What is a common advantage of using cloud computing for data analysis compared to traditional on-premises solutions?
- Cost-effectiveness
- Limited Accessibility
- Scalability
- Security Concerns
One of the common advantages of using cloud computing for data analysis is scalability. Cloud services allow users to scale resources up or down based on demand, providing flexibility and efficiency in resource utilization. This makes it easier to handle varying workloads compared to traditional on-premises solutions.
When faced with a data discrepancy, a data analyst should communicate this by presenting _______.
- Corrective Action Plans
- Data Anomalies
- Executive Summaries
- Root Cause Analysis
Presenting data anomalies is crucial when faced with a discrepancy. This involves identifying and communicating irregularities or inconsistencies in the data, facilitating a clear understanding of potential issues and allowing for further investigation and resolution.
What is kurtosis in a data set, and how does it inform about the data distribution?
- Kurtosis measures the "tailedness" of a distribution
- Kurtosis measures the central tendency of a distribution
- Kurtosis measures the shape of a distribution's peak
- Kurtosis measures the spread of a distribution
Kurtosis is a measure of the "tailedness" or shape of the tails of a distribution. It informs about the presence of outliers and the sharpness of the distribution's peak.
How does the use of color in data visualization enhance the viewer's understanding of the data?
- Adds aesthetic appeal without affecting interpretation
- Emphasizes important data points and trends
- Represents a third dimension in 2D visualizations
- Serves as a substitute for labels
Color in data visualization can be used to emphasize important data points and trends, making it easier for viewers to interpret and understand the information. It adds a layer of information without cluttering the visualization.
How do you create a time series object in R?
- Using the data.frame() function
- Using the list() function
- Using the ts() function
- Using the vector() function
In R, the ts() function is commonly used to create time series objects. It allows you to specify the time points and values, providing a convenient way to work with time-based data.
In a team meeting, a data analyst is most effective when they:
- Avoid engaging in discussions with team members
- Clearly communicate findings and insights in a concise manner
- Focus solely on technical details
- Keep information to themselves for further analysis
A data analyst is most effective in a team meeting when they clearly communicate findings and insights in a concise manner. This promotes collaboration and ensures that everyone understands the key takeaways from the analysis.
In advanced reporting, ________ is used to dynamically filter and analyze data based on user-defined parameters.
- Drill-down
- Filtering
- Pivoting
- Slicing
Filtering is used in advanced reporting to dynamically narrow down and analyze data based on user-defined parameters. This allows users to focus on specific aspects of the data for a more detailed analysis.
Which HTTP method is most commonly used to retrieve data from a RESTful API?
- DELETE
- GET
- POST
- PUT
The most common HTTP method used to retrieve data from a RESTful API is GET. The GET method is used to request data from a specified resource and is considered a safe and idempotent operation in the context of RESTful architecture.
In a scenario where an organization is facing data breaches, which data governance policy should be prioritized for improvement?
- Access Control
- Data Classification
- Data Encryption
- Data Retention
In the context of data breaches, improving data encryption policies is crucial. Encryption helps protect sensitive data, making it unreadable to unauthorized users even if they gain access. Strengthening this policy enhances the organization's data security measures.
A company wants to analyze its sales data over the past five years to predict future trends. Which data warehousing model would best suit this requirement?
- Data Mart
- Snowflake Schema
- Star Schema
- Time-variant Data Model
The Time-variant Data Model, specifically suited for historical data, is ideal for analyzing sales trends over the past five years. Star and Snowflake Schemas are more focused on relationships between dimensions, and a Data Mart is a smaller subset of a data warehouse.