Data Science Miscellaneous — Part 1 MCQs with Explanations

Exploratory Data Analysis (EDA) is primarily used to gain insights into the data's underlying structure and patterns. It helps in identifying key variables, relationships, and outliers that can influence further analysis. While creating visualizations and collecting data are part of the data analysis process, the core objective of EDA is to understand the data's characteristics and inform the next steps in the analysis.

Chi-squared test is the correct answer to “Which statistical test is used to determine if there is a significant association between two categorical variables”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Reducing biases in data collection is the correct answer to “In the context of data ethics, what does "bias mitigation" refer to”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

The model is too simple and underperforms on the training data is the correct answer to “What does the term "overfitting" mean in machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Data Mining is the correct answer to “In the CRISP-DM data mining process model, what does "DM" stand for”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

spaCy is the correct answer to “Which Python library is commonly used for natural language processing tasks such as text tokenization and language understanding”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

A technique for assessing a model's performance on unseen data is the correct answer to “What does the term "cross-validation" refer to in machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To group data points into clusters based on similarity is the correct answer to “What is the main purpose of the K-means clustering algorithm”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Histogram is the correct answer to “Which data visualization technique is suitable for displaying the distribution of a single numerical variable”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Structured Query Language is the correct answer to “What does the acronym "SQL" stand for in the context of databases”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To bring all features to a similar scale for better model performance is the correct answer to “What is the primary purpose of feature scaling in machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Text vectorization is the correct answer to “What is the term for the process of converting text data into numerical form for machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Application Programming Interface is the correct answer to “What does the acronym "API" stand for in the context of data and software integration”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To determine if there is a significant difference or effect is the correct answer to “In the context of data analysis, what is the primary goal of hypothesis testing”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Inferential statistics is the correct answer to “What statistical technique is used to estimate population parameters from a sample of data”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Data cleaning is the correct answer to “What is the term for the process of finding and correcting errors in a dataset”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Extract, Transform, Load is the correct answer to “In data science, what does the acronym "ETL" stand for”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To compare two versions of a webpage or product to determine which one performs better is the correct answer to “What is the primary goal of A/B testing in data analysis”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To prevent overfitting by adding a penalty term to the loss function is the correct answer to “In the context of machine learning, what is the purpose of regularization techniques such as L1 and L2 regularization”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

One-hot encoding is the correct answer to “What is the term for the process of converting categorical data into numerical form for machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

t-test is the correct answer to “Which statistical test is commonly used to determine if there is a significant difference between the means of two groups”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Filling in missing values in a dataset is the correct answer to “What does the term "imputation" refer to in data preprocessing”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Feature selection is the correct answer to “In machine learning, what is the term for the process of selecting the most important features for a model”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To display the counts of true positive, true negative, false positive, and false negative predictions is the correct answer to “What is the primary purpose of the "Confusion Matrix" in the evaluation of classification models”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To reduce the number of features while preserving important information is the correct answer to “In data science, what is the primary goal of dimensionality reduction techniques like Principal Component Analysis (PCA)”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Extract, Transform, Load is the correct answer to “What does the acronym "ETL" stand for in the context of data warehousing”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Part-of-speech tagging is the correct answer to “In the context of natural language processing (NLP), what is the term for assigning parts of speech (e.g., noun, verb) to words in a text”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Bootstrap resampling is the correct answer to “What statistical technique is used to estimate a population parameter by repeatedly sampling from the population”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To classify data points based on the majority class among their K nearest neighbors is the correct answer to “What is the main purpose of the K-nearest neighbors (K-NN) algorithm in machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Data imputation is the correct answer to “In data science, what is the term for the process of finding and handling missing data in a dataset”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Combining multiple models to improve predictive performance is the correct answer to “What does the term "ensemble learning" refer to in machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

The probability of making a Type I error is the correct answer to “In statistical hypothesis testing, what is the significance level typically denoted by the symbol ? (alpha)”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To create new features from existing data is the correct answer to “What is the primary goal of feature engineering in machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Term Frequency-Inverse Document Frequency is the correct answer to “In the context of natural language processing (NLP), what does "TF-IDF" stand for”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Systematic errors in model predictions is the correct answer to “What does the term "bias" refer to in machine learning and statistics”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Data compression is the correct answer to “In data science, what is the term for the process of reducing the volume of data while retaining meaningful information”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Analysis of Variance (ANOVA) is the correct answer to “Which statistical test is used to determine if there is a significant difference between the means of more than two groups”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To measure the strength and direction of a linear relationship between two variables is the correct answer to “What is the primary purpose of the "Pearson correlation coefficient" in statistics”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

Ensemble model is the correct answer to “In machine learning, what is the term for a type of model that can make predictions for both classification and regression tasks”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.

To reduce overfitting and improve model accuracy is the correct answer to “What is the main purpose of the Random Forest algorithm in machine learning”. This question is standard in competitive examination syllabi for Data Science Miscellaneous.