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MIDTERMSSSSSSS YAWA > BAT > Flashcards

BAT Flashcards

1
Q

Field of computer
science that uses math, and statistics.

A

Analytics

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2
Q

involves sifting through massive data

A

Analytics/Data Analytics

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3
Q

highly organized
and formatted so that it’s easily
searchable in relational database

A

Structure Data

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3
Q

has no predefined format or organization, making it difficult to collect, process and analyze.

A

Unstructured Data

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4
Q

the art of assembling the data gathered through Business Intelligence in such a way that it can be analyzed by people.

A

Business Analytics

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5
Q

the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about business operations and make better, fact-based decisions

A

Business Analytics

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6
Q

the process of collecting information from all sources to make data-driven decisions in an organization

A

Business Intelligence

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7
Q

the study of data to extract meaningful insights for business

A

Data Science

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7
Q

the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis.

A

Data Mining

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7
Q

We should always start with Business Problems

A

Business Understanding

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8
Q

Cross-Industry Standard Process for Data Mining.

A

CRISP-DM

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9
Q

basic entities such as
name, age, etc.

A

Data

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10
Q

involves
accessing the data and exploring
it using tables and graphics

A

Data Understanding

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11
Q

level or depth of
data

A

Granularity

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11
Q

it is a safe space to
explore

A

Sand Box

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12
Q

for live data

A

Production

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12
Q

often extremely time consuming

A

Data Preperation

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13
Q

a simplified
description of a system or
process to assist calculation and
predictions

A

Model

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13
Q

Give Popular Analytics tools

A

Excel, Python, Rstudio, Database, Tableu power BI

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14
Q

explore/analyze smaller data sets

A

Excel

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14
Q

visualize your data with dashboard

A

Tableu power Bl

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14
Q

allows you to build statistical models that can make predictions about your data

A

Python / Rstudio

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15
Q

Allows you to communicate and interact with databases

A

Databases

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16
Q

4 Characteristics of Big Data

A

Volume, Velocity, Variety and Veracity

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16
Q

data size

A

Volume

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16
Q

speed of change

A

Velocity

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16
Q

Different forms of data

A

Variety

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17
Q

collect data (unstructured
data)

A

Input

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18
Q

normalization

A

Process

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18
Q

Structured data
(Organized)

A

Output

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19
Q

Presents new opportunities and challenges

A

Big Data

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19
Q

use of images to present information

A

Visualization

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20
Q

about creating a business insight, rather than simply reporting on collected business data.

A

Visualization

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21
Q

act of taking information

A

Data Visualization

22
Q

2 goals of Data visualization

A

Explanatory and exploratory Analysis

23
Q

These visuals are meant to direct the viewer along a defined path

A

Explanatory Analysis

24
Q

Patterns to find story in data

A

Trends, Correlations and Outliers

25
Q

the first step of data analysis

A

Exploratory analysis

26
Q

Do not use high contrast colors

A

True

27
Q

Use more than 5 colors in a single layout

A

True

28
Q

use one color to represent each category

A

True

29
Q

Type of chart that is very versatile. They are best used to show change over time, compare different categories or compare parts of a whole

A

Bar Chart

30
Q

best used for making part-to-whole
comparisons with discrete or
continuous data. They are the
most impactful with a small data
set.

A

Pie Chart

31
Q

Type of Pie chart that is used to show part-to whole relationships.

A

Standard

32
Q

Stylistic variation that enables the inclusion of a total value or design element in the center

A

Donut

33
Q

Used to show timeseries relationships with continuous data. They help show trend,
acceleration, deceleration, and
volatility

A

Line Chart

34
Q

Area charts depict a
time-series relationship, but they are
different than line charts in that they
can represent volume.

A

Area Chart

35
Q

Shows the relationship
between items based on two sets of
variables. They are best used to show
correlation in a large amount of data

A

Scatter Plot

36
Q

They are good for
displaying nominal comparisons or
ranking relationships need
information on the diversity of the
employees’ location, address, school
graduated etc.

A

Bubble Chart

37
Q

Unusual Measurement that may require attention, but not in an overwhelming

A

Call attention

38
Q

should be used to call attention to specific values to differentiate categorical variables

A

Color

39
Q

The process of turning raw data into business action

A

Framework for Business Analytics

40
Q

first step in turning data
into analytics

A

Data Extraction

41
Q

this is where the data is cleaned, curated, organized, and ready for analysis

A

Data warehousing

42
Q

This is the data that is used to benchmark or to profile.

