EMC D-DS-FN-23 Dumps

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 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.

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.

Ambari, Oozie, and Zookeeper are tools used for cluster and workflow management in Hadoop. Ambari manages and monitors clusters, Oozie handles workflow scheduling, and Zookeeper coordinates distributed processes.

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.

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.

The formula for Naïve Bayes is:

For the renewal class, we are given:

● P(Class = Renewal) = 0.8 (since 80% renew the policy)

● P(Age > 65 years | Renewal) = 0.3

● P(Housing = Own | Renewal) = 0.9

● P(Renewal Month = August | Renewal) = 0.1

P(Renewal) = P(Renewal) × P(Age > 65 years | Renewal) × P(Housing = Own | Renewal) × P(Renewal Month = August | Renewal)

P(Renewal) = 0.8 × 0.3 × 0.9 × 0.1 = 0.0216

MapReduce is designed to process a few large files that are split into blocks and then processed in parallel across multiple machines. This approach allows for efficient distributed processing of large datasets.

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.

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).

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.

Feature selection involves identifying the most useful input variables that contribute meaningfully to the model, improving accuracy and reducing complexity during the model building phase.

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

A graph of the WSS (Within-Cluster Sum of Squares) versus the value of K helps determine the optimal number of clusters. The "elbow" point on the graph represents the value of K at which adding more clusters no longer significantly improves the WSS.

The R-squared value is approximately 0.96, indicating that 96% of the variance in the data is explained by the model.

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.

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.