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

The project goals component of a final presentation provides a succinct overview of the business situation that led to the initiation of the project. It sets the context for why the project was undertaken and what it aimed to achieve.

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 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.

Hypothesis testing is the appropriate approach to determine whether a significant shift in spending has occurred. By comparing pre- and post-program spending, hypothesis testing can assess if the observed changes are statistically significant.

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.

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.

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.

Information gain is calculated as the difference between the entropy of the node and the entropy after the split. For Variable B, the information gain is 0.8 − 0.4 = 0.4, which is higher than the gain from Variable A (0.3). The Variable B is selected for partitioning.

Hive converts SQL-like commands into execution plans that run as Tez, Spark, or MapReduce jobs on a Hadoop cluster, enabling users to query large datasets using a familiar query language.

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.

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.

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.

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.

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.