

How do I predict relevant conditions?

Question 59

For 3. Predict | Recommendation, If you only have knowledge of how people interact with items, start with:

Answer 59

Collaborative filtering

Question 60

For 3. Predict | Recommendation, If you have a feature vector of item characteristics, start with:

Answer 60

Content-based methods

Question 61

For 3. Predict | Recommendation, If you only have knowledge of how items are connected to one another, start with:

Answer 61

Graph-based methods

Question 62

What is the key question for 4. Advise?

Answer 62

What course of action should I take?

Question 63

What is the key question for 4. Advise | Logical Reasoning?

Answer 63

How do I sort through different evidence?

Question 64

For 4. Advise | Logical Reasoning, If you have expert knowledge to capture

Answer 64

Expert systems

Question 65

For 4. Advise | Logical Reasoning, If you're looking for basic facts

Answer 65

Logical reasoning

Question 66

What is the key question for 4. Advise | Optimization?

Answer 66

How do I identify the best course of action when my objective can be expressed as a utility function?

Question 67

For 4. Advise | Optimization, If your problem is represented by a non-deterministic utility function, start with:

Answer 67

Stochastic search

Question 68

For 4. Advise | Optimization, If approximate solutions are acceptable, start with:

Answer 68

Genetic algorithms, Simulated annealing, Gradient search

Question 69

For 4. Advise | Optimization, If your problem is represented by a deterministic utility function, start with:

Answer 69

Linear programming, Integer programming, Non-linear programming

Question 70

For 4. Advise | Optimization, If you have limited resources to search with

Answer 70

Active learning

Question 71

For 4. Advise | Optimization, If you want to try multiple models

Answer 71

Ensemble learning

Question 72

What is the key question for 4. Advise | Simulation?

Answer 72

How do I characterize a system that does not have a closed-form representation?

Question 73

For 4. Advise | Simulation, If you must model discrete entities, start with:

Answer 73

Discrete event simulation (DES)

Question 74

For 4. Advise | Simulation, If there are a discrete set of possible states, start with:

Answer 74

Markov models

Question 75

For 4. Advise | Simulation, If there are actions and interactions among autonomous entities, start with:

Answer 75

Agent-based simulation

Question 76

For 4. Advise | Simulation, If you do not need to model discrete entities, start with:

Answer 76

Monte Carlo simulation

Question 77

For 4. Advise | Simulation, If you are modeling a complex system with feedback mechanisms between actions, start with:

Answer 77

Systems dynamics

Question 78

For 4. Advise | Simulation, If you require continuous tracking of system behavior, start with:

Answer 78

Activity-based simulation

Question 79

For 4. Advise | Simulation, If you already have an understanding of what factors govern the system, start with:

Answer 79

ODES, PDES

Question 80

For 4. Advise | Simulation, If you have imprecise categories

Answer 80

Fuzzy logic