You're working with a dataset containing sales data from various regions. You want to identify sales patterns, seasonal trends, and anomalies. Which EDA techniques and visualization tools would be best suited for this?
- Scatter plots and t-SNE
- Box plots and bar charts
- Time series plots and heatmaps
- Histograms and parallel coordinates
For exploring sales patterns and seasonal trends, time series plots and heatmaps are excellent choices. Time series plots can reveal trends over time, and heatmaps can show correlations between different regions and sales data, helping identify anomalies and patterns.
In EDA, which method can help in understanding how a single variable is distributed across various categories or groups?
- Histogram
- Box Plot
- Scatter Plot
- Bar Plot
A bar plot is used to visualize the distribution of a single variable across different categories or groups. It displays the data in rectangular bars, making it easy to compare and understand how the variable is distributed among the categories. Commonly used in Exploratory Data Analysis (EDA).
In an RNN, which component is responsible for allowing information to be passed from one step in the sequence to the next?
- Hidden State
- Input Layer
- Output Layer
- Activation Function
The hidden state in an RNN is responsible for passing information from one step in the sequence to the next. It carries information from previous steps and combines it with the current input to capture sequential dependencies, making it a crucial component in recurrent neural networks.
XML and JSON data formats, which can have a hierarchical structure, are examples of which type of data?
- Unstructured Data
- Semi-Structured Data
- Structured Data
- NoSQL Data
XML and JSON are examples of semi-structured data. Semi-structured data is characterized by a hierarchical structure and flexible schemas, making it a middle ground between structured and unstructured data. It is commonly used in various data exchange and storage scenarios.
The _______ step in the Data Science Life Cycle is crucial for understanding how the final model will be integrated and used in the real world.
- Data Exploration
- Data Preprocessing
- Model Deployment
- Data Visualization
The "Model Deployment" step in the Data Science Life Cycle is essential for taking the data science model from development to production. It involves integrating the model into real-world applications, making it a crucial phase.
Text data from social media platforms, such as tweets or Facebook posts, is an example of which type of data?
- Structured data
- Semi-structured data
- Unstructured data
- Binary data
Text data from social media platforms is typically unstructured. It doesn't have a fixed format or schema. It may include text, images, videos, and other content without a well-defined structure, making it unstructured data.
Which component of the Hadoop ecosystem is primarily used for distributed data storage?
- HDFS (Hadoop Distributed File System)
- Apache Spark
- MapReduce
- Hive
HDFS (Hadoop Distributed File System) is the primary component in the Hadoop ecosystem for distributed data storage. It is designed to store large files across multiple machines and provides data durability and fault tolerance.
In a convolutional neural network (CNN), which type of layer is responsible for reducing the spatial dimensions of the input?
- Convolutional Layer
- Pooling Layer
- Fully Connected Layer
- Batch Normalization Layer
The Pooling Layer in a CNN is responsible for reducing the spatial dimensions of the input. This layer downsamples the feature maps, which helps in retaining important features and reducing computational complexity.
Which Python library is specifically designed for statistical data visualization and is built on top of Matplotlib?
- Seaborn
- Pandas
- Numpy
- Scikit-learn
Seaborn is a Python library built on top of Matplotlib, designed for statistical data visualization. It provides a high-level interface for creating informative and attractive statistical graphics, making it a valuable tool for data analysis and visualization.
In time series forecasting, which method captures both trend and seasonality in the data?
- Moving Average
- Exponential Smoothing
- ARIMA (AutoRegressive Integrated Moving Average)
- Exponential Moving Average
ARIMA (AutoRegressive Integrated Moving Average) captures both trend and seasonality in time series data. It combines autoregressive, differencing, and moving average components to model complex time series patterns, making it a powerful method for forecasting data with seasonal and trend components.
You're analyzing a dataset with the heights of individuals. While the mean height is 165 cm, you notice a few heights recorded as 500 cm. These values are likely:
- Data entry errors
- Outliers
- Missing data
- Measurement errors
The heights recorded as 500 cm are likely outliers in the dataset. Outliers are data points that significantly differ from the majority of the data and may indicate measurement errors or anomalies. It's important to identify and handle outliers appropriately during data analysis.
Before deploying a model into production in the Data Science Life Cycle, it's essential to have a _______ phase to test the model's real-world performance.
- Training phase
- Deployment phase
- Testing phase
- Validation phase
Before deploying a model into production, it's crucial to have a testing phase to evaluate the model's real-world performance. This phase assesses how the model performs on unseen data to ensure its reliability and effectiveness.