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Data Analytics > ch.1 > Flashcards

ch.1 Flashcards

1
Q

3 elements spurred recent explosive growth in the use of analytical methods in business applications

A
  1. The tracking and storing of large amounts of data.
  2. Methodological developments to extract knowledge from data.
  3. An explosion in computing power.
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2
Q

Strategic decisions

A

involve higher-level issues concerned with the overall direction of the organization.
Define the organization’s overall goals and aspirations for the future.

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3
Q

Tactical decisions

A

concern how the organization should achieve the goals and objectives set by its strategy.
Usually the responsibility of midlevel management.

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4
Q

Operational decisions

A

affect how the firm is run from day to day.
The domain of operations managers, who are the closest to the customer.

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5
Q

5 steps that define the decision-making process

A
  1. Identify and define the problem.
  2. Determine the criteria that will be used to evaluate alternative solutions.
  3. Determine the set of alternative solutions.
  4. Evaluate the alternatives.
  5. Choose an alternative.
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6
Q

Rules of thumb

A

ex: you know that you will need to double the number of employees. You know it id the right decision, because it follows a pattern

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7
Q

What is business analytics

A

analytics is the scientific process of transforming data into insight for making better decisions.

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8
Q

The use of data-driven or fact-based decision making is often seen as more objective than other decision-making alternatives

T/F

A

T

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9
Q

Tools of business analytics can aid in decision making by

A

creating insights from data,
improving our ability to forecast for planning more accurately,
helping us quantify risk, and
yielding better alternatives through analysis and optimization.

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10
Q

Descriptive. analytics

A

encompasses the set of techniques that describes what has happened in the past

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11
Q

which ones do descriptive analytics techniques include

A

data queries
reports
descriptive statistics
data visualization (including data dashboards)
unsupervised learning techniques from data mining
basic spreadsheet models

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12
Q

A data query

A

requests information with certain characteristics from a database.

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13
Q

A report

A

resulting from a query may include descriptive statistics and data visualizations to find patterns or relationships in a large database

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14
Q

Data dashboards

A

dashboards are collections of tables, charts, maps, and summary statistics updated as new data becomes available.

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15
Q

Data mining

A

unsupervised learning techniques.

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16
Q

An unsupervised learning technique

A

a descriptive method that seeks to identify patterns in different types of data that are based on notions of
similarity (cluster analysis)
correlation (association rules)

17
Q

Predictive analytics

A

consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another.
Predictive analytics includes
linear regression and time series analysis,
data mining supervised learning techniques
A predictive model provides a forecast or prediction, not a decision.

18
Q

Supervised learning techniques

A

techniques use past data to find patterns or relationships among data elements in a large database.

19
Q

simulation

A

which involves using probability and statistics to construct a computer model to study the impact of uncertainty on a decision

20
Q

Prescriptive analytics

A

indicates a course of action to take.
A prescriptive model is a predictive model combined with a rule.
Prescriptive models that rely on a rule or set of rules are often called rule-based models.

21
Q

Examples of prescriptive analytics

A
  • portfolio models in finance,
  • supply network design models in operations, and
  • price-markdown models in retailing.
22
Q

optimization models

A

Models that give the best decision subject to the constraints of the situation

23
Q

Simulation optimization

A

combines probability and statistics to model uncertainty with optimization techniques and find good decisions in highly complex and uncertain settings.

24
Q

Decision analysis

A

used to develop an optimal strategy when a decision maker faces several decision alternatives and an uncertain set of future events

25
Q

Utility theory

A

is a branch of decision analysis that assigns values to outcomes based on the decision maker’s attitude toward risk.

26
Q

Big data

A

any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software.
IBM describes the phenomenon of big data through the four Vs:
Volume
Velocity
Variety
Veracity

27
Q

Artificial intelligence (AI)

A

uses big data and computers to make decisions that would have required human intelligence in the past.

28
Q

Applications of AI include facial recognition for security checkpoints and self-driving vehicles.

A

T/F

29
Q

Data scientists

A

analysts trained in computer science and statistics who know how to process and analyze massive amounts of data

30
Q

The complexities of big data have decreased the demand for analysts
T/F

A

False

31
Q

Data Ethics

A

The set of principles and processes that guide the ethical collection, processing, analysis, use and application of data having an effect on human lives and society.

What is right, what is fair, what is just.

32
Q

Data Protection Law GDPR

A

The General Data Protection Regulation is an EU law that came into effects in May 2018.

The GDPR requires organization handling personal data according to its six data principles.

  1. it is processed fairly, lawfully and transparently.
  2. it is collected and processed for specific reasons and stored for specific periods of time, and that it is not used for reasons beyond its original purpose.
  3. only the data necessary for the purpose it is intended is collected, and not more.
  4. it is accurate and that reasonable steps are taken to ensure it remains accurate.
  5. it is kept in a form that allows individuals to be identified only as long as is necessary.
  6. it is kept securely and protected from unlawful access, accidental loss or damage
33
Q

PIPEDA

A

PIPEDAapplies to private-sector organizations across Canada that collect, use or disclose personal information in the course of acommercial activity.

34
Q

PIPEDA does not apply

A
  • Personal information handled by federal government
  • An individual’s collection, use or disclosure of personal information strictly for personal purposes
  • An individual’s collection, use or disclosure of personal information strictly for personal purposes journalistic, artistic or literary purposes
  • Business contact information used or disclosed solely for the purpose of communicating with that person in relation

Data Analytics (1 decks)

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