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Google Data Analytics > Google Data Analysis > Flashcards

Google Data Analysis Flashcards

1
Q

A/B testing

A

The process of testing two variations of the same web page to determine which page is more successful at attracting user traffic and generating revenue

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2
Q

Compatibility

A

How well two or more datasets are able to work together

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3
Q

Data analysis process

A

The six phases of ask, prepare, process, analyze, share, and act whose purpose is to gain insights that drive informed decision-making

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4
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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5
Q

Data life cycle

A

The sequence of stages that data experiences, which include plan, capture, manage, analyze, archive, and destroy

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6
Q

First-party data

A

Data collected by an individual or group using their own resources

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7
Q

Gap analysis

A

A method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future

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8
Q

Problem types

A

The various problems that data analysts encounter, including categorizing things, discovering connections, finding patterns, identifying themes, making predictions, and spotting something unusual

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9
Q

Statistical power

A

The probability that a test of significance will recognize an effect that is present

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10
Q

Statistical significance

A

The probability that sample results are not due to random chance

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11
Q

Wide data

A

A dataset in which every data subject has a single row with multiple columns to hold the values of various attributes of the subject

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12
Q

Administrative metadata

A

Metadata that indicates the technical source of a digital asset

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13
Q

Descriptive metadata

A

Metadata that describes a piece of data and can be used to identify it at a later point in time

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14
Q

Structural metadata

A

Metadata that indicates how a piece of data is organized and whether it is part of one or more than one data collection

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15
Q

3 types of metadata

A
  • descriptive
  • structural
    administrative
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16
Q

Foreign key

A

A field within a database table that is a primary key in another table (Refer to primary key)

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17
Q

Primary key

A

An identifier in a database that references a column in which each value is unique (Refer to foreign key)

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18
Q

Metadata

A

Data about data

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19
Q

Elements of metadata

A
  • title and discription
  • tags and categories
    -who created it and when
    -who last modified it and when
  • who can access or update it
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20
Q

Metadata repository

A

A database created to store metadata

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21
Q

Metadata repositories

A
  • describe the state and location of the meatdata
  • describe the structures of the tables inside
  • describe how the data flows through the repository
  • keep track of who access the metadata and when
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22
Q

Hypothesis testing

A

A process to determine if a survey or experiment has meaningful results

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23
Q

Confidence level

A

The probability that a sample size accurately reflects the greater population

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24
Q

Margin of error

A

The maximum amount that sample results are expected to differ from those of the actual population

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25
Q

to calculate margin of error you need

A
  • population size
  • sample size
  • confidence level
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26
Q

Dirty data

A

Data that is incomplete, incorrect, or irrelevant to the problem to be solved

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27
Q

Clean data

A

Data that is complete, correct, and relevant to the problem being solved

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28
Q

Data engineer

A

A professional who transforms data into a useful format for analysis and gives it a reliable infrastructure

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29
Q

Data warehousing specialist

A

A professional who develops processes and procedures to effectively store and organize data

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30
Q

Confidence interval

A

A range of values that conveys how likely a statistical estimate reflects the population

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31
Q

Statistical significance

A

The probability that sample results are not due to random chance

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32
Q

Why a minimum sample of 30?

A

this recommendation is based on the central limit theorem (CLT) in the field of probability and statistics. A sample of 30 is the smallest sizes for which the clt still valid

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33
Q

Das zentrale Grenzwertsatz

A

Das zentrale Grenzwertsatz (englisch: central limit theorem) ist ein wichtiger Satz der Wahrscheinlichkeitstheorie. Es besagt, dass sich die Summe von unabhängigen und identisch verteilten Zufallsvariablen einer bestimmten Verteilung annähert, wenn die Anzahl der Summanden groß genug ist.

Genauer gesagt besagt der zentrale Grenzwertsatz, dass die Verteilung der Summe von unabhängigen und identisch verteilten Zufallsvariablen einer Normalverteilung annähert, wenn die Anzahl der Summanden groß genug ist. Dies bedeutet, dass viele Zufallsvariablen, die in der Realität auftreten, durch eine Normalverteilung approximiert werden können.

