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Final Exam
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
Volume
massive amount of data involved
Velocity
data comes in a quick speeds or in real time (news feeds)
Variety
unstructured and unprocessed data (social media comments)
Veracity
quality of data (including cleanliness which includes errors/data integrity issues, reliability and representationally faithful)
Data analytics
science of examining raw data, removing excess noise, and organizing data with the purpose of drawing conclusions for decision making
Limiting factors of data analytics
storage and processing power
ETL
Extract, Transform, and Load; what data scientists must do to large volumes of data from multiple sources/platforms
Impact of DA on Financial Accounting
identify trends, identify potential risks and opportunities
Impact of DA on Auditing
spend less time looking for evidence and more time making judgements; expanded capabilities in services
AMPS Model
Ask the question 2. Master the data 3. Perform the analysis 4. Share the story
Data Visualization
Process of presenting information graphically
Steps of Data Visualization
understand the data 2. select data visualization tool 3. develop and present the visualization
Categorical Data Type
Vertical bar, horizontal bar, treemaps, bubble charts
Categorical Data Purpose
Comparisons of performance metrics
Univariate Data Type
Histograms
Univariate Data Purpose
Frequencies, range of values, most likely values
Multivariate relationship Data Type
Scatter plots
Multivariate relationship Data Purpose
Relationships, correlations
Geospatial Data Type
Maps, symbol maps
Geospatial Data Purpose
Comparisons among locations
Time trends type
Line Charts
Time trends data purpose
comparison over time
Proportional Data Type
Pir Charts, doughnut charts, treemaps
Proportional Data Purpose
Comparison of parts of a whole