C730 Data Wrangling
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Free C730 Data Wrangling Questions
- easier and quicker to understand
- shows patterns, trends, relationships overtime
- Allows predication of future events
- identifies new emerging changes
- Communicates a story or a message
- All of the above
Explanation
Data visualization is vital because it transforms complex data into clear, graphical representations that make information easier to interpret. Through charts, graphs, and dashboards, it reveals patterns, trends, and relationships that might be hidden in raw data. Visualization aids in identifying emerging changes, supports forecasting, and effectively communicates insights to stakeholders. This makes it a powerful tool for storytelling, strategic decision-making, and driving business understanding across all levels of an organization.
- To visualize data for presentations
- To structure, clean, and prepare raw data for analysis
- To store data in databases
- To generate reports for stakeholders
Explanation
The primary goal of data wrangling is to structure, clean, and prepare raw data for analysis. This process involves correcting errors, removing duplicates, and transforming unorganized data into a usable format. By ensuring that data is accurate and properly formatted, analysts can extract meaningful insights more efficiently. High-quality, well-prepared data serves as the foundation for reliable analysis, visualization, and data-driven decision-making within organizations.
- Finished, usable products which result from the production process.
- The process by which scarce resources are transformed into finished goods/services.
- The scarce resources which can be used to directly or indirectly satisfy human wants.
- None of the above answers are correct.
Explanation
In economics and business, “output” refers to the finished, usable products or services that result from a production process. These outputs are what firms produce to meet consumer demand and generate revenue. They can be tangible goods like machinery and clothing or intangible services like banking and education. Output serves as a key indicator of productivity and efficiency, reflecting how effectively resources are transformed into valuable end products.
- unorganized, unprocessed data
- data that has been processed into a useful form called information
- data that a user enters into a computer
- data that is communicated to a user through the use of input devices
Explanation
Output refers to data that has been processed into a useful form called information. Once raw data is entered and processed by a computer system, it becomes meaningful and interpretable for users. This processed data, or output, can take many forms such as reports, visual displays, or printed documents. Output is essential because it provides users with actionable insights that support decision-making and communication within organizations.
- preparation of financial statements
- planning and decision making
- strategic management
- management and operational control
Explanation
Budgeting and profit planning fall under the management function of planning and decision making. These processes help managers forecast future financial performance, set objectives, and allocate resources efficiently. By planning ahead, organizations can anticipate challenges, evaluate alternatives, and make informed decisions that align with long-term goals. Budgeting also provides a framework for measuring performance and guiding operational activities toward achieving profitability and sustainability.
- Quality data is validated and accurate.
- Quality data can be trusted to produce accurate insights so decisions can be made.
- The information in quality data is consistent across platforms.
- Quality data has all of the data that is needed, and it is complete and accessible.
Explanation
The main benefit of quality data is that it can be trusted to produce accurate insights, enabling sound decision-making. High-quality data is reliable, consistent, and error-free, ensuring that analyses reflect the true state of operations. When decision-makers have confidence in their data, they can identify trends, allocate resources effectively, and implement strategies that yield measurable results, reducing the risk of costly mistakes or misinformed choices.
- By providing a single source of truth that enhances data accuracy
- By eliminating the need for data visualization tools
- By focusing solely on data entry processes
- By segregating data into different departments
Explanation
Data integration contributes to informed decision-making by providing a single source of truth that enhances data accuracy. It consolidates data from multiple sources—such as sales, finance, and customer systems—into one unified view. This eliminates inconsistencies, reduces duplication, and ensures that decision-makers have access to accurate and consistent information. With integrated data, organizations can identify trends more effectively, improve collaboration, and make data-driven decisions with greater confidence and precision.
- Increasing data entry speed
- Implementing robust data saving and backup procedures
- Focusing on data visualization techniques
- Integrating more CRM systems
Explanation
When a company faces data integrity issues caused by frequent data loss, it should prioritize implementing robust data saving and backup procedures. Backups ensure that data remains secure and recoverable in the event of system failures, human error, or cyber incidents. Regular and automated backup processes—along with redundancy and secure storage—help maintain data continuity, accuracy, and reliability, thereby preserving the integrity of critical business information.
- Providing missing data
- Correcting erroneous data
- Deleting duplicates
- All of the above
Explanation
Data cleansing involves all activities that enhance the accuracy and reliability of a dataset, including providing missing data, correcting errors, and deleting duplicates. This process ensures that the dataset is consistent, valid, and free of inaccuracies that could distort analysis or lead to poor decisions. By maintaining clean and reliable data, organizations can produce trustworthy insights and improve the quality of their data-driven processes.
- Ingest data sources into the target data store in the pipeline.
- Give business users access to the data to build reports.
- Combine data from different sources into a single dataset.
- Run scripts to perform row counts for each data source.
Explanation
A key task in the data enriching step is to combine data from different sources into a single dataset. Data enrichment enhances the value of existing data by merging it with external or complementary datasets, providing deeper insights and context. This process helps organizations develop a more complete understanding of customers, operations, or markets. Enriched data supports more accurate analysis, predictive modeling, and decision-making.
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