In the context of dashboard design, what is the significance of the 'data-ink ratio'?
- It calculates the ratio of data points to the size of the dashboard, optimizing space utilization.
- It evaluates the ratio of data points to the ink color used, emphasizing the importance of color coding.
- It measures the ratio of data points to the total number of points on a chart, ensuring data accuracy.
- It represents the ratio of data to the total ink used in a visualization, emphasizing the importance of minimizing non-data ink.
The 'data-ink ratio' represents the proportion of ink in a visualization that conveys meaningful information. It emphasizes the importance of maximizing the ink used to represent data while minimizing non-data ink, promoting clarity and efficiency in dashboard design.
How does 'snowflake schema' in a data warehouse improve upon the star schema?
- It adds more complexity to the data model.
- It eliminates the need for dimension tables.
- It increases the number of redundant fields in dimension tables.
- It normalizes dimension tables, reducing redundancy and improving data integrity.
The 'snowflake schema' improves upon the star schema by normalizing dimension tables, reducing redundancy, and improving data integrity. This makes the schema more flexible and scalable, allowing for efficient storage and maintenance of data in the data warehouse.
When the following is executed: data = [1, 2, 3, 4, 5]; filtered = filter(lambda x: x % 2 == 0, data); print(list(filtered)), what is the output?
- [1, 2, 3, 4, 5]
- [1, 3, 5]
- [2, 4]
- [4]
The filter function with the lambda expression filters out the even numbers from data, resulting in the output [2, 4].
_______ is a technique in unsupervised learning used to reduce the dimensionality of data.
- Decision Trees
- K-Means Clustering
- Principal Component Analysis (PCA)
- Support Vector Machines (SVM)
Principal Component Analysis (PCA) is a technique in unsupervised learning used to reduce the dimensionality of data by transforming it into a set of linearly uncorrelated variables known as principal components.
In Excel, what is the difference between relative and absolute cell references?
- Absolute references are used for text data, and relative references are used for numeric data.
- Absolute references change automatically when a formula is copied, while relative references stay the same.
- Relative references adjust when a formula is copied to another cell, while absolute references remain constant.
- Relative references are only used in complex formulas.
The key difference is that relative references adjust when a formula is copied to another cell, whereas absolute references remain constant. This distinction is crucial for maintaining the integrity of formulas in Excel.
In database normalization, the process of organizing data to reduce redundancy is referred to as _______.
- Aggregation
- Denormalization
- Indexing
- Normalization
In database normalization, the process of organizing data to reduce redundancy is referred to as "Normalization." This involves organizing data to minimize duplication and dependency, leading to a more efficient and structured database design.
In dplyr, to perform operations on multiple columns at once, the _______ function is used.
- across()
- group_by()
- mutate()
- summarize()
The across() function in dplyr is used to perform operations on multiple columns simultaneously. It is particularly useful when you want to apply the same operation to multiple columns in a data frame.
What advanced metric is used to assess the long-term value of a customer to a business?
- Cost per Acquisition (CPA)
- Customer Lifetime Value (CLV)
- Net Promoter Score (NPS)
- Return on Investment (ROI)
Customer Lifetime Value (CLV) is a key metric in assessing the long-term value of a customer to a business. It represents the total revenue a business expects to earn from a customer throughout their entire relationship. ROI, NPS, and CPA are important metrics but focus on different aspects.
The process of continuously checking and ensuring the quality of data throughout the project life cycle is known as _________.
- Data Mining
- Data Quality Management
- Data Validation
- Data Wrangling
Data Quality Management involves continuously checking and ensuring the quality of data throughout the project life cycle. It includes processes to identify and correct errors, inconsistencies, and inaccuracies in the data.
What is the impact of big data technologies on data-driven decision making?
- Enhanced scalability and processing speed
- Increased data security concerns
- Limited applicability to small datasets
- Reduced need for data analysis
Big data technologies, with enhanced scalability and processing speed, enable organizations to process and analyze vast amounts of data quickly. This facilitates more informed and timely data-driven decision making.