BADM 211
The term "business analytics" may be best defined as _____________________. -
answer The practice and art utilizing quantitative data to aid in decision-making.
Typically includes BI as well as sophisticated data analysis and data mining algorithms
to explain relationships and predict new records.
The term "business intelligence" may be best defined as ____________. - answer
Utilizing data visualization and reporting for understanding "what happened and what is
happening". Includes the use of dashboards. Used to refer to data visualization and
reporting.
Data mining adds __________ to data visualization and exploratory analyses. - answer
Data mining goes beyond counts, descriptive techniques and reporting. Introduces data
visualization. Includes statistical and machine learning methods that inform decision-
making. Used for prediction. Adds power and automaticity.
Other names for Data Mining - answerpredictive analytics, predictive modeling, and
machine learning. Somewhere between classical statistics and computing.
Machine learning refers to algorithms that __________________. - answerAlgorithms
that learn directly from data in a layered or iterative fashion. A sample machine learning
algorithm is k-nearest-neighbors algorithm that would be used in place of a linear
regression model.
Which of the following is not a characterization of Big Data? - answerBig Data is a
relative term that is used to describe large amounts of data. It is usually characterized
by VOLUME-amount VELOCITY-speed at which new data is generated VARIETY-
different types of data and VERACITY-data is being generated organically
Which of the following is not in the list of skills for a data scientist? - answerData
scientists have emerged to help make sense of Big Data. Data science is a mix of skills
in the areas of statistics, machine learning, math, programming, business, and IT. The
must be able to program (most say), most DO NOT actually spend their time working
with large data that is the work of IT programmers Data scientists usually come into play
after that.
A categorical variable may be best defined as a ______________. - answerA variable
that takes on one of several fixed values, for example, a flight could be on-time,
delayed, or canceled. Places information into different categories.
, A predictor may be best defined as a(n) ______________. - answerAKA Input Variable,
feature input variable, independent variable, field. A variable, usually denoted by X,
used as an input into a predictive model.
The conditional probability that event A will occur given that event B has already
occurred may be written as ________________. - answerP(A|B)
Which of the following is not a term for "observation"? - answerAn observation is the
unit of analysis on which measurements are taken.
AKA instance, sample, example, case, record, pattern, or row.
Which of the following is NOT an example of classification? - answerClassification is
perhaps the most basic form of data analysis. Examples include responding or not
responding, defaulting or not defaulting, normal or fraudulent, available for service or
unavailable, illness can be recovered still ill or dead. Understand where the
classification is unknown with the goal of predicting the classification.
Which of the following is an example of prediction? - answerPrediction (aka
estimation/regression) is similar to classification except that you are trying to predict the
value of a numerical variable (amount of purchase) rather than a class (purchaser or
non-purchaser). May be used for continuous and categorical data.
Online recommendation systems use an approach known as _______________. -
answerCollaborative Filtering which combines the individual users buying trends, tastes,
preferences, and other measurable information to make individualized predictions.
Generates a "what goes with what" not a broad approach.
The performance of data mining algorithms is often improved by limiting variables and
by ______________. - answerData Reduction, Dimension Reduction and Clustering -
Grouping large groups of data into smaller homogeneous groups which allows you to
have a smaller focus within the dataset. Clustering may also help determine how to
reduce the number of cases. Dimension Reduction may be a good first step in order to
improve predictive power, manageability and interpretability.
Affinity analysis is used by grocery stores to determine effective ____________. -
answerAKA Association rules are designed to help find general associations or patterns
between items in large databases. Grocery stores can use such information for
production placement. They can use the rules for weekly promotional offers or for
bundling products.
Which of the following represents dimension reduction? - answerReducing the number
of variables. Usually an initial step used before deploying data mining methods.
Cluster analysis is a method sometimes used for _______________. - answerReducing
the number of cases in very large data set.
The term "business analytics" may be best defined as _____________________. -
answer The practice and art utilizing quantitative data to aid in decision-making.
Typically includes BI as well as sophisticated data analysis and data mining algorithms
to explain relationships and predict new records.
The term "business intelligence" may be best defined as ____________. - answer
Utilizing data visualization and reporting for understanding "what happened and what is
happening". Includes the use of dashboards. Used to refer to data visualization and
reporting.
Data mining adds __________ to data visualization and exploratory analyses. - answer
Data mining goes beyond counts, descriptive techniques and reporting. Introduces data
visualization. Includes statistical and machine learning methods that inform decision-
making. Used for prediction. Adds power and automaticity.
Other names for Data Mining - answerpredictive analytics, predictive modeling, and
machine learning. Somewhere between classical statistics and computing.
Machine learning refers to algorithms that __________________. - answerAlgorithms
that learn directly from data in a layered or iterative fashion. A sample machine learning
algorithm is k-nearest-neighbors algorithm that would be used in place of a linear
regression model.
Which of the following is not a characterization of Big Data? - answerBig Data is a
relative term that is used to describe large amounts of data. It is usually characterized
by VOLUME-amount VELOCITY-speed at which new data is generated VARIETY-
different types of data and VERACITY-data is being generated organically
Which of the following is not in the list of skills for a data scientist? - answerData
scientists have emerged to help make sense of Big Data. Data science is a mix of skills
in the areas of statistics, machine learning, math, programming, business, and IT. The
must be able to program (most say), most DO NOT actually spend their time working
with large data that is the work of IT programmers Data scientists usually come into play
after that.
A categorical variable may be best defined as a ______________. - answerA variable
that takes on one of several fixed values, for example, a flight could be on-time,
delayed, or canceled. Places information into different categories.
, A predictor may be best defined as a(n) ______________. - answerAKA Input Variable,
feature input variable, independent variable, field. A variable, usually denoted by X,
used as an input into a predictive model.
The conditional probability that event A will occur given that event B has already
occurred may be written as ________________. - answerP(A|B)
Which of the following is not a term for "observation"? - answerAn observation is the
unit of analysis on which measurements are taken.
AKA instance, sample, example, case, record, pattern, or row.
Which of the following is NOT an example of classification? - answerClassification is
perhaps the most basic form of data analysis. Examples include responding or not
responding, defaulting or not defaulting, normal or fraudulent, available for service or
unavailable, illness can be recovered still ill or dead. Understand where the
classification is unknown with the goal of predicting the classification.
Which of the following is an example of prediction? - answerPrediction (aka
estimation/regression) is similar to classification except that you are trying to predict the
value of a numerical variable (amount of purchase) rather than a class (purchaser or
non-purchaser). May be used for continuous and categorical data.
Online recommendation systems use an approach known as _______________. -
answerCollaborative Filtering which combines the individual users buying trends, tastes,
preferences, and other measurable information to make individualized predictions.
Generates a "what goes with what" not a broad approach.
The performance of data mining algorithms is often improved by limiting variables and
by ______________. - answerData Reduction, Dimension Reduction and Clustering -
Grouping large groups of data into smaller homogeneous groups which allows you to
have a smaller focus within the dataset. Clustering may also help determine how to
reduce the number of cases. Dimension Reduction may be a good first step in order to
improve predictive power, manageability and interpretability.
Affinity analysis is used by grocery stores to determine effective ____________. -
answerAKA Association rules are designed to help find general associations or patterns
between items in large databases. Grocery stores can use such information for
production placement. They can use the rules for weekly promotional offers or for
bundling products.
Which of the following represents dimension reduction? - answerReducing the number
of variables. Usually an initial step used before deploying data mining methods.
Cluster analysis is a method sometimes used for _______________. - answerReducing
the number of cases in very large data set.