Dieser Satz ist von großer Bedeutung in der Statistik, da er es ermöglicht, viele statistische Tests durchzuführen und Schätzungen zu machen, auch wenn die zugrunde liegende Verteilung unbekannt ist. Der zentrale Grenzwertsatz ist ein wichtiger Bestandteil vieler statistischer Methoden und hat Anwendungen in vielen Bereichen, wie der Finanzmathematik, der Physik und der Ingenieurwissenschaften.

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34
Q

Random sampling

A

A way of selecting a sample from a population so that every possible type of the sample has an equal chance of being chosen

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35
Q

Sampling bias

A

Overrepresenting or underrepresenting certain members of a population as a result of working with a sample that is not representative of the population as a whole

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36
Q

Sample

A

In data analytics, a segment of a population that is representative of the entire population

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37
Q

types of insufficient data

A
  • data from only one sourse
  • data that keeps updating
  • outdated data
  • geographically limited data
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38
Q

SMART methodology

A

A tool for determining a question’s effectiveness based on whether it is specific, measurable, action-oriented, relevant, and time-bound

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39
Q

critical questions about the predictiv analytical models

A
  • why is it taking so long to put new or updated models into production?
  • who created the model and why?
  • what input variables are used to make predictions and to make precisions?
  • how are models used?
  • how are models performing and when were they last updated?
  • where is the supporting documentation?
    no ansvers - no real value
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40
Q

making predictions

A

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

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41
Q

6 problem types that data analysts typically face

A
  1. making predictions
  2. cetegorizing things
  3. spotting something unusual
  4. identifying themes
  5. discovering connections
  6. finding patterns
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42
Q

data analysis process (google)

A

The six phases of ask, prepare, process, analyze, share, and act whose purpose is to gain insights that drive informed decision-making

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43
Q

Data-driven decision-making

A

Using facts to guide business strategy

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44
Q

Algorithm

A

A process or set of rules followed for a specific task

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45
Q

how data is collected

A
  • interviews
  • observations
  • forms
  • questionairies
  • survey
  • cookies
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46
Q

Metric

A

A single, quantifiable type of data that is used for measurement

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47
Q

Problem domain

A

The area of analysis that encompasses every activity affecting or affected by a problem

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48
Q

Structured thinking

A

The process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying options

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49
Q

Scope of work (SOW)

A

An agreed-upon outline of the tasks to be performed during a project

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50
Q

Report

A

A static collection of data periodically given to stakeholders

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51
Q

Quantitative data

A

A specific and objective measure, such as a number, quantity, or range

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52
Q

Quantitative data tools

A
  • structural interviews
  • survey
  • polls
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53
Q

Qualitative data

A

A subjective and explanatory measure of a quality or characteristic

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54
Q

Qualitative data tools

A
  • focus groups
  • social media
  • in-person interviews
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55
Q

Data life cycle

A

The sequence of stages that data experiences, which include plan, capture, manage, analyze, archive, and destroy

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56
Q

Best practices when organizing data

A
  • naming conventios
  • foldering
  • archiving older files
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57
Q

data life cicle
5) archive

A

keep relevant data stored long-term and future reference

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58
Q

Bias

A

A conscious or subconscious preference in favor of or against a person, group of people, or thing

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59
Q

Confirmation bias

A

The tendency to search for or interpret information in a way that confirms pre-existing beliefs

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60
Q

Interpretation bias

A

The tendency to interpret ambiguous situations in a positive or negative way

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61
Q

Data integrity

A

The accuracy, completeness, consistency, and trustworthiness of data throughout its life cycle

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62
Q

Data replication

A

The process of storing data in multiple locations

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63
Q

Data transfer

A

The process of copying data from a storage device to computer memory or from one computer to another

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64
Q

Data manipulation

A

The process of changing data to make it more organized and easier to read

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65
Q

Statistical significance

A

The probability that sample results are not due to random chance

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66
Q

Data bias

A

When a preference in favor of or against a person, group of people, or thing systematically skews data analysis results in a certain direction

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67
Q

types of data bias

A
  • observer bias
  • interpretation bias
  • confirmation bias
  • sampling bias
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68
Q

