You are working on a project where you need to predict the next word in a sentence. Which type of neural network architecture would be most suitable for this task?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM) Network
- Generative Adversarial Network (GAN)
Predicting the next word in a sentence is a sequential data problem, making it suitable for recurrent neural networks. LSTMs are particularly effective for this task as they can capture long-term dependencies in the data, which is essential for predicting words in a sentence.
In the realm of Data Science, the library _______ in Python is widely used for data manipulation and cleaning.
- TensorFlow
- Pandas
- Matplotlib
- Scikit-learn
Pandas is a popular Python library for data manipulation and cleaning. It provides data structures and functions for working with structured data, making it a valuable tool in data science, which makes option B the correct answer.
The method where data values are shifted and rescaled to range between 0 and 1 is called _______.
- Data Normalization
- Data Imputation
- Data Resampling
- Data Transformation
The method of shifting and rescaling data values to range between 0 and 1 is known as "data normalization." This is commonly used in machine learning to ensure that all features have the same scale, preventing certain features from dominating others.
The _______ typically works closely with business stakeholders to understand their requirements and translate them into data-driven insights.
- Data Scientist
- Data Analyst
- Data Engineer
- Business Analyst
Data Scientists often work closely with business stakeholders to understand their requirements and translate them into data-driven insights. They use statistical and analytical techniques to derive insights that support decision-making.
In deep learning, the technique used to skip one or more layers by connecting non-adjacent layers is called _______.
- Dropout
- Batch Normalization
- Skip Connections
- Pooling
In deep learning, the technique used to skip one or more layers by connecting non-adjacent layers is called "Skip Connections." Skip connections allow the model to bypass one or more layers and facilitate the flow of information from one layer to another, helping in the training of deep neural networks.
Which of the following tools is typically used to manage and query relational databases in Data Science?
- Excel
- Hadoop
- SQL (Structured Query Language)
- Tableau
SQL (Structured Query Language) is a standard tool used for managing and querying relational databases. Data scientists frequently use SQL to extract, manipulate, and analyze data from these databases, making it an essential skill for working with structured data.
You're working on a real estate dataset where the price of the house is significantly influenced by its age and square footage. To capture this combined effect, what type of new feature could you create?
- Interaction feature
- Categorical feature with age groups
- Time-series feature
- Ordinal feature
To capture the combined effect of age and square footage on house price, you can create an interaction feature. This feature multiplies or combines the two variables to represent their interaction, allowing the model to consider how they jointly affect the target variable. An interaction feature is valuable in regression models.
In a traditional relational database, the data stored in a tabular format is often referred to as _______ data.
- Structured Data
- Unstructured Data
- Semi-Structured Data
- Raw Data
In a traditional relational database, the data is structured and organized in tables with a predefined schema. It's commonly referred to as "Structured Data" because it adheres to a strict structure and schema.
In which type of learning does the model discover patterns or structures without any prior labeling of data?
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
Unsupervised Learning is the type where the model discovers patterns or structures without prior data labeling. Common tasks in this category include clustering and dimensionality reduction, helping find hidden insights in data without any guidance.
For time-series data, which variation of gradient boosting might be more appropriate?
- XGBoost
- AdaBoost
- LightGBM
- Random Forest
Time-series data often has specific characteristics, such as seasonality and trends. LightGBM is well-suited for such data as it can handle categorical features efficiently and is capable of capturing complex patterns, making it a strong choice for time-series forecasting.