For large-scale data sets, _______ techniques are applied to manage and interpret the data efficiently.
- Clustering
- Normalization
- Sampling
- Stratification
Sampling techniques are applied to large-scale data sets to manage and interpret the data efficiently. By analyzing a subset of the data, meaningful insights can be derived without the need to process the entire dataset.
In the context of time series analysis, what does the acronym ARIMA stand for?
- Advanced Regression for Integrated Models and Analysis
- Arithmetic Recursive Integrated Moving Average
- Autoregressive Integrated Moving Average
- Average Range of Integrated Moving Analysis
ARIMA stands for Autoregressive Integrated Moving Average. It is a popular time series forecasting method that combines autoregression, differencing, and moving average components.
The integration of various data sources, tools, and methodologies to achieve a project goal is referred to as _________ integration.
- Data
- Method
- System
- Tool
Data integration involves the seamless combination of various data sources, tools, and methodologies to achieve a project goal. This process ensures that different data sets can work together harmoniously and contribute to the overall success of the project.
To count the number of rows in a SQL table, you would use the _______ function.
- AVG
- COUNT
- MAX
- SUM
The COUNT function in SQL is used to count the number of rows in a table. It is commonly used in conjunction with the SELECT statement to retrieve the count of rows that meet certain criteria.
What is the first step in the problem-solving process?
- Define the problem
- Evaluate the results
- Generate possible solutions
- Implement the solution
The first step in the problem-solving process is to clearly define the problem. Without a clear understanding of the problem, it is difficult to develop effective solutions.
_______ is a dimensionality reduction technique used to reduce the number of features in a dataset while retaining most of the information.
- K-Means Clustering
- Principal Component Analysis (PCA)
- Random Forest
- Support Vector Machine (SVM)
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while retaining essential information. It is commonly used to improve computational efficiency and remove redundant features.
For an e-commerce website, which KPI effectively measures customer retention and loyalty?
- Average Order Value (AOV)
- Click-Through Rate (CTR)
- Conversion Rate
- Customer Lifetime Value (CLV)
Customer Lifetime Value (CLV) is a crucial KPI for measuring customer retention and loyalty in an e-commerce setting. It represents the total value a customer is expected to bring to the business over their entire relationship. CTR, Conversion Rate, and AOV are important but focus on different aspects of e-commerce performance.
To add a condition to a SQL query for groupings, the ________ clause is used.
- GROUP
- HAVING
- ORDER BY
- WHERE
The HAVING clause in SQL is used to add a condition to a query when using GROUP BY. It allows you to filter the results of a GROUP BY based on a specified condition.
What is the purpose of a standard deviation in a data set?
- It calculates the average of the data set
- It counts the number of data points
- It identifies the minimum value in the data set
- It measures the spread or dispersion of data points
Standard deviation measures the spread or dispersion of data points from the mean. It provides insights into the variability of the data set, helping analysts understand the distribution of values.
What is the process of dividing a data set into multiple subsets called in data mining?
- Data Discretization
- Data Partitioning
- Data Segmentation
- Data Splitting
The process of dividing a data set into multiple subsets is called Data Splitting. It involves separating the data into training and testing sets to assess the performance of a model on unseen data. Data Partitioning, Data Segmentation, and Data Discretization refer to different techniques in data preprocessing.