Exam Chapter 1. What is business
Analytics? (Correct Answers provided)
Business Analytics - Answer-- the practice and art of bringing quantitative data to bear
on decision making.
Business Intelligence - Answer-- Data visualization and reporting for understanding
"what happened and what is happening."
-- Done by the use of charts, tables, and dashboards to display, examine, and explore
data.
Data mining - Answer-- Business analytics methods that go beyond counts, descriptive
techniques, and reporting, and methods based on business rules.
Machine learning - Answer-- refers to algorithms that learn directly from data, especially
local patterns, often in layered or iterative fashion.
Statistical models - Answer-- refers to methods that apply globe structure to the data.
Overfitting - Answer-- where a model is fit so closely to the available sample of data that
it describes not merely structural characteristics of that data but random peculiarities as
well.
Big data - Answer-- data today are big by reference to the past, and to the methods and
devices available to deal with them.
Challenges with big data - Answer-- Often characterized by the four V's
-- Volume
-- Velocity
-- Variety
-- Veracity
Challenges with big data
- Volume - Answer-- Refers to the amount of data
Challenges with big data
- Velocity - Answer-- Refers to the flow rate - the speed at which it is being generated
and changed.
Challenges with big data
- Variety - Answer-- Refers to the different types of data being generated (currency,
dates, numbers, text, ect.).
, Challenges with big data
- Veracity - Answer-- Refers to the fact that data is being generated by organic
distributed processes (e.g., millions of people signing up for services for free
downloads) and not subject to the controls or quality checks that apply to data collected
for a study.
Data science - Answer-- A mix of skills in the areas of statistics, machine learning, math,
programming, business, and IT.
Algorithm - Answer-- A specific procedure used to implement a particular data mining
technique: classification tree, discrimination analysis, and the like.
Confidence - Answer-- A performance measure in association rules of the type "IF A
and B are purchased, THEN C is also purchased." Confidence is the conditional
probability that C will be purchased IF A and B are purchased.
-- Also has a broader meaning in statistics (Confidence interval), concerning the degree
of error in an estimate that results from selecting one sample as opposed to another.
Holdout Data - Answer-- A sample of data not used in fitting a model, but instead used
to assess the performance of that model. This book uses the term "Validation set and
test set."
Observation - Answer-- The unit of analysis on which the measurements are taken.
-- AKA: instance, sample, example, case, record*, pattern, or row.
Row - Answer-- In spreadsheets, each row typically represents a record.
Column - Answer-- In spreadsheets, each column represents a variable.
Predictor - Answer-- A variable, usually denoted by X, used as an input into a predictive
model.
-- AKA: feature, input variable*, independent variable, or from a database perspective, a
field.
Response - Answer-- A variable, usually denoted by Y, which is the variable being
predicted in supervised learning.
-- AKA: dependent variable, output variable, target variable, or outcome variable.
Predictive Analytics - Answer-- Classification
- Prediction
- Association Rules (affinity analysis and collaborative filleting)
- Clustering
Supervised Learning - Answer-- Goal: predict a single "target" or "outcome" variable
- Methods: Prediction and classification
Analytics? (Correct Answers provided)
Business Analytics - Answer-- the practice and art of bringing quantitative data to bear
on decision making.
Business Intelligence - Answer-- Data visualization and reporting for understanding
"what happened and what is happening."
-- Done by the use of charts, tables, and dashboards to display, examine, and explore
data.
Data mining - Answer-- Business analytics methods that go beyond counts, descriptive
techniques, and reporting, and methods based on business rules.
Machine learning - Answer-- refers to algorithms that learn directly from data, especially
local patterns, often in layered or iterative fashion.
Statistical models - Answer-- refers to methods that apply globe structure to the data.
Overfitting - Answer-- where a model is fit so closely to the available sample of data that
it describes not merely structural characteristics of that data but random peculiarities as
well.
Big data - Answer-- data today are big by reference to the past, and to the methods and
devices available to deal with them.
Challenges with big data - Answer-- Often characterized by the four V's
-- Volume
-- Velocity
-- Variety
-- Veracity
Challenges with big data
- Volume - Answer-- Refers to the amount of data
Challenges with big data
- Velocity - Answer-- Refers to the flow rate - the speed at which it is being generated
and changed.
Challenges with big data
- Variety - Answer-- Refers to the different types of data being generated (currency,
dates, numbers, text, ect.).
, Challenges with big data
- Veracity - Answer-- Refers to the fact that data is being generated by organic
distributed processes (e.g., millions of people signing up for services for free
downloads) and not subject to the controls or quality checks that apply to data collected
for a study.
Data science - Answer-- A mix of skills in the areas of statistics, machine learning, math,
programming, business, and IT.
Algorithm - Answer-- A specific procedure used to implement a particular data mining
technique: classification tree, discrimination analysis, and the like.
Confidence - Answer-- A performance measure in association rules of the type "IF A
and B are purchased, THEN C is also purchased." Confidence is the conditional
probability that C will be purchased IF A and B are purchased.
-- Also has a broader meaning in statistics (Confidence interval), concerning the degree
of error in an estimate that results from selecting one sample as opposed to another.
Holdout Data - Answer-- A sample of data not used in fitting a model, but instead used
to assess the performance of that model. This book uses the term "Validation set and
test set."
Observation - Answer-- The unit of analysis on which the measurements are taken.
-- AKA: instance, sample, example, case, record*, pattern, or row.
Row - Answer-- In spreadsheets, each row typically represents a record.
Column - Answer-- In spreadsheets, each column represents a variable.
Predictor - Answer-- A variable, usually denoted by X, used as an input into a predictive
model.
-- AKA: feature, input variable*, independent variable, or from a database perspective, a
field.
Response - Answer-- A variable, usually denoted by Y, which is the variable being
predicted in supervised learning.
-- AKA: dependent variable, output variable, target variable, or outcome variable.
Predictive Analytics - Answer-- Classification
- Prediction
- Association Rules (affinity analysis and collaborative filleting)
- Clustering
Supervised Learning - Answer-- Goal: predict a single "target" or "outcome" variable
- Methods: Prediction and classification
