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

An Excel file is an example of quasi-structured data because it contains structured data (like rows and columns) but may have irregularities, such as varying data formats, missing headers, or mixed data types within cells, making it less strictly structured than a relational database.

Using tables of numbers to present all of the data visually should be avoided, as it can overwhelm the audience and make it harder to interpret key insights. Instead, visualizations should simplify data and focus on illustrating trends or patterns effectively.

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.

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.

Hadoop is primarily written in Java. It is built on the Java programming language to provide a framework for distributed storage and processing of large datasets.

Analysts value clarity and interpretability in data presentations. Clean, simple-to-understand visuals help them accurately assess the data, model outputs, and insights without unnecessary complexity.

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.

Three built-in data types in R are Boolean, integer, and character. These data types are fundamental to R and represent logical values (TRUE/FALSE), whole numbers, and text data, respectively.

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.

In ANOVA (Analysis of Variance), the null hypothesis is that all population means are equal to each other. The goal is to test whether there is evidence to reject this hypothesis, indicating that at least one population mean differs from the others.

Lift measures how many times more often X and Y occur together than would be expected if they were statistically independent. It is expressed as a ratio, and a lift greater than 1 indicates a positive association between X and Y.

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).

A Type I error occurs when the null hypothesis is rejected even though it is actually true. This is also known as a "false positive" in hypothesis testing.

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.