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WorksheetsData Science
Total questions: 35
Worksheet time: 18mins
Which of the following statements best describes a DataFrame in Python, often used in Data Science?
A DataFrame is a collection of functions used to manipulate data.
A DataFrame is a two-dimensional, size-mutable, and labeled data structure similar to a table in a relational database.
A DataFrame is a Python library used for machine learning.
A DataFrame is a process of cleaning and transforming raw data into a usable format.
What is the primary goal of Data Science?
To collect as much data as possible
To develop theories about data structures
To extract useful insights from data
To maintain large databases
What is data in the context of Data Science?
Random information
Raw facts and figures
Organized knowledge
Mathematical formulas
Which of the following is NOT a type of data?
Structured
Unstructured
Semi-structured
Sequential
What does a database table primarily consist of?
Data points and axes
Rows and columns
Files and folders
Lists and arrays
In a relational database, what do columns represent?
Individual records
Attributes or fields
Keys for searching
Functions
Which Python library is most commonly used for data manipulation in Data Science?
NumPy
pandas
matplotlib
TensorFlow
What is a DataFrame in Python?
A collection of rows and columns like a table
A type of list that stores values
A special type of graph used for data visualization
A built-in function in Python
Which function in Python is used to read a CSV file into a DataFrame?
What does the term “data cleaning” refer to in data preparation?
Arranging data in a specific order
Removing errors and inconsistencies in data
Generating new data
Visualizing data
Which Python function is used to display the first few rows of a DataFrame?
display()
show()
head()
top()
In Data Science, what is a “feature”?
A graphical representation of data
A characteristic or attribute of the data
A type of machine learning model
A function used for data processing
What is the process of splitting a dataset into training and testing sets called?
Data augmentation
Data separation
Data splitting
Data sampling
Which of the following is a common data preparation technique?
Data scraping
Data normalization
Data interpretation
Data modeling
In Python, which library is primarily used for numerical operations in Data Science?
pandas
NumPy
seaborn
TensorFlow
What does the function describe() in pandas provide?
Visualizations for the DataFrame
Descriptive statistics for numerical columns
A summary of the data types in the DataFrame
A list of the first five rows of data
What is the purpose of data normalization?
To create new features
To scale data to a common range
To remove duplicates
To visualize data
In a DataFrame, what does the method dropna() do?
Drops duplicate rows
Removes rows with missing values
Filters data based on a condition
Sorts the DataFrame by a column
In Python, which of the following is commonly used for data visualization?
pandas
NumPy
matplotlib
SciPy
What is the primary role of functions in Python when working with Data Science?
To create new files
To automate repetitive tasks
To generate random data
To visualize data
What does the groupby() function in pandas do?
Groups data based on conditions and performs aggregation
Combines two DataFrames
Merges columns together
Sorts the DataFrame
What is the primary difference between NumPy arrays and pandas DataFrames?
NumPy arrays can only hold numerical data, while DataFrames can hold multiple data types
NumPy arrays are used for visualization, while DataFrames are not
NumPy arrays are two-dimensional, while DataFrames are always three-dimensional
There is no difference
What is one common use of the apply() function in pandas?
To apply a function along an axis of the DataFrame
To visualize data
To split data into smaller DataFrames
To merge multiple DataFrames
What is data wrangling?
The process of exploring and visualizing data
The process of gathering, cleaning, and organizing raw data
The process of deploying machine learning models
The process of summarizing data using statistics
Which of the following describes “missing data”?
Data with incorrect values
Data points that have not been collected or recorded
Data points that contain duplicates
Data points that are outliers
What is data transformation?
The process of converting data from one format to another
The process of deleting unnecessary data
The process of splitting data into training and testing sets
The process of building machine learning models
What is the purpose of using the "header=0" parameter when reading a CSV file in pandas?
To specify that the data contains numerical values
To indicate that the first row of the CSV contains column headers
To remove missing values from the dataset
To specify the data type of each column
Which of the following best describes discrete data?
Data that can take any value between two numbers
Data that can be measured with infinite precision
Data that represents distinct, countable values like "number of students"
Data that can be arranged in a meaningful order
Which type of data cannot be quantified and is classified into categories without a meaningful order?
Discrete data
Continuous data
Nominal data
Ordinal data
What is the appropriate data type for storing nominal data in a pandas DataFrame?
int64
float64
object
datetime64
Which of the following examples represents continuous data?
The number of pets in a household
The temperature in degrees Celsius
The color of a person's eyes
The letter grade of a student
What is the result of calling the info() function on a pandas DataFrame?
A statistical summary of the numerical columns
A list of column names, data types, and non-null values in the DataFrame
A preview of the first five rows in the dataset
A visualization of the dataset
Which pandas function would you use to check the frequency of categorical (qualitative) values in a DataFrame column?
value_counts()
describe()
dropna()
head()
In the context of data science, what does the parameter sep="," specify when reading a CSV file with pandas?
The file format is JSON
The data is separated by commas
The file contains missing values
The first row should be skipped
Which of the following best describes ordinal data?
Data where the order of the values is meaningless
Data that can be divided into an infinite number of values
Data that has a meaningful order, such as "low, medium, high"
Data that is measured using numbers only
