FREE Data Science Basic Questions and Answers

Question 1

What language is utilized in the field of data science?

Accepted Answer

Ruby

Answer

While Python and R are dominant in data science, Ruby is a general-purpose language that can also be utilized. It offers capabilities for data manipulation, scripting, and web development, which can be relevant in data-related projects, especially when integrating with web applications. Although its ecosystem for advanced statistical modeling and machine learning is less extensive than Python or R, Ruby's flexibility allows for its use in various data science tasks.

Question 2

Select the appropriate data science components.

Accepted Answer

All of the above

Answer

Effective data science is a multidisciplinary field that integrates several key components. Domain expertise is crucial for understanding the business context and interpreting results, while data engineering is vital for building robust data pipelines and preparing data. These, along with statistical knowledge, programming skills, and machine learning expertise, collectively form the essential pillars for successful data science projects.

Question 3

Which of these doesn't happen throughout the data science process?

Accepted Answer

Communication building

Answer

The standard data science process typically includes stages like discovery (problem definition), data preparation, model planning, model building, and operationalization, followed by communication of results. 'Communication building' as a distinct, non-analytical step is not part of this technical workflow. While effective communication of findings is paramount, 'communication building' itself doesn't represent a phase within the data science lifecycle.

Question 4

How many groups in total can data be characterized?

Accepted Answer

2

Answer

Data can broadly be characterized into two fundamental groups: qualitative (categorical) and quantitative (numerical). Qualitative data describes qualities or characteristics that cannot be measured numerically, while quantitative data consists of numerical values that can be measured or counted. These two types form the basis for all data collection, analysis, and modeling approaches.

Question 5

Select if the following assertion is accurate or not:

Accepted Answer

False

Answer

Without an assertion provided in the question, it's impossible to determine the specific reason why 'False' is the correct answer. This indicates a flaw in the question itself, as a statement is required to evaluate its accuracy. However, in a typical data science context, 'False' would be chosen if the unstated assertion presented a common misconception or an inaccurate claim about the field.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

FREE Data Science Basic Questions and Answers