Continuous data

A

Data that is measured and can have almost any numeric value

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69
Q

Discrete data

A

Data that is counted and has a limited number of values

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70
Q

Ordinal data

A

Qualitative data with a set order or scale

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71
Q

External data

A

Data that lives, and is generated, outside of an organization

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72
Q

Nominal data

A

A type of qualitative data that is categorized without a set order

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73
Q

Internal data

A

Data that lives within a company’s own systems

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74
Q

Qualitative data

A

A subjective and explanatory measure of a quality or characteristic

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75
Q

Second-party data

A

Data collected by a group directly from its audience and then sold

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76
Q

Population

A

In data analytics, all possible data values in a dataset

77
Q

Third-party data

A

Data provided from outside sources who didn’t collect it directly

78
Q

Structured data

A

Data organized in a certain format such as rows and columns

79
Q

Unstructured data

A

Data that is not organized in any easily identifiable manner

80
Q

Long data

A

A dataset in which each row is one time point per subject, so each subject has data in multiple rows

81
Q

Dataset

A

A collection of data that can be manipulated or analyzed as one unit

82
Q

Attribute

A

A characteristic or quality of data used to label a column in a table

83
Q

Fairness

A

A quality of data analysis that does not create or reinforce bias

84
Q

Query

A

A request for data or information from a database

85
Q

Data governance

A

A process for ensuring the formal management of a company’s data assets

86
Q

Naming conventions

A

Consistent guidelines that describe the content, creation date, and version of a file in its name

87
Q

Data-inspired decision-making

A

Exploring different data sources to find out what they have in common

88
Q

Data analysis

A

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

89
Q

Data science

A

A field of study that uses raw data to create new ways of modeling and understanding the unknown

90
Q

Data analysis process

A

The six phases of ask, prepare, process, analyze, share, and act whose purpose is to gain insights that drive informed decision-making

91
Q

Formula

A

A set of instructions used to perform a calculation using the data in a spreadsheet

92
Q

Observation

A

The attributes that describe a piece of data contained in a row of a table

93
Q

Data ecosystem

A

The various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data

94
Q

Data

A

A collection of facts

95
Q

Data validation

A

A tool for checking the accuracy and quality of data

96
Q

Analytical skills

A

Qualities and characteristics associated with using facts to solve problems

97
Q

Observer bias

A

The tendency for different people to observe things differently (also called experimenter bias)

98
Q

Unbiased sampling

A

When the sample of the population being measured is representative of the population as a whole

99
Q

Data interoperability

A

The ability to integrate data from multiple sources and a key factor leading to the successful use of open data among companies and governments

100
Q

Data anonymization

A

The process of protecting people’s private or sensitive data by eliminating identifying information

101
Q

Openness

A

The aspect of data ethics that promotes the free access, usage, and sharing of data

102
Q

Currency

A

The aspect of data ethics that presumes individuals should be aware of financial transactions resulting from the use of their personal data and the scale of those transactions

103
Q

Consent

A

The aspect of data ethics that presumes an individual’s right to know how and why their personal data will be used before agreeing to provide it

104
Q

Transaction transparency

A

The aspect of data ethics that presumes all data-processing activities and algorithms should be explainable and understood by the individual who provides the data

105
Q

Ownership

A

The aspect of data ethics that presumes individuals own the raw data they provide and have primary control over its usage, processing, and sharing

106
Q

Data ethics

A

Well-founded standards of right and wrong that dictate how data is collected, shared, and used

107
Q

Data model

A

A tool for organizing data elements and how they relate to one another

108
Q

Data element

A

A piece of information in a dataset

109
Q

Open data

A

Data that is available to the public

110
Q

threats to data integrity

A
  • humon error
  • viruses
  • malware
  • hacking
  • system failures
111
Q

Data formats

A
    • inernal - external
    • continuous -discrete
    • structured - unstructured
    • nominal - ordinal
  5. -qualitative - quantitative
    • primary - secondary
112
Q

Primary data

A

collected by a researcher from first-hand sources

113
Q

Data type

A

An attribute that describes a piece of data based on its values, its programming language, or the operations it can perform

