D496 Introduction to Data Science
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Free D496 Introduction to Data Science Questions
1. What is the primary purpose of a data warehouse?
- To facilitate real-time transaction processing.
- To store data for archival purposes.
- To centralize and consolidate large amounts of data for analysis and reporting.
- To manage web-based applications.
- To encrypt sensitive data for security.
Explanation
A data warehouse is designed to serve as a central repository where data from multiple sources is collected, cleaned, transformed, and stored for analysis and reporting purposes. It supports business intelligence (BI) activities by providing a unified view of historical and current data, enabling organizations to make informed decisions. Unlike operational databases optimized for transaction processing, data warehouses are optimized for query performance and analytical workloads, allowing complex queries and trend analysis across large datasets.
2. What is the primary purpose of the Extraction-Transformation-Load (ETL) process in a data warehouse?
- Data security
- Storing raw data
- Extracting useful data from operational sources and loading it into the data warehouse
- Running analytical queries
Explanation
The ETL process—short for Extract, Transform, Load—is essential in building and maintaining a data warehouse. Its primary purpose is to extract data from multiple operational sources, transform it into a consistent and usable format through data cleaning, validation, and integration, and finally load it into the data warehouse for analysis and reporting. This process ensures that the data stored in the warehouse is accurate, standardized, and ready for business intelligence activities.
3. The ETL process of data analytics stands for
- Engage, transmit, leverage.
- Extract, transform, load.
- Engage, transform, leverage.
- Extract, transmit, load.
Explanation
ETL stands for Extract, Transform, Load, a fundamental process in data integration and analytics. It involves three key steps: Extracting data from multiple sources (such as databases, APIs, or flat files), Transforming it into a consistent and usable format through cleaning and structuring, and finally Loading it into a data warehouse or other storage system for analysis. This process ensures that data is accurate, consistent, and ready for business intelligence and reporting purposes.
4. What is a significant limitation of using pie charts for displaying data?
- They can effectively show trends over time.
- They may not accurately represent small differences between categories.
- They are suitable for displaying continuous data.
- They require a large amount of data to be effective.
Explanation
A key limitation of pie charts is that they struggle to accurately display small differences between categories. When slices are close in size, it becomes difficult to distinguish which category is larger or smaller, leading to potential misinterpretation. Pie charts are best for showing simple proportions of a whole but become ineffective when there are many categories or when the data values are very similar. In such cases, bar charts or other visualizations provide clearer insights.
5. What is the purpose of database normalization?
- To increase data redundancy
- To introduce undesirable characteristics
- To eliminate Insertion, Update, and Deletion Anomalies
- To store data in a non-logical manner
Explanation
Database normalization is the process of organizing data within a database to reduce redundancy and improve data integrity. Its primary goal is to eliminate anomalies that may occur during data operations such as insertion, updating, or deletion. By structuring data into related tables and establishing proper relationships, normalization ensures consistency, efficiency, and logical data storage. This process enhances the reliability of the database and simplifies data maintenance.
6. What can a graph be used to represent?
- All the answers are correct.
- Professional network.
- A geographical map.
- Product relationships for recommendations.
Explanation
Graphs are powerful data structures used to model and represent relationships between entities. In a graph, nodes represent entities, and edges represent the connections or relationships between them. This flexibility allows graphs to represent various real-world networks such as social or professional connections (like LinkedIn), geographical maps (where locations are nodes and routes are edges), and product recommendation systems (linking users with products they might like). Because of their versatility, graphs are widely used in fields such as social media analysis, logistics, and recommendation engines.
7. Enterprise authority that ensures control and accountability for enterprise data through the establishment of decision rights and data policies and standards that are implemented and monitored through a formal structure of assigned roles, responsibilities, and accountabilities is called ________.
- Data stewardship
- Data sharing
- Data integrity
- Data governance
Explanation
Data governance refers to the overall management framework that ensures data is properly handled, managed, and protected within an organization. It defines decision rights, accountability, and processes for data usage and management. This involves establishing data policies, standards, and procedures to ensure the accuracy, availability, and integrity of enterprise data. Unlike data stewardship, which focuses on executing specific data-related tasks, data governance provides the overarching structure for control, compliance, and responsibility across the organization.
8. What is the primary focus of DATA SCIENCE?
- extracting insights from data
- managing large databases
- coding websites
Explanation
Data Science is an interdisciplinary field focused on extracting meaningful insights and knowledge from data using techniques from statistics, mathematics, programming, and machine learning. It involves collecting, cleaning, analyzing, and interpreting data to support data-driven decision-making and predictive modeling. While managing databases and coding are part of the process, the ultimate goal of data science is to transform data into actionable insights that help solve real-world problems or inform strategic decisions.
9. Finish the following sentence. A t-test:
- Defines the hypothesis for an experiment.
- Describes how much variability there is in a data set.
- Determines if two means are significantly different from each other.
- Determines the shape of the normal distribution for a data set.
Explanation
A t-test is an inferential statistical test used to determine whether there is a statistically significant difference between the means of two groups. It helps assess whether observed differences are due to chance or reflect a real effect in the population. The t-test assumes that data follows a normal distribution and is commonly used in hypothesis testing for experiments comparing two conditions, such as treatment vs. control groups.
10. Bar charts are best used for what?
- Comparison
- Distribution
- Composition
- Relationships
Explanation
Bar charts are most effective for making comparisons between different categories of data. Each bar represents a category, and its length or height corresponds to the value being measured, allowing for easy visual comparison across categories. This makes bar charts ideal for displaying discrete data such as sales figures by region, product performance, or survey responses. They help identify differences, trends, or rankings quickly, making them one of the most widely used visualization tools for categorical data.
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