D-DS-FN-23 Questions and Answers

When communicating project results to executives and the project sponsor, it's important to focus on business outcomes and benefits, ensuring they understand the value the project brings to the organization rather than the technical details.

The project goals component of a final presentation provides a succinct overview of the business situation that led to the initiation of the project. It sets the context for why the project was undertaken and what it aimed to achieve.

The metrics used to calculate relevance in text analysis are TF (Term Frequency) and IDF (Inverse Document Frequency). TF measures how frequently a term appears in a document, while IDF adjusts the importance of the term based on how common it is across all documents. Their combination, known as TF-IDF, helps evaluate the relevance of terms in a given document relative to a larger corpus.

A line chart is specifically designed to display time series data. It shows data points in a sequential order, making it easy to observe trends over time.

In a Moving Average (MA) process of order q, the current deviation from the mean is modeled as a linear combination of the previous q deviations (errors).

The primary role of a business intelligence analyst on an analytics project is to provide business-domain expertise. They bridge the gap between the technical aspects of the project and the business requirements, ensuring that the data and analysis align with the business objectives.

Defining categories such as performance levels falls under the model planning phase, where data is prepared and structured for analysis. This step involves selecting techniques and identifying how data will be used in modeling.

A logarithmic transformation is commonly used to reduce right skewness (long tail to the right) and make data more normally distributed.

K-means clustering is best suited for numeric data, as it relies on calculating the Euclidean distance between data points, which is meaningful for numerical values.

Both matrix and array data structures in R can only contain one data type across all their elements, ensuring consistency in the structure.

Naïve Bayes classifiers are particularly effective for handling high-dimensional data (many features) and are resistant to overfitting. Despite the strong independence assumption, they can still perform well in many real-world scenarios, especially with large datasets and many features.