In time series analysis, _______ is a common method for forecasting future data points.
- Clustering
- Linear Regression
- Moving Average
- Principal Component Analysis
In time series analysis, Moving Average is a common method for forecasting future data points. It involves calculating the average of a set of values over a moving window, providing a smoothed representation of the underlying trend.
How can you handle missing values in a dataset in R?
- na.rm = TRUE
- removeNA()
- na.omit()
- deleteNA()
The correct option is na.omit(). This function is used to handle missing values in a dataset by omitting (removing) rows with missing values. Options like na.rm = TRUE are used in specific functions to handle missing values within those functions, but they are not standalone functions for handling missing data.
When creating a pie chart, what is the key factor to consider for effectively communicating data?
- Colors
- Labels
- Proportions
- Size
The key factor in a pie chart is accurately representing proportions. Each slice should reflect the relative size of the corresponding data category. Colors, labels, and size are important, but proportions ensure the viewer interprets the data correctly.
What is the mean of a data set and how is it calculated?
- The middle value in a sorted list
- The most frequently occurring value
- The range of values
- The sum of all values divided by the number of values
The mean of a data set is calculated by summing up all values and dividing by the total number of values. It represents the average value in the data set.
Which SQL clause is used to filter the records returned from a SELECT query?
- FROM
- GROUP BY
- ORDER BY
- WHERE
The WHERE clause is used to filter records returned from a SELECT query in SQL. It allows you to specify conditions that the retrieved data must meet.
What is a 'fact table' in a data warehouse and how does it differ from a 'dimension table'?
- Fact table contains descriptive data, whereas dimension tables contain quantitative data.
- Fact table contains quantitative data and is connected to dimension tables, whereas dimension tables provide descriptive information about data in the fact table.
- Fact table is used for historical data, whereas dimension table is used for real-time data.
- Fact table is used for indexing, whereas dimension table is used for primary storage.
A 'fact table' in a data warehouse contains quantitative data and is connected to dimension tables, which provide descriptive information about the data in the fact table. The fact table is the core of the data warehouse and supports analytics.
What is the role of change data capture in ETL processes?
- Aggregating data for reporting purposes
- Capturing and tracking changes in source data over time
- Encrypting data during transfer
- Indexing data for faster retrieval
Change Data Capture (CDC) in ETL processes involves identifying and tracking changes in source data over time. This allows for the extraction of only the modified data, reducing processing time and ensuring data accuracy in the target system.
In BI tools, what is the purpose of a dashboard?
- Data Cleaning
- Data Encryption
- Data Storage
- Presenting Key Metrics
The purpose of a dashboard in BI tools is to present key metrics and insights in a visually accessible format. Dashboards provide a consolidated view of important information, making it easier for users to monitor performance and draw conclusions from the data.
In a real-time stock trading application, what algorithm would you use to ensure that you always get the best or optimal solution for stock price analysis?
- Bellman-Ford Algorithm
- Dijkstra's Algorithm
- Dynamic Programming
- Greedy Algorithm
A Greedy Algorithm is often used in real-time stock trading applications for optimal solutions. It makes locally optimal choices at each stage, aiming to find the global optimum. This is crucial for quickly making decisions in dynamic and time-sensitive environments. Dijkstra's Algorithm, Bellman-Ford Algorithm, and Dynamic Programming may not be as suitable for real-time stock price analysis.
In ETL, what is the significance of data staging?
- Direct loading of data into the target system
- Final storage of cleaned data
- Skipped phase in ETL process
- Temporary storage of raw data before transformation
Data staging in ETL is the temporary storage of raw data before it undergoes transformation. It allows for data validation, debugging, and auditing before the cleaned data is loaded into the target system.