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module 3 - (2/2) Wrangling Data Flashcards | StudBud
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module 3 - (2/2) Wrangling Data
module 3 - (2/2) Wrangling Data
25 terms
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Flashcards
25 cards
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Practice quiz
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Questions
25
Answers
1
What's the focus of the
Discovery (Exploration) phase
in data wrangling?
Understand data for cleaning.
Install new software.
Create detailed reports.
Make financial forecasts.
2
What does it mean to
'enrich data'
in the transformation phase?
Remove all old data.
Delete typing errors.
Add data for more meaningful analysis.
Change column order.
3
Which programming language uses
Pandas
for fast and easy data analysis operations?
C++
Python
Java
R
4
If website visits show "negative visits," what does that suggest about the data?
It's perfect.
It's complete.
It's very popular.
It's not reliable.
5
What happens if an organisation uses
poor quality data
?
Increases profits.
Improves speed.
Weakens competitive standing.
Reduces workload.
6
Which tool is a basic
spreadsheet
with built-in formulas for data?
Microsoft Excel
OpenRefine
Google DataPrep
Python
7
What's a common method for handling
missing values
in data?
Ignoring them completely.
Filtering out records or imputation.
Changing all values to zero.
Adding random numbers.
8
In transformation, what's the aim of
data normalisation
?
Add duplicate data.
Reduce redundancy and inconsistency.
Join rows from different tables.
Transform image formats.
9
What's the purpose of the
Publication phase
in data organisation?
Deliver transformed and validated data.
Analyse initial patterns.
Collect more data.
Delete sensitive data.
10
What's the main goal of
data organisation
(data wrangling)?
Prepare raw data for analysis
Sell data to companies.
Develop new applications.
Create automatic charts.
11
What's a "logic check" in data reliability?
Playing games.
Counting all numbers.
Does data look right at high level?
Writing code.
12
What tool helps inspect data structure and content to find anomalies?
Data Profiling.
Spreadsheets.
Data Visualisation.
SQL.
13
What is the
first step
in a typical data cleaning process?
Cleaning the data.
Verification.
Inspection.
Publication.
14
Which Python library is designed for
fast and easy data analysis
operations?
Pandas
Numpy
Jupyter
Scipy
15
What does
standardisation
aim to achieve in data cleaning?
Add more errors.
Ensure consistent formats.
Remove all text.
Make data bigger.
16
Which
interactive cloud service
is known for its
collaboration features
in data cleaning?
Trifacta Wrangler
Microsoft Excel
OpenRefine
Watson Studio Refinery
17
What
open-source
tool is menu-based and good for cleaning mixed text files?
Trifacta Wrangler
R
Watson Studio Refinery
OpenRefine
18
Which
cloud service
offers smart suggestions for your next data step?
Python
Microsoft Excel
Google DataPrep
OpenRefine
19
What
IBM cloud tool
cleans large raw data for analytics with built-in operations?
Trifacta Wrangler
R
Watson Studio Refinery
Google DataPrep
20
What type of tool would you use for very
large-scale data clean-up
that automatically suggests actions?
Basic text editor.
Cloud data service.
Spreadsheets.
Pen and paper
21
What programming language is strong for data analysis and making
aesthetic charts
?
Python
Java
SQL
R
22
Why is spending time on understanding data important?
To delay work.
To ignore errors.
To make it bigger.
No dataset is perfect.
23
What's a big part of a data pro's job, even with good tools?
Selling data to clients.
Gathering, cleaning, preparing data.
Only making reports.
Writing new software.
24
What must financial data be, besides accurate?
Printed out.
Fun.
Non-bias and error-free.
Old.
25
What's a key benefit of using
OpenRefine
for data wrangling?
Menu-based operations, easy to learn.
Exclusively for big data streams.
Requires complex coding.
Only works with structured data.
