Brainscape's Knowledge GenomeTM


Top Flashcards (Certified)
All Flashcards

MIDTERMSSSSSSS YAWA > BAT > Flashcards

BAT Flashcards

(83 cards)

Start Studying
1
Q

Field of computer
science that uses math, and statistics.

A

Analytics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

involves sifting through massive data

A

Analytics/Data Analytics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

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

A

Structure Data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

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

A

Unstructured Data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

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

A

Business Intelligence

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

the study of data to extract meaningful insights for business

A

Data Science

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

We should always start with Business Problems

A

Business Understanding

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Cross-Industry Standard Process for Data Mining.

A

CRISP-DM

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

basic entities such as
name, age, etc.

A

Data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

involves
accessing the data and exploring
it using tables and graphics

A

Data Understanding

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

level or depth of
data

A

Granularity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

it is a safe space to
explore

A

Sand Box

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

for live data

A

Production

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

often extremely time consuming

A

Data Preperation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

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

A

Model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Give Popular Analytics tools

A

Excel, Python, Rstudio, Database, Tableu power BI

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

explore/analyze smaller data sets

A

Excel

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

visualize your data with dashboard

A

Tableu power Bl

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

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

A

Python / Rstudio

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Allows you to communicate and interact with databases

A

Databases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

4 Characteristics of Big Data

A

Volume, Velocity, Variety and Veracity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
data size
Volume
16
speed of change
Velocity
16
Different forms of data
Variety
17
collect data (unstructured data)
Input
18
normalization
Process
18
Structured data (Organized)
Output
19
Presents new opportunities and challenges
Big Data
19
use of images to present information
Visualization
20
about creating a business insight, rather than simply reporting on collected business data.
Visualization
21
act of taking information
Data Visualization
22
2 goals of Data visualization
Explanatory and exploratory Analysis
23
These visuals are meant to direct the viewer along a defined path
Explanatory Analysis
24
Patterns to find story in data
Trends, Correlations and Outliers
25
the first step of data analysis
Exploratory analysis
26
Do not use high contrast colors
True
27
Use more than 5 colors in a single layout
True
28
use one color to represent each category
True
29
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
Bar Chart
30
best used for making part-to-whole comparisons with discrete or continuous data. They are the most impactful with a small data set.
Pie Chart
31
Type of Pie chart that is used to show part-to whole relationships.
Standard
32
Stylistic variation that enables the inclusion of a total value or design element in the center
Donut
33
Used to show timeseries relationships with continuous data. They help show trend, acceleration, deceleration, and volatility
Line Chart
34
Area charts depict a time-series relationship, but they are different than line charts in that they can represent volume.
Area Chart
35
Shows the relationship between items based on two sets of variables. They are best used to show correlation in a large amount of data
Scatter Plot
36
They are good for displaying nominal comparisons or ranking relationships need information on the diversity of the employees’ location, address, school graduated etc.
Bubble Chart
37
Unusual Measurement that may require attention, but not in an overwhelming
Call attention
38
should be used to call attention to specific values to differentiate categorical variables
Color
39
The process of turning raw data into business action
Framework for Business Analytics
40
first step in turning data into analytics
Data Extraction
41
this is where the data is cleaned, curated, organized, and ready for analysis
Data warehousing
42
This is the data that is used to benchmark or to profile.
Descriptive Analytics.
42
This is the process of moving data from source systems to data warehouse to an analytical tool.
Extract, Transform and Load Processes (ETL)
43
Using analytics in reporting financial results, from gathering financial Inputs from different sources, cleansing it, to reporting it.
Descriptive analysis
43
This is used to determine relationships between two different types of data and make predictions about future data.
Predictive Analytics.
43
This is used to create recommendations through simulation and optimization models.
Prescriptive Analytics.
44
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.
Predictive analysis
45
When we want to determine the feasibility of the project, say the likelihood that the project will falter, or overshoot the budget, or fail.
Prescriptive analysis
46
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.
Descriptive Analytics
47
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.
Predictive Analytics
48
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.
Descriptive analytics
48
Employee engagement, such as looking at what makes them content, happy, and stay in the company (ex. party, bonus, free training, etc)
Prescriptive Analytics
49
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.
Predictive analytics
50
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.
Prescriptive analytics
50
processed data for a given context and specific application.
Information
50
the heart of each system
Data
50
facts or figures which can be stored in a database.
Data
51
a collection of logically related data and it is typically visualize as tables; composed of cells matched with several columns and rows
Database
52
a software package or software that allows you to store, retrieve, package your database
DBMS
53
a moral principle that somehow guides a person on what is bad and what is good.
Ethics
53
Ethical considerations are crucial to ensuring that data and analytics are used fairly, transparently, and accountable.
Importance of Ethics in data and analytics
54
Businesses that prioritize data ethics are more likely to gain the trust of their customers and stakeholders
Importance of Ethics in data and analytics
55
data analyst must recognize and address potential biases in their data, which may arise from unrepresentative samples or biased data collection methods.
Discrimination and Bias
55
ensuring that data is of high quality and accurately reflects the phenomena being studied prevents incorrect conclusions or misleading results
Integrity of data analytics
55
openly sharing data, methodologies, and code, researchers can help others verify their findings and build upon their work.
Lack of Transparency
55
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.
Data privacy Law
56
right to know when his or her personal data shall be, are being, or have been processed.
Right to be informed
56
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.
Right to access
57
Dispute any inaccuracy or error in thepersonal information processed, and to have the personal information controller it immediately.
Right to rectify
58
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.
Right to file a complaint

MIDTERMSSSSSSS YAWA (4 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions