Which term refers to the process of transforming data to have a mean of 0 and a standard deviation of 1?
- Outlier Detection
- Data Imputation
- Standardization
- Feature Engineering
Standardization is the process of transforming data to have a mean of 0 and a standard deviation of 1. This helps in making data more interpretable and suitable for various machine learning algorithms, as it removes the scale effect.
Which of the following best describes the role of "Neural Architecture Search" in the future of Data Science?
- Automating data cleaning and preprocessing
- Designing neural network architectures automatically
- Conducting statistical analysis on large datasets
- Implementing data security measures
"Neural Architecture Search" is a technique that involves designing neural network architectures automatically. It is a crucial tool in the future of Data Science as it can optimize the architecture of neural networks for various tasks, improving model performance and efficiency. It automates a critical aspect of deep learning model development.
When working with time-series data in Tableau, a common visualization to show data trends over time is the _______ chart.
- Bubble
- Gantt
- Line
- Scatter
In Tableau, the "Line" chart is commonly used to visualize time-series data trends. It's an effective way to display how a specific variable changes over time, making it a valuable tool for understanding temporal patterns in data.
For datasets with categorical variables, the _______ method can be used to handle missing values by assigning a new category for missingness.
- Mean Imputation
- Mode Imputation
- Median Imputation
- Most Frequent Imputation
When dealing with missing values in categorical data, the most frequent imputation (Option D) method is used, which replaces missing values with the category that occurs most often in the column. This approach is suitable for handling categorical variables.
Which type of recommender system suggests items based on a user's past behavior and not on the context?
- Content-Based Recommender System
- Collaborative Filtering
- Hybrid Recommender System
- Context-Based Recommender System
Collaborative Filtering recommends items based on user behavior and preferences. It identifies patterns and similarities among users, making suggestions based on what similar users have liked in the past. Context-Based Recommender Systems consider contextual information, but this question is about past behavior-based recommendations.
Which emerging technology in Data Science uses a combination of AI, sensors, and data analytics to predict and prevent equipment failures?
- Blockchain
- Quantum Computing
- Internet of Things (IoT)
- Virtual Reality (VR)
The Internet of Things (IoT) involves the use of AI, sensors, and data analytics to monitor and predict equipment failures. By collecting and analyzing data from various devices, IoT enables proactive maintenance and prevents costly breakdowns.
As a data scientist, you're handed a project to predict future sales for a retail company. You've gathered the data, cleaned it, and built a predictive model. Before deploying this model, what step should you prioritize to ensure it will function as expected in a real-world setting?
- Fine-tuning the model
- Data preprocessing
- Model evaluation
- Monitoring the model's performance
Monitoring the model's performance is crucial to ensure that it functions as expected in a real-world setting. This involves continuous evaluation and making adjustments as needed to adapt to changing data and ensure the model remains accurate and reliable over time.
_______ is a technique in ensemble methods where models are trained on different subsets of the data.
- Cross-validation
- Feature engineering
- Data augmentation
- Bagging
Bagging is a technique used in ensemble methods, such as Random Forest, where multiple models are trained on different subsets of the data. The results are then combined to improve the overall model's performance and reduce overfitting.
Which curve plots the true positive rate against the false positive rate for different threshold values of a classification problem?
- ROC Curve
- Precision-Recall Curve
- Learning Curve
- Sensitivity-Specificity Curve
The ROC (Receiver Operating Characteristic) Curve plots the True Positive Rate (Sensitivity) against the False Positive Rate for different threshold values of a classification model. It is used to evaluate the model's performance in distinguishing between classes at various thresholds.
A healthcare dataset contains a column for 'Age' and another for 'Blood Pressure'. If you want to ensure both features contribute equally to the distance metric in a k-NN algorithm, what should you do?
- Standardize both 'Age' and 'Blood Pressure'
- Normalize both 'Age' and 'Blood Pressure'
- Use Euclidean distance as the metric
- Give more weight to 'Blood Pressure'
To ensure that both 'Age' and 'Blood Pressure' contribute equally to the distance metric in a k-NN algorithm, you should standardize both features. Standardization scales the features to have a mean of 0 and a standard deviation of 1, preventing one from dominating the distance calculation. Normalization may not achieve this balance, and changing the distance metric or giving more weight to one feature can bias the results.