114
Q

2 common methods to develop data models

A

-entity relationship diagram (ERD)
- unified modeling lanquage (UML)

115
Q

5) Share DA-process

A
  • untersrand visualization
    -create effective visuals
    -bring data to life
    -use data storytelling
  • communicate to help others understand results
116
Q

Sorting

A

The process of arranging data into a meaningful order to make it easier to understand, analyze, and visualize

117
Q

decision intelligence

A

is a combination of applied data science and the social and managerial science

118
Q

data life cicle 6)destroy

A

remove data from storage and delete any shared copies of the data

119
Q

3) Process DA-Process

A
  • create and transform data
  • maintain data intergrity
    -test data
    -clean data
    -verify and report on cleaning results
120
Q

4) Analyse DA-Process

A

-use tools to format and transform data
-sort and filter data
-identify patterns and draw conclusions
-make predictions and recommendations
make data-driven decisions

121
Q

1) Prepare DA-Process

A

-understand how data is generated and collected
- identify and use different data formats, types and structures
- make sure data is unbiased and credible
-organize and protect data

122
Q

Step 1 - Ask

A

-define the problem you are trying to solve
-make sure you fully understand the stakeholders expectations
-focus on the actual problem and avoid any distractions
-collaborate with stackeholders and keep open line of communication
-take astep back and see the whole situation in context

123
Q

Ask DA-Process

A

-aks effective questions
-define the problem
-use structured thinking
-communicate whit others

124
Q

dashboards pros&cons

A

pros:
-dynamic automatic and interactive
-more stackholder access
-low maintenance
cons:
-labor intensive design
-can be confusing
-potentially uncleaned data

125
Q

reports pros&cons

A

pros:
-high level historical data
- easy to design
-pre-cleande and sorted data
cons:
-continual maintenance
-less visually appealing
-static

126
Q

Step 4. Analyse

A

think analyticaly about my data
- perform calculations
-combine data from multiple sourses
-create tables with your results

Q:
1) what story is my data telling me
2)how will my data help me solve this problem?

127
Q

Step 3. Process

A

clean data of any possible errors, inaccuracies, inconsistencies
- using spreadsheet functions fo find incorreclty entered data
- using SQL functions to check for extra spaces
-removing repeated entries
-checking for bias in data
Q:
1. what data errors or inaccuracies might get in my way of getting out of best possible answer to find the problem i’m trying to solve
2. how can i clean my data so the information i have is more consistent

128
Q

Data analytics

A

The science of data

129
Q

Confidence interval

A

A range of values that conveys how likely a statistical estimate reflects the population

130
Q

A/B testing

A

The process of testing two variations of the same web page to determine which page is more successful at attracting user traffic and generating revenue

131
Q

Access control

A

Features such as password protection, user permissions, and encryption that are used to protect a spreadsheet

132
Q

Accuracy

A

The degree to which data conforms to the actual entity being measured or described

133
Q

Action-oriented question

A

A question whose answers lead to change

134
Q

Analytical thinking

A

The process of identifying and defining a problem, then solving it by using data in an organized, step-by-step manner

135
Q

Bad data source

A

A data source that is not reliable, original, comprehensive, current, and cited (ROCCC)

136
Q

Big data

A

Large, complex datasets typically involving long periods of time, which enable data analysts to address far-reaching business problems

137
Q

Boolean data

A

A data type with only two possible values, usually true or false

138
Q

Changelog

A

A file containing a chronologically ordered list of modifications made to a project

139
Q

Compatibility

A

How well two or more datasets are able to work together

140
Q

Completeness

A

The degree to which data contains all desired components or measures

141
Q

Consistency

A

The degree to which data is repeatable from different points of entry or collection

142
Q

Context

A

The condition in which something exists or happens

143
Q

Cross-field validation

A

A process that ensures certain conditions for multiple data fields are satisfied

144
Q

Dashboard

A

A tool that monitors live, incoming data

145
Q

Data analyst

A

Someone who collects, transforms, and organizes data in order to draw conclusions, make predictions, and drive informed decision-making

