Data Analytics ACCTCY 4328

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Description and Tags

Final Exam

25 Terms

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Big Data

datasets that are too large and complex for businesses’ existing systems to handle their traditional capabilities to capture store, manage, and analyze these data sets

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Volume

massive amount of data involved

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Velocity

data comes in a quick speeds or in real time (news feeds)

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Variety

unstructured and unprocessed data (social media comments)

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Veracity

quality of data (including cleanliness which includes errors/data integrity issues, reliability and representationally faithful)

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Data analytics

science of examining raw data, removing excess noise, and organizing data with the purpose of drawing conclusions for decision making

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Limiting factors of data analytics

storage and processing power

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ETL

Extract, Transform, and Load; what data scientists must do to large volumes of data from multiple sources/platforms

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Impact of DA on Financial Accounting

 identify trends, identify potential risks and opportunities

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Impact of DA on Auditing

spend less time looking for evidence and more time making judgements; expanded capabilities in services

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AMPS Model

  1. Ask the question 2. Master the data 3. Perform the analysis 4. Share the story


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Data Visualization

Process of presenting information graphically

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Steps of Data Visualization

  1. understand the data 2. select data visualization tool 3. develop and present the visualization


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Categorical Data Type

Vertical bar, horizontal bar, treemaps, bubble charts

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Categorical Data Purpose

Comparisons of performance metrics

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Univariate Data Type

Histograms

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Univariate Data Purpose

Frequencies, range of values, most likely values

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Multivariate relationship Data Type

Scatter plots

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Multivariate relationship Data Purpose

Relationships, correlations

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Geospatial Data Type

Maps, symbol maps

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Geospatial Data Purpose

Comparisons among locations

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Time trends type

Line Charts

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Time trends data purpose

comparison over time

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Proportional Data Type

Pir Charts, doughnut charts, treemaps

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Proportional Data Purpose

Comparison of parts of a whole