Master of Data Science Flashcards

Which of the following should the question mark in the accompanying figure be used in place of?

Answer: Data Science

Without the accompanying figure, this question implies a diagram illustrating the intersection or overarching field of various disciplines like statistics, computer science, and domain expertise. Data Science is precisely this interdisciplinary field that combines these elements to extract knowledge and insights from data. Therefore, it is the most fitting term to represent the central concept.

Identify the accurate statement.

Answer: Raw data is original source of data

Raw data refers to the initial, unprocessed information collected directly from its source. It is the original form of the data before any cleaning, transformation, or analysis has been applied. This makes it the fundamental starting point for any data science project.

What tasks are carried out by data scientists?

Answer: All of the above

Data scientists perform a wide array of tasks throughout the data lifecycle. They are responsible for defining the core questions to be answered, creating reproducible code for consistent and verifiable results, and critically challenging findings to ensure accuracy and validity. These activities are all integral to effective data analysis and insight generation.

Which of the following languages is crucial for data science?

Answer: R

R is a powerful and widely used programming language specifically designed for statistical computing and graphics. It offers an extensive ecosystem of packages for data manipulation, visualization, statistical modeling, and machine learning, making it a crucial tool for many data scientists. While Python is also very popular, R remains a cornerstone in the field.

Identify the incorrect statement.

Answer: Data visualization is the organization of information according to preset specifications

Data visualization is the graphical representation of information and data to help users understand patterns, trends, and outliers. It's about making complex data more accessible and understandable through charts, graphs, and maps, rather than simply organizing it according to preset specifications. The other options describe valid data manipulation techniques.

Which of the following approaches should be utilized when posing questions about data analysis?

Answer: Find out the question which is to be answered

Effective data analysis always begins with a clear understanding of the problem or question that needs to be solved. Defining the question precisely helps to focus the analysis, guide data collection, and ensure that the insights derived are relevant and actionable. Without a well-defined question, data analysis can become unfocused and yield irrelevant results.

One of the fundamental abilities in data science is which of the following?

Answer: Data Visualization

Data visualization is a fundamental skill in data science because it enables practitioners to explore datasets, identify patterns, communicate findings effectively, and present complex information in an understandable format. It's often the first step in understanding new data and a critical component of explaining results to stakeholders. While machine learning and statistics are also crucial, visualization is key for both exploration and communication.

Which of the following traits best describes a hacker?

Answer: Willing to find answers on their own

In the context of data science and technology, a 'hacker' (often used positively) refers to someone who is resourceful, curious, and driven to explore, experiment, and find solutions independently. This trait emphasizes a proactive and self-reliant approach to problem-solving and learning, which is highly valued in the field.

Which of the aforementioned describes processed data?

Answer: All steps should be noted

Processed data has undergone various transformations, cleaning, and manipulation steps from its raw form. It is essential to meticulously document every step taken during this processing to ensure reproducibility, transparency, and traceability of the data's journey. This documentation allows others to understand and replicate the analysis.

Which of the following methods falls under the category of applied machine learning?

Answer: Boosting

Boosting is an ensemble machine learning technique that combines multiple 'weak' learning models to create a 'stronger' predictive model. It sequentially builds models, with each new model attempting to correct the errors of the previous ones. This makes boosting a specific and widely used method within applied machine learning.

Which of the following methods also goes by the name "bagging"?

Answer: Bootstrap aggregating

Bagging is an acronym that stands for 'Bootstrap Aggregating.' It is an ensemble machine learning technique where multiple versions of a predictor are generated by taking bootstrap samples (random samples with replacement) of the training data. The predictions from these multiple models are then aggregated to produce a final, more robust prediction.

What qualifies as a characteristic of raw data?

Answer: Original version of data

Raw data is the initial, unprocessed form of information collected directly from its source. It represents the original version of the data before any cleaning, transformation, or analysis has been applied. This characteristic distinguishes it from processed or analyzed data.

Which of the subsequent CLI commands also has a file renaming option?

Answer: mv

The `mv` command in a Command Line Interface (CLI) is primarily used to move files or directories from one location to another. However, it can also be used to rename a file or directory by moving it to the same directory but specifying a new name. For example, `mv old_name.txt new_name.txt` renames the file.

Which of the following uses information about one object to forecast the values of another?

Answer: Predictive

Predictive analysis focuses on using historical data, statistical algorithms, and machine learning techniques to forecast future outcomes or unknown values. It involves building models that can predict a target variable based on the relationships identified with other input variables. This is distinct from exploratory, inferential, or descriptive analysis.

What is the primary objective of statistical modeling?

Answer: Inference

While statistical modeling can involve summarizing data, its primary objective is often inference. Inference involves drawing conclusions or making predictions about a larger population based on a sample of data, quantifying uncertainty, and understanding the relationships between variables. It allows us to generalize findings beyond the observed data.

Which of the following analyses aids in determining how a variable change affects a system?

Answer: Causal

Causal analysis is specifically designed to identify and understand cause-and-effect relationships between variables. It helps determine whether a change in one variable directly leads to a change in another, providing insights into how interventions or modifications might affect a system. This is crucial for making informed decisions and predictions about system behavior.