What is data collection in the context of data science?

• Analyzing and interpreting data

• Storing and organizing data

• Manipulating and transforming data

• Gathering and acquiring data from various sources.

Why is data cleaning important in data science?

• Data cleaning is important in data science because it ensures the accuracy, consistency, and reliability of the data.

• Data cleaning only focuses on the accuracy of the data.

• Data cleaning is not important in data science.

• Data cleaning is time-consuming and unnecessary in data science.

What is data analysis and why is it crucial in data science?

• Data analysis is the process of collecting and storing data for future use.

• Data analysis is not important in data science as it can be done manually.

• Data analysis is only used for academic research and has no practical applications in the real world.

• Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It is crucial in data science because it helps in understanding the data, identifying patterns and trends, making predictions, and deriving insights that can drive business decisions.

Tags

NGSS.HS-ESS3-5

What are some common techniques used for data visualization?

• tables, histograms, network graphs

• box plots, treemaps, parallel coordinates

• bar charts, line charts, scatter plots, pie charts, and heat maps

• word clouds, bubble charts, radar charts

Tags

NGSS.MS-PS3-1

What is the purpose of box and whisker plots in data science?

• To identify the correlation between variables

• To compare multiple datasets

• To calculate the mean and median of a dataset

• To visualize the distribution of a dataset and identify outliers and spread of the data.

Tags

NGSS.MS-PS3-1

How can data collection be done effectively?

• Data collection can be done effectively by collecting a small sample size.

• Data collection can be done effectively by collecting data from unreliable sources.

• Data collection can be done effectively by randomly selecting data without any specific criteria.

• Data collection can be done effectively by following the steps mentioned in the explanation.

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NGSS.MS-PS2-2

What are some challenges faced during the data cleaning process?

• Missing data, inconsistent data formats, duplicate data, outliers, and handling errors or inconsistencies in the data.

• Data cleaning is not necessary, data cleaning is time-consuming, data cleaning is expensive

• Lack of data quality standards, data duplication, data inconsistency, data normalization issues

• Irrelevant data, incomplete data, incorrect data formats, handling missing values

What are the steps involved in data analysis?

• Defining the problem and objectives, Collecting and cleaning the data, Exploring and analyzing the data, Drawing conclusions and making predictions, Communicating the results

• Collecting and cleaning the data, Exploring and analyzing the data, Drawing conclusions and making assumptions, Communicating the results

• Defining the problem and objectives, Exploring and analyzing the data, Drawing conclusions and making predictions, Communicating the results

• Collecting and analyzing the data, Drawing conclusions and making assumptions, Communicating the results

Tags

NGSS.MS-ETS1-3

How can box and whisker plots be interpreted?

• By analyzing the five key summary statistics and visualizing the distribution, skewness, and outliers in the data.

• By examining the mode and median of the data.

• By looking at the range of the data.

• By analyzing the mean and standard deviation of the data.

What is the purpose of data visualization in data science?

• To make data more confusing and difficult to understand.

• To hide patterns, trends, and relationships within the data.

• To present data in a written format instead of visual format.

• To present data in a visual format and help understand patterns, trends, and relationships within the data.