Why is data cleaning important in data science?
Data Visualization and Analysis Techniques

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•
Mathematics
•
11th Grade
•
Hard
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1.
FLASHCARD QUESTION
Front
Back
Data cleaning is important in data science because it ensures the accuracy, consistency, and reliability of the data.
2.
FLASHCARD QUESTION
Front
What are the steps involved in data analysis?
Back
1. Defining the problem and objectives 2. Collecting and cleaning the data 3. Exploring and analyzing the data 4. Drawing conclusions and making predictions 5. Communicating the results
3.
FLASHCARD QUESTION
Front
What is the purpose of data visualization in data science?
Back
To present data in a visual format and help understand patterns, trends, and relationships within the data.
4.
FLASHCARD QUESTION
Front
What are some common techniques used for data visualization?
Back
Bar charts, line charts, scatter plots, pie charts, and heat maps.
5.
FLASHCARD QUESTION
Front
What is data collection in the context of data science?
Back
Gathering and acquiring data from various sources.
6.
FLASHCARD QUESTION
Front
What is data analysis?
Back
The process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.
7.
FLASHCARD QUESTION
Front
What is the difference between qualitative and quantitative data?
Back
Qualitative data describes characteristics or qualities, while quantitative data represents numerical values that can be measured.
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