What is the process of extracting patterns and information from data called?
A. Data Wrangling
B. Data Visualization
C. Data Engineering
D. Data Mining
Which statistical measure represents the middle value of a dataset when it is sorted in ascending order?
A. Median
B. Mean
C. Standard Deviation
D. Mode
In Data Science, what is the purpose of feature engineering?
A. To extract features from data
B. To visualize data features
C. To clean data features
D. To model data features
What is the term for a machine learning algorithm that learns from historical data to make predictions about the future?
A. Regression
B. Clustering
C. Classification
D. Supervised Learning
Which of the following is an example of an unsupervised learning algorithm used in clustering data?
A. Linear Regression
B. K-Means Clustering
C. Decision Trees
D. Logistic Regression
What is the primary focus of Data Science?
A. Data Cleaning
B. Data Visualization
C. Data Analysis
D. Data Storage
Which of the following is NOT a common data format used in Data Science projects?
A. JSON
B. XML
C. CSV
D. HTML
Which of the following is a technique used to handle missing data in a dataset?
A. Data Augmentation
B. Data Imputation
C. Data Transformation
D. Data Normalization
What is the primary purpose of data visualization in Data Science?
A. To make data more complicated
B. To simplify complex data
C. To increase data complexity
D. To store data
Which step in the Data Science process involves building and training predictive models?
A. Data Collection
B. Data Visualization
C. Data Cleaning
D. Model Building
Which technology is often used to process and analyze large-scale data sets in Data Science?
A. Hadoop
B. SQL
C. Python
D. HTML
What does the acronym "EDA" stand for in Data Science?
A. Exploratory Data Analysis
B. Effective Data Algorithms
C. Extracted Data Aggregation
D. Efficient Data Assessment
Which of the following is NOT a key skill required for a Data Scientist?
A. Data Visualization
B. Storytelling
C. Database Administration
D. Machine Learning
Which step in the Data Science process involves selecting the appropriate model and algorithm for analysis?
A. Data Cleaning
B. Data Visualization
C. Data Collection
D. Model Building
In Data Science, what is the term for a dataset that contains both input features and output labels?
A. Test Data
B. Training Data
C. Validation Data
D. Unlabeled Data
What is the primary goal of Data Science?
A. Data Visualization
B. Data Cleaning
C. Predictive Analytics
D. Extracting Data from APIs
Which programming language is commonly used for Data Science tasks?
A. Java
B. Python
C. C++
D. JavaScript
Which step in the Data Science process involves understanding and preparing the data for analysis?
A. Data Collection
B. Data Visualization
C. Data Cleaning
D. Model Building
What is the term for a data point that falls far from the rest of the data in a dataset?
A. Outlier
B. Median
C. Mean
D. Variance
Which of the following is NOT a type of machine learning algorithm commonly used in Data Science?
A. Linear Regression
B. K-Means Clustering
C. Decision Trees
D. Object-Oriented Programming
What is the term for the process of finding and correcting errors in a dataset?
A. Data transformation
B. Data cleaning
C. Data aggregation
D. None of the above
In data science, what does the acronym "ETL" stand for?
A. Encode, Tokenize, Leverage
B. Estimate, Test, Label
C. Extract, Transform, Load
D. Explore, Train, Learn
What is the primary goal of A/B testing in data analysis?
A. To create visualizations
B. To build predictive models
C. To collect more data
D. To compare two versions of a webpage or product to determine which one performs better
In the context of machine learning, what is the purpose of regularization techniques such as L1 and L2 regularization?
A. To prevent overfitting by adding a penalty term to the loss function
B. To remove outliers from the data
C. To increase model complexity
D. To reduce dimensionality
What is the term for the process of converting categorical data into numerical form for machine learning?
A. Data normalization
B. Text vectorization
C. Feature scaling
D. One-hot encoding
What is the primary purpose of feature scaling in machine learning?
A. To increase model complexity
B. To add more features to the dataset
C. To bring all features to a similar scale for better model performance
D. To remove irrelevant features
What is the term for the process of converting text data into numerical form for machine learning?
A. Feature scaling
B. One-hot encoding
C. Data normalization
D. Text vectorization
What does the acronym "API" stand for in the context of data and software integration?
A. Application Programming Interface
B. Advanced Programming Integration
C. Algorithmic Programming Interface
D. Automated Program Integration
In the context of data analysis, what is the primary goal of hypothesis testing?
A. To visualize data
B. To summarize data
C. To build predictive models
D. To determine if there is a significant difference or effect
What statistical technique is used to estimate population parameters from a sample of data?
A. Descriptive statistics
B. Exploratory data analysis
C. Inferential statistics
D. Data preprocessing
What is the primary goal of exploratory data analysis (EDA)?
A. To build predictive models
B. To create visualizations
C. To collect more data
D. To understand the data's underlying structure and patterns
What does the term "cross-validation" refer to in machine learning?
A. A technique for assessing a model's performance on unseen data
B. A method for finding optimal hyperparameters
C. A type of ensemble learning method
D. A way to preprocess data
What is the main purpose of the K-means clustering algorithm?
A. To reduce dimensionality
B. To classify data into categories
C. To perform regression analysis
D. To group data points into clusters based on similarity
Which data visualization technique is suitable for displaying the distribution of a single numerical variable?
A. Scatter plot
B. Box plot
C. Histogram
D. Bar chart
What does the acronym "SQL" stand for in the context of databases?
A. System Query Language
B. Structured Query Language
C. Structured Question Language
D. None of the above
Which statistical test is used to determine if there is a significant association between two categorical variables?
A. Chi-squared test
B. T-test
C. ANOVA
D. Regression analysis
In the context of data ethics, what does "bias mitigation" refer to?
A. Increasing the sample size
B. Improving model accuracy
C. Removing outliers from a dataset
D. Reducing biases in data collection
What does the term "overfitting" mean in machine learning?
A. The model fits the training data too closely and performs poorly on new data
B. The model generalizes well to new data
C. The model is too simple and underperforms on the training data
D. The model is perfectly accurate on all data
In the CRISP-DM data mining process model, what does "DM" stand for?
A. Data Modeling
B. Data Mining
C. Data Manipulation
D. None of the above
Which Python library is commonly used for natural language processing tasks such as text tokenization and language understanding?
A. Matplotlib
B. Scikit-learn
C. spaCy
D. Seaborn