146
Q

Data constraints

A

The criteria that determine whether a piece of a data is clean and valid

147
Q

Data design

A

How information is organized

148
Q

Data mapping

A

The process of matching fields from one data source to another

149
Q

Data merging

A

The process of combining two or more datasets into a single dataset

150
Q

Data range

A

Numerical values that fall between predefined maximum and minimum values

151
Q

Data security

A

Protecting data from unauthorized access or corruption by adopting safety measures

152
Q

Data strategy

A

The management of the people, processes, and tools used in data analysis

153
Q

Data visualization

A

The graphical representation of data

154
Q

Estimated response rate

A

The average number of people who typically complete a survey

155
Q

Experimenter bias

A

The tendency for different people to observe things differently (Refer to Observer bias)

156
Q

Gap analysis

A

A method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future

157
Q

General Data Protection Regulation of the European Union (GDPR)

A

Policy-making body in the European Union created to help protect people and their data

158
Q

Good data source

A

A data source that is reliable, original, comprehensive, current, and cited (ROCCC)

159
Q

Incomplete data

A

Data that is missing important fields

160
Q

Inconsistent data

A

Data that uses different formats to represent the same thing

161
Q

Incorrect/inaccurate data

A

Data that is complete but inaccurate

162
Q

Mandatory

A

A data value that cannot be left blank or empty

163
Q

Normalized database

A

A database in which only related data is stored in each table

164
Q

Outdated data

A

Any data that has been superseded by newer and more accurate information

165
Q

Problem types:

A

The various problems that data analysts encounter, including categorizing things, discovering connections, finding patterns, identifying themes, making predictions, and spotting something unusual

166
Q

Redundancy

A

When the same piece of data is stored in two or more places

167
Q

Reframing

A

The process of restating a problem or challenge, then redirecting it toward a potential resolution

168
Q

Regular expression (RegEx)

A

A rule that says the values in a table must match a prescribed pattern

169
Q

Small data

A

Small, specific data points typically involving a short period of time, which are useful for making day-to-day decisions

170
Q

Stakeholders

A

People who invest time and resources into a project and are interested in its outcome

171
Q

Technical mindset

A

The ability to break things down into smaller steps or pieces and work with them in an orderly and logical way

172
Q

Transferable skills

A

Skills and qualities that can transfer from one job or industry to another

173
Q

Typecasting

A

Converting data from one type to another

174
Q

Validity

A

The degree to which data conforms to constraints when it is input, collected, or created

175
Q

Verification

A

A process to confirm that a data-cleaning effort was well executed and the resulting data is accurate and reliable

176
Q

aspects of data ethics

A

-ownership
-transaction transparency
-consent
-currency
-privacy
-openness

177
Q

PII

A

personal identifiable information

information that can be used by itself or with other data to track down a person’s identity

178
Q

privacy

A

preserving a data subjets information and activity any time a data transaction occurs

179
Q

structured data

A

-defined data types
-most often quantitative data
-easy to organise
-easy to search
-easy to analyse
-stored in relational databases & data warehouese
-contained in rows and colums

180
Q

Re-identification

A

A process used to wipe data clean all personally identifying information

181
Q

confidence level

A

confidence level is targered before you start your study, because it will affect how big your margin of error is at the end of your study.

how confident you are in the survey sesults. F.e. a 95% confidence level means that if you were to run the same survey 100 times you would be get similar results 95 of those 100 times.

182
Q

data collection considerations

A

how the data will be collected
chose data sources
decide what data to use
how much data to collect select the right data type
determine the time frame

183
Q

6) act DA process

A

-apply your insights
-solve problem
-make decisions
-create something new

184
Q

good data sources

A

Reliable
Original
Comprehensive
Current
Cited

185
Q

spotting something unusual

A

identifying data that is different from the norm

186
Q

4) analyse DA process

A

use data to solve problems make decisions and support buisness goals

187
Q

data anlyticer skills quantities

A

curiosity
understanding context
having technical mindset
data design
data strategy

188
Q

SAS’s iterative life cycle

A

ask-prepare-explore-model-implement-act-evaluate

Google Data Analytics (2 decks)

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