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Data Analytics > chapter 1 > Flashcards

chapter 1 Flashcards

1
Q

Define Data Analytics

A

the process of evaluating data with the purpose of drawing conclusions to address business questions.

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2
Q

What is used to analyze data to give organizations the information they need to make sound and timely decisions?

A

technologies, systems, practices, methodologies, databases, statistics, and applications.

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3
Q

Patterns are discovered from…

A

past archives

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4
Q

What is an analytics mindset?

A

recognizing when and how data analytics can address accounting questions

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5
Q

what is Data scrubbing and data preparation

A

comprehend the process needed to extract (query), clean, and prepare the data before analysis

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6
Q

Define data quality

A

recognize what is meant by data quality, be it completeness, reliability, or validity

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7
Q

descriptive data analysis

A

perform basic analysis to understand the quality of the underlying data and their ability to address the business question

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8
Q

data analysis through data manipulation

A

demonstrate ability to sort, rearrange, merge, and reconfigure data in a manner that allows enhanced analysis

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9
Q

problem solving through statistical data analysis

A

identify and implement an approach that will use statistical data analysis to draw conclusions and make recommendations on a timely basis

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10
Q

data visualization and data reporting

A

report results of analysis in an accessible way to each varied decision maker and his or her specific needs

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11
Q

what is the objective of data extraction

A

to identify and obtain the data from the appropriate source

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12
Q

what is the objective of transforming data

A

to validate the data for completeness and integrety

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13
Q

what is the objective of loading data

A

to load the data into the appropriate tool for analysis

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14
Q

what are the five steps of the ETL process

A

determine the purpose and scope of the data request, obtain the data, validate the data for completeness and integrity, clean the data, load the data for data analysis.

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15
Q

Define classification

A

an attempt to assign each unit in a population into a few categories

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16
Q

define Regression

A

a data approach that attempts to estimate or predict, for each unit, the numerical value of some variable using some type of statistical model.

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17
Q

define similarity matching

A

a data approach that attempts to identify similar individuals based on data known about them

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18
Q

define clustering

A

an attempt to divide individuals into groups in a useful or meaningful way

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19
Q

define co-occurrance grouping

A

a data approach that attempts to discover associations between individuals based on transactions involving them (i.e. when amazon says customers who bought this also bought…

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20
Q

define profiling

A

a data approach that attempts to characterize the “typical” behavior of an individual, group, or population by generating summary statistics about the data (mean, median, stnd deviation)

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21
Q

define link prediction

A

a data approach that attempts to predict a relationship between 2 data items (i.e. facebook sees you have 20 mutual friends w someone, suggests them as a friend)

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22
Q

define structured data

A

data that are stored in a database or spreadsheet and are readily searchable

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23
Q

define training data

A

existing data that have been manually evaluated and assigned a class

24
Q

define test data

A

a set of data used to assess the degree and strength of a predicted relationship established by the analysis of training data

25
Q

what is Benford’s law

A

it states that when you have a large set of naturally occuring numbers, the leading digit is likely to be small.

26
Q

Define diagnostic analytics

A

procedures that explore the current data to determine why something has happened the way it has, typically comparing the data to a benchmark

27
Q

what are the examples of diagnostic analytics

A

profiling, clustering, similarity matching, co-occurrence grouping

28
Q

define predictive analytics and what are it’s examples

A

procedures that can generate a model that can be used to determine what can happen in the future. Examples are regression, classification, and link prediction.

29
Q

what is nominal data

A

qualitative data that cannot be ranked (i.e. hair color)

30
Q

what is ordinal data

A

qualitative data that can be ranked (i.e. gold, silver, bronze, or an A,B,C grade)

31
Q

what is ratio data

A

quantitative data where 0 defines the “absence” of something. (i.e. cash)

32
Q

what is interval data

A

quantitative data where 0 is just another number (i.e. temperature)

33
Q

What is discrete data

A

quantitative data that only shows whole numbers (i.e. points in a basketball game)

34
Q

What is continuous data

A

quantitative data that shows numbers with decimals (i.e. height)

35
Q

what are declarative visualizations

A

visualizations that present findings to an audience (i.e. financial results

36
Q

what are exploratory visualizations

A

visualizations used to gain insight while you are interacting with the data (i.e. identifying good customers)

37
Q

what is the base standard

A

defines the formats for files & fields as well as master data requirements for users, business units, and tax tables.

38
Q

what is the general ledger standard

A

Defines the chart of accounts, source listings, trial balance, and general ledger or journal entry detail

39
Q

what is the order to cash subledger standard

A

defines sales orders and line items, shipments, invoices, open accounts receivable and adjustments, cash receipts, and customer master data.

40
Q

what is the procedure to pay subledger standard

A

defines purchases and line items, goods received, invoices received, open accounts payable and adjustments, payments, and supplier master data

41
Q

what is the inventory subledger standard

A

defines inventory location master data, product master data, inventory on hand data, and inventory movement transactions, and physical inventory and material cost

42
Q

what is the fixed asset subledger

A

defines fixed asset master data, additions, removals, and depreciation calculations.

43
Q

what is a homogeneous system

A

one single uniform installation or instance of a system

44
Q

what is a heterogeneous system

A

multiple installations or systems

45
Q

what do systems translator software do

A

attempts to map the various tables and fields from the varied enterprise systems in a heterogeneous system into a data warehouse

46
Q

what is a data warehouse

A

where all of the data can be analyzed centrally, it is a repository of data accumulated from internal and external data sources

47
Q

what is a flat file

A

a means of storing the data in one place, it is a single table of data with user-defined attributes that is stored separate from any application

48
Q

what is a correlation coefficient

A

how closely 2 datasets are correlated or predictive of one another

49
Q

what numbers do correlation coefficients range between

A

-1 to 1

50
Q

what is the hot hand fallacy

A

assuming events are not independent when they are

51
Q

what is selection bias

A

Having the wrong take because of the group you’re deriving data from

52
Q

what is publication bias

A

significant findings are published, not finding anything results in not being published

53
Q

recall bias

A

participants do not remember previous events or experiences accurately, or once they are told something happened a certain way, they believe to remember it that way.

54
Q

what is survivorship bias

A

focusing on the people or things that made it past some sort of selection (we analyze data from existing companies, not the ones that failed)

55
Q

what charts would you use for conceptual (qualitative) data

A

bar charts, pie chart, heat map, tree map (for comparison)
symbol map (for geographic data)
word cloud (text data)

56
Q

what charts would you use for data-driven (quantitative) data

A

box and whisker plot (for outlier detection) scatter plot (relationship between two variables)
line chart (trend over time)
filled map (geographic data)

Data Analytics (2 decks)

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