Pass the EMC Data Science D-DS-FN-23 Questions and answers with CertsForce

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.

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.

Linear regression is the appropriate technique for estimating a continuous target variable, such as home sale price, based on other continuous and categorical input variables like square footage, number of bedrooms, and lot size.

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

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.

Data conditioning in the data preparation phase refers to the process of cleaning the data, normalizing datasets, and performing transformations to ensure the data is in a suitable format for analysis or modeling.

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.

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.

P(Milk∩Eggs) is the probability of both Milk and Eggs being bought together, which is the count for Milk and Eggs (110).

P(Milk)) is the probability of Milk being bought, which is the total for Milk (233) divided by the grand total (374).

P(Eggs) is the probability of Eggs being bought, which is the total for Eggs (150) divided by the grand total (374).

The value of the lift for the association rule Milk -> Eggs is approximately 1.18.