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

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 Hadoop Distributed File System (HDFS) is a master/slave system designed to run on commodity hardware, meaning it can efficiently store large volumes of data across inexpensive, off-the-shelf hardware.

Recalibration of the model should be assessed during the Operationalize phase, as this is when the model is deployed and monitored in real-world conditions, making it easier to identify if it requires adjustments based on its performance.

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.

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 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.

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).

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

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

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.

The EXCEPT operator returns rows that exist in the result set of the first SELECT statement but not in the second. It removes duplicates and only shows the difference between the two datasets.

The Discovery phase involves understanding the business problem by conducting project sponsor interviews, gathering requirements, and drafting a problem statement to define the analytic objectives clearly.

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.

An itemset is a set of discrete variables (or items) that are linked or appear together in a transaction or dataset, commonly used in association rule mining to find relationships between items.