Big Data Flashcards
16 cards from real MS-DS Master of Data science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 16 Big Data flashcards as text
Which of the following terms best describes the illustration below?
Answer: Big Data
The term 'Big Data' refers to extremely large, complex, and diverse datasets that traditional data processing applications struggle to handle. It is characterized by the '3 Vs': Volume (immense amount of data), Velocity (speed of data generation and processing), and Variety (diverse types of data). An illustration depicting these characteristics would best be described as Big Data.
Identify the accurate statement.
Answer: None of the above
Let's evaluate the statements: A) Data Cleaning focuses on identifying and correcting errors, not prediction. B) Representing data for insights is indeed both a science and an art (data visualization/communication). C) Machine learning focuses heavily on prediction based on learned properties from training data, especially in supervised learning. Given that B and C are largely accurate statements in general data science contexts, and A is clearly inaccurate, the instruction to identify the 'accurate statement' and the provided answer 'None of the above' suggests a very specific or nuanced interpretation where B and C are deemed insufficiently precise or universally true. However, without further context, C is generally considered an accurate description of a core ML focus.
Which of the following big data traits is comparatively more important to data science?
Answer: Variety
While Volume, Velocity, and Veracity are all crucial traits of Big Data, Variety is often considered particularly important for data science. Data scientists frequently work with diverse data types—structured, semi-structured, and unstructured—from various sources. The ability to integrate, process, and derive insights from this heterogeneous data is a core challenge and strength of data science.
Which of the following analytic skills does an information management organization offer?
Answer: All of the above
An information management organization, especially in the context of Big Data and data science, offers a range of analytic skills and capabilities. These include Information Integration (combining data from various sources), Content Management (organizing and managing diverse content), and Stream Computing (processing data in real-time as it arrives). All these are essential for effectively leveraging data.
Identify the incorrect statement.
Answer: Big Data is just about lots of data
The statement 'Big Data is just about lots of data' is incorrect because Big Data is defined by more than just its immense volume. It also encompasses velocity (the speed at which data is generated and processed), variety (the diverse types of data), and often veracity (the quality and trustworthiness of the data). Reducing Big Data to merely its size misses these other critical dimensions.
After gathering the data, which of the following steps does the data scientist perform?
Answer: Data Cleansing
After gathering data, the immediate and crucial next step for a data scientist is typically data cleansing (also known as data cleaning or data scrubbing). This involves identifying and correcting errors, inconsistencies, missing values, and duplicates to ensure the data is accurate and reliable for subsequent analysis and modeling. High-quality data is foundational for meaningful insights.
Which of the following focuses on the data's (previously unidentified) qualities being discovered?
Answer: Data mining
Data mining is the process of discovering patterns, insights, and knowledge from large datasets, often previously unidentified. It involves using various techniques from statistics, machine learning, and database systems to extract valuable information. The core objective is to uncover hidden relationships and predictive models within the data.
Data that list all findings in a category are referred to as _________ data.
Answer: summarized
Summarized data presents aggregated information, often in tables or charts, that condenses raw findings into meaningful categories. This process involves calculating totals, averages, or counts to provide an concise overview rather than individual data points, making it easier to understand trends and patterns.
Which of the subsequent is an illustration of raw data?
Answer: All of the above
Raw data refers to unprocessed, original data collected directly from its source. Real-time GPS files, initial time-series temperature values, and original sonar swath files are all examples of data in its most basic, unanalyzed form before any cleaning, transformation, or aggregation.
Identify the accurate statement.
Answer: Primary data is original source of data
Primary data is information collected directly by the researcher or organization for a specific purpose, making it the original source. In contrast, secondary data is information that has already been collected by someone else and is available from existing sources.
Which of the following information is entered into a formula to provide findings that are widely accepted?
Answer: Processed
Processed data, also known as clean or transformed data, is information that has been organized, structured, and validated after collection. This preparation makes it suitable for analysis, ensuring accuracy and consistency when entered into formulas to derive reliable and widely accepted findings.
Which of the following languages should the question mark in the illustration below replace?
Answer: Java
Without the specific illustration, it's impossible to provide context. However, Java is a widely used, high-level, class-based, object-oriented programming language known for its 'write once, run anywhere' capability. It is extensively used in enterprise-level applications, Android development, and big data technologies, making it a common language in data science contexts.
The problems with big data veracity go beyond volume, diversity, and velocity.
Answer: True
The '3 V's' (Volume, Velocity, Variety) are commonly associated with Big Data, but 'Veracity' is a fourth crucial dimension. Veracity refers to the trustworthiness, accuracy, and quality of the data, addressing issues like bias, noise, and abnormalities that can significantly impact analysis and decision-making.
Which of the subsequent processes involves organizing datasets to make analysis easier?
Answer: Data tidying
Data tidying is the process of structuring datasets to make them easy to analyze. It involves ensuring that each variable forms a column, each observation forms a row, and each type of observational unit forms a table, which simplifies data manipulation and statistical modeling.
Which of the following aspects of untidy data is most frequently problematic?
Answer: All of the above
Untidy data often presents several issues that hinder analysis. These include having a single observational unit spread across multiple tables, variables being stored in both rows and columns, and column headers containing values rather than variable names, all of which complicate data processing and interpretation.
Identify the accurate statement.
Answer: Nearly 80% of data analysis is spent on the cleaning and preparing data
Data scientists often report that a significant portion of their time, typically around 80%, is dedicated to data cleaning and preparation tasks. This involves identifying and correcting errors, handling missing values, transforming data formats, and structuring it for analysis, highlighting its critical role in ensuring data quality and reliable insights.