Data Visualization and Analysis Techniques

Front

What is data collection in the context of data science?

Back

Gathering and acquiring data from various sources.

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

Front

Why is data cleaning important in data science?

Back

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

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.

Front

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

Back

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

Tags

CCSS.HSS.ID.A.1

CCSS.6.SP.B.4

Front

Define data analysis.

Back

The process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.

Front

What is the role of exploratory data analysis (EDA)?

Back

To analyze data sets to summarize their main characteristics, often using visual methods.

Tags

CCSS.6.SP.B.5B

Front

What is a histogram used for in data visualization?

Back

To represent the distribution of numerical data by showing the number of data points that fall within a specified range of values.

Tags

CCSS.HSS.ID.A.1

CCSS.6.SP.B.4

Front

Explain the concept of outliers in data analysis.

Back

Outliers are data points that differ significantly from other observations in a dataset, which can indicate variability in measurement or may suggest experimental errors.

Front

What is the significance of the mean in data analysis?

Back

The mean is the average of a set of numbers, providing a central value that summarizes the data.

Tags

CCSS.6.SP.A.3