A

Descriptive Analytics.

42
Q

This is the process of moving data
from source systems to data warehouse to
an analytical tool.

A

Extract, Transform and Load Processes
(ETL)

43
Q

Using analytics in reporting financial results, from gathering financial Inputs from different sources, cleansing it, to reporting it.

A

Descriptive analysis

43
Q

This is used to determine relationships between two different types of data and make predictions about future data.

A

Predictive Analytics.

43
Q

This is used to create recommendations through simulation and optimization models.

A

Prescriptive Analytics.

44
Q

When we want to predict the trend of sales for the next two months using historical patterns of seasonality, and examining whether investing a lot in sales people might also drive the sales trend.

A

Predictive analysis

45
Q

When we want to determine the feasibility of the project, say the likelihood that the project will falter, or overshoot the budget, or fail.

A

Prescriptive analysis

46
Q

You want to understand the demographics of the employees in your company. You may need information on the diversity of the employees’ location, address, school graduated etc.

A

Descriptive Analytics

47
Q

Looking at the historical patterns of resignations to determine the likely causes of resignations and the number of employees that are likely to resign in the future. Want to determine the drivers that make employees stay in the company.

A

Predictive Analytics

48
Q

Determine how many people clicked the ads, how many people bought the product, and how many people paid cash-on- delivery, or by credit card.

A

Descriptive analytics

48
Q

Employee engagement, such as looking at what makes them content, happy, and stay in the company (ex. party, bonus, free training, etc)

A

Prescriptive Analytics

49
Q

If you want to understand how factors (e.g. price, marketing mix and attributing the effect, channels, mode of payment, etc.) contribute to the performance to predict the future performance (success or failure) of a campaign or achieve targets.

A

Predictive analytics

50
Q

Recommendation engines which are found to be successful in driving more sales. These are the recommendations that you can see whenever you visit an online shopping website, say to buy a book.

A

Prescriptive analytics

50
Q

processed data for a given context and specific application.

A

Information

50
Q

the heart of each system

A

Data

50
Q

facts or figures which can be stored in a database.

A

Data

51
Q

a collection of logically related data and it is typically visualize as tables; composed of cells matched with several columns and rows

A

Database

52
Q

a software package or software that allows you to store, retrieve, package your database

A

DBMS

53
Q

a moral principle that somehow guides a person on what is bad and what is good.

A

Ethics

53
Q

Ethical considerations are crucial
to ensuring that data and analytics
are used fairly, transparently, and
accountable.

A

Importance of Ethics in data and analytics

54
Q

Businesses that prioritize data
ethics are more likely to gain the
trust of their customers and
stakeholders

A

Importance of Ethics in data and analytics

55
Q

data analyst must recognize and address potential biases in their data, which may arise from unrepresentative samples or biased data collection methods.

A

Discrimination and Bias

55
Q

ensuring that data is of high quality and accurately reflects the phenomena being studied prevents incorrect conclusions or misleading results

A

Integrity of data analytics

55
Q

openly sharing data, methodologies, and code, researchers can help others
verify their findings and build upon their work.

A

Lack of Transparency

55
Q

Republic Act
No. 10173, otherwise known as the Data Privacy Act is a law that seeks to protect all forms of information, be it private, personal, or sensitive.
It is meant to cover both natural
and juridical persons involved in
the processing of personal
information.

A

Data privacy Law

56
Q

right to know when his or her personal data shall be, are being, or have been processed.

A

Right to be informed

56
Q

able to compel any entity possessing any personal data to provide the data subject with a description of such data in its possession, as well as the purposes for which they are to be or are being processed.

A

Right to access

57
Q

Dispute any inaccuracy or error in thepersonal information processed, and to have the personal information controller it immediately.

A

Right to rectify

58
Q

with the national privacy commission affords a remedy to any data subject who feels that his/her personal information has been misused, maliciously disclosed, or improperly disposed, or in case of any violation of his on her data privacy rights.

A

Right to file a complaint

MIDTERMSSSSSSS YAWA (4 decks)

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