D-DS-FN-23 Dell Data Science Foundations Questions and Answers

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.

In linear regression, the model output includes coefficients that indicate the relative impact of the input variables on the outcome. These coefficients represent how much each input variable influences the predicted outcome.

A key aspect of a data scientist's profile is skeptical and critical thinking, which helps in questioning assumptions, validating data, and interpreting results accurately to ensure sound analysis and decision-making.

The Partial Autocorrelation Function (PACF) is examined to identify the order of the autoregressive (AR) component in an ARIMA model. It helps determine how much of the autocorrelation is explained by previous lags, assisting in selecting the appropriate AR order.

A box and whisker plot is ideal for visualizing the relationship between time spent and product category, especially when you have multiple categories. It shows the distribution of time spent in each product category, including the median, quartiles, and any potential outliers.

A key business driver for Big Data analytics adoption is the ability to identify new business opportunities by analyzing large and complex datasets, which can provide valuable insights for decision-making and strategy development.

Qualitative data represents non-numeric categories. Nominal data consists of named categories without any order, while ordinal data includes categories with a meaningful order but without consistent intervals between them.

Multinomial logistic regression should be used when the dependent variable has more than two categories (i.e., it is categorical with more than two possible outcomes). This differs from binary logistic regression, which is used when the dependent variable is binary (i.e., has two categories).

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 tokenizer for punctuation splits the text at punctuation marks, removing them and separating words. Contractions like "Don’t" become "dont" (punctuation removed, no splitting), resulting in: Stop, he, shouted, dont, go, there.

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.

A branch in a decision tree represents the outcome of a test or decision on a variable, leading to further splits or classifications based on the test result.

The decision tree having 17 layers and a leaf node with only three customers suggests that the tree is overfitting the data. This is because a deep tree with many layers can lead to overfitting, where the model fits the training data too closely, capturing noise and small details that do not generalize well to new data. Reducing the tree depth or pruning the tree can help mitigate overfitting.