The function ________ is used in R to create user-defined functions.
- create_function()
- define_function()
- function()
- user_function()
In R, the function() keyword is used to create user-defined functions. It is followed by a set of parentheses that can contain function arguments, and then the function body is enclosed in curly braces.
In dplyr, which function combines two data frames horizontally?
- bind_rows()
- cbind()
- combine()
- merge()
In dplyr, the bind_rows() function is used to combine two data frames horizontally. It stacks the rows of the second data frame below the first, assuming the columns have the same names and types. merge() is used for more complex merging, and cbind() is a base R function for column binding. combine() is not a valid function in this context.
When analyzing a case study for a logistics company, which key performance indicator (KPI) is most relevant for assessing delivery efficiency?
- Customer Acquisition Cost
- Employee Satisfaction Score
- On-time Delivery Rate
- Return on Investment (ROI)
The On-time Delivery Rate is the most relevant KPI for assessing delivery efficiency in a logistics company. It measures the percentage of deliveries that are made on time, reflecting the company's ability to meet customer expectations regarding delivery timelines.
To ensure effective data-driven decision making, data must be _______ and reliable.
- Abundant
- Accessible
- Accurate
- Adaptive
To ensure effective data-driven decision making, data must be accurate and reliable. Accuracy is crucial to avoid making decisions based on faulty information, and reliability ensures consistency in data quality.
In a scenario where a company is facing declining sales, what type of reporting technique would be best to identify the underlying causes?
- Comparative Analysis
- Descriptive Analysis
- Predictive Analysis
- Trend Analysis
Trend analysis would be the most suitable reporting technique in this scenario. It helps identify patterns and trends over time, allowing analysts to understand the factors contributing to declining sales. Comparative analysis focuses on comparisons between different entities, which may not be as effective in this context.
What role does predictive analytics play in data-driven decision making?
- It analyzes current data to identify patterns and trends.
- It focuses on creating data visualizations to communicate insights.
- It involves testing hypotheses and drawing conclusions from data samples.
- It uses historical data and statistical algorithms to make predictions about future outcomes.
Predictive analytics plays a crucial role in data-driven decision making by utilizing historical data and statistical algorithms to make predictions about future outcomes. It enables organizations to anticipate trends, make proactive decisions, and optimize processes based on expected future scenarios.
_______ is a technique used to handle imbalanced datasets in predictive model training.
- K-Means Clustering
- Mean Imputation
- Principal Component Analysis
- SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is a technique used to handle imbalanced datasets in predictive model training. It generates synthetic samples for the minority class to balance the dataset and improve the model's performance on minority class instances.
A data warehouse that is designed to focus on a specific business area or department is called a _______.
- Data Cluster
- Data Mart
- Data Silo
- Data Warehouse
A Data Mart is a subset of a data warehouse that is designed to focus on a specific business area or department. It contains a more specialized set of data that is relevant to a particular group of users.
During the transform phase of ETL, what is a key task performed on the data?
- Cleaning and restructuring
- Data extraction
- Data loading
- Indexing
In the transform phase of ETL (Extract, Transform, Load), a key task is cleaning and restructuring the data. This involves operations such as filtering, aggregating, and transforming the data to make it suitable for the target system or database.
The _______ method combines multiple weak models to create a stronger predictive model.
- Classification
- Clustering
- Ensemble
- Regression
The Ensemble method combines multiple weak models (such as decision trees) to create a more robust and accurate predictive model. This approach aims to reduce overfitting and improve generalization.