Data Analytics Flashcards

1
Q

Six-step data analysis process.

A

(1) Ask, (2) Prepare, (3) Process, (4) Analyze, (5) Share, (6) Act

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2
Q

The Ask step of the data analysis process.

A

Business challenge, objective, or question. Define the problem. Focus on the actual problem and avoid any distractions. Collaborate with stakeholders. Take a step back and see the whole situation in context.

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3
Q

The Prepare step of the data analysis process.

A

Data generation, collection, storage, and data management.

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4
Q

The Process step of the data analysis process.

A

Data cleaning and data integrity

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5
Q

The Analyze step of the data analysis process.

A

Data exploration, visualization, and analysis. Find patterns, relationships, and trends.

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6
Q

The Share step of the data analysis process.

A

Communicating and interpreting results

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7
Q

The Act step of the data analysis process.

A

Putting insights to work to solve the problem

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8
Q

EMC’s Data Analysis Process: Six-steps

A

(1) Discovery, (2) Pre-processing data, (3) Model planning, (4) Model Building, (5) Communicate results, (6) Operationalize

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9
Q

SAS’s Interactive Process: Seven-steps

A

(1) Ask, (2) Prepare, (3) Explore, (4) Model, (5) Implement, (6) Act, (7) Evaluate

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10
Q

Project-based Data Analytics Process: Five-steps

A

(1) Identifying the Problem, (2) Design data requirements, (3) Pre-processing data, (4) Performing data analysis, (5) Visualizing data

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11
Q

Big Data Analytics Process: Nine-steps

A

(1) Business case evaluation, (2) Data identification, (3) Data acquisition and filtering, (4) Data extraction, (5) Data validation and cleaning, (6) Data aggregation and representation, (7) Data analysis, (8) Data visualization, (9) Utilization of analysis results.

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12
Q

Good questions to ask

A

What is the root cause of the problem?
How do I define success for this project?
What kind of results are needed?
Who will be informed?
Am I answering the question being asked?
How quickly does a decision need to be made?

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13
Q

Data analysis

A

The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making

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14
Q

The 5 key aspects to analytical thinking

A

Visualization, Strategy, Problem-Orientation, Correlation, and Big-picture and detail-oriented thinking.

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15
Q

Gap analysis

A

Lets you examine and evaluate how a process works currently in order to get where you want to be in the future.

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16
Q

What is the life cycle of data?

A

Plan, capture, manage, analyze, archive and destroy.

17
Q

Plan stage of data life cycle

A

Decide what kind of data is needed, how it will be managed, and who will be responsible for it.

18
Q

Capture stage of data life cycle

A

Collect or bring in data from a variety of different sources.

19
Q

Manage stage of data life cycle

A

Care for and maintain the data. This includes determining how and where it is stored and the tools used to do so.

20
Q

Analyze stage of data life cycle

A

Use the data to solve problems, make decisions, and support business goals.

21
Q

Archive stage of data life cycle

A

Keep relevant data stored for long-term and future reference.

22
Q

Destroy stage of data life cycle

A

Remove data from storage and delete any shared copies of the data.

23
Q

Common problem types

A

(1) Making predictions, (2) Categorizing things, (3) Spotting something unusual, (4) Identifying themes (5) Discovering connections, (6) Finding patterns

24
Q

Making predictions

A

using data to make an informed decision about how things may be in the future

25
Q

Categorizing things

A

assigning information to different groups or clusters based on common features.

26
Q

Spotting something unusual

A

identify data that is different from the norm

27
Q

Identifying themes

A

Grouping categorized information into broader concepts

28
Q

Discovering connections

A

Finding similar challenges faced by different entities, and combining data and insights to address them

29
Q

Finding patterns

A

using historical data to understand what happened in the past and is therefore likely to happen again.

30
Q

SMART question

A

Specific, Measurable, Action-Oriented, Relevant, and Time-bound

31
Q

Specific question

A

Specific questions are simple, significant and focused on a single topic or a few closely related ideas. Does the question address the problem? Does it have context? Will it uncover a lot of the information you need?

32
Q

Measurable question

A

Measurable questions can be quantified and assessed.

33
Q

Action-oriented question

A

Action-oriented questions encourage change. Will the answers provide information that helps you devise some type of plan?

34
Q

Relevant question

A

Relevant questions matter, are important and have significance to the problem you’re trying to solve

35
Q

Time-bound question

A

Time-bound questions specify the time to be studied.

36
Q
A