D496 Introduction to Data Science
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Free D496 Introduction to Data Science Questions
1. Which of the following best defines descriptive analytics and the primary question it seeks to answer?
- Descriptive analytics involves predicting future trends and answers the question, 'What will happen?'
- Descriptive analytics summarizes past data and answers the question, 'What happened?'
- Descriptive analytics focuses on identifying relationships in data and answers the question, 'Why did it happen?'
- Descriptive analytics is concerned with data security and answers the question, 'How is data protected?'
Explanation
Descriptive analytics is the foundational level of data analytics that focuses on summarizing and interpreting historical data to understand what has already occurred. It uses methods such as data aggregation, reporting, and visualization to present insights in an understandable format. The goal is to identify patterns or trends in past performance, providing context for future analysis. By answering the question “What happened?”, descriptive analytics helps organizations monitor outcomes and establish a baseline for predictive and prescriptive analytics.
2. Data transformations are used to
- Adjust data to meet assumptions of statistical tests.
- Adjust nonsignificant data to make them significant.
- Adjust for the effects of unequal sample sizes.
- All of the answers are correct.
Explanation
Data transformations are mathematical modifications applied to data to make it suitable for analysis. The main purpose is to meet the assumptions of statistical tests, such as normality or homogeneity of variance, ensuring valid and reliable results. Transformations can also help stabilize variance, normalize distributions, and improve interpretability of relationships between variables. However, transformations do not change the significance of data or adjust for unequal sample sizes. Their primary role is to make data conform to the requirements of specific analytical models.
3. When is using a diagnostic approach to data analytics appropriate?
- When you need to understand why an event occurred.
- When you need to make your best guess about the future.
- When you need to figure out how to best approach a similar situation in the future.
- When you need to know exactly what happened.
Explanation
A diagnostic approach in data analytics is used when the goal is to determine why something happened. It goes beyond descriptive analytics (which explains what happened) to identify the root causes behind observed outcomes or trends. Techniques such as data drilling, correlation analysis, and hypothesis testing are often used to uncover influencing factors. This approach is especially useful for organizations seeking to improve performance by understanding underlying causes of success or failure.
4. In a typical data science workflow, what are the steps involved?
- Model training, model exploration, cleaning the outcomes, interpreting the results.
- Cleaning data, exploring data, model training and evaluation, obtaining results, deploying the model
- Obtaining data, data cleaning, model training, model exploration, model deployment
- Obtaining data, scrubbing data, exploring the dataset, train and evaluate a model, and interpreting the results
Explanation
A standard data science workflow follows a logical sequence of steps that ensure data is properly prepared, analyzed, and used to generate actionable insights. It typically begins with obtaining data from relevant sources, followed by scrubbing (cleaning) it to handle missing, inconsistent, or erroneous values. The next phase involves exploring the dataset through descriptive statistics and visualization to understand patterns or relationships. Then, models are trained and evaluated to identify the best-performing algorithm, and finally, the results are interpreted to derive insights or deploy the model for real-world applications. This workflow ensures data integrity, analytical rigor, and actionable outcomes.
5. Which stage of the decision science life cycle includes performing exploratory data analysis?
- Model data
- Analyze data
- Capture data
- Deploy data model
Explanation
Exploratory Data Analysis (EDA) is performed during the Analyze Data stage of the decision science life cycle. This stage involves examining datasets to summarize their main characteristics, often using statistical and visual techniques. EDA helps identify patterns, trends, correlations, and anomalies in the data, guiding model selection and feature engineering in later stages. It is a critical step that transforms raw data into actionable insights for decision-making.
6. What is the primary function of a data warehouse?
- Consolidating and storing structured data
- Storing raw data
- Performing real-time data analysis
- Conducting data exploration
Explanation
A data warehouse is designed to consolidate, organize, and store structured data from various sources into a central repository. It serves as a foundation for business intelligence (BI) and reporting by allowing data to be queried and analyzed efficiently. Data warehouses typically use a predefined schema and support historical data storage, enabling organizations to perform trend analysis, performance tracking, and decision-making. Unlike data lakes, which can store unstructured data, data warehouses focus on structured and processed data ready for analysis.
7. Which of the following is NOT typically considered a data quality issue during the Data Understanding phase?
- Inconsistent data formats
- Duplicate records
- Data redundancy
- Data visualization techniques
Explanation
The Data Understanding phase in data science involves examining the collected data to identify issues such as missing values, inconsistencies, duplicates, and redundancy, which can all affect data quality. Problems like inconsistent formats or duplicate entries must be detected and resolved before analysis begins. However, data visualization techniques are not a data quality issue—they are tools used later in the process to explore and present data.
8. Data profiling is:
- The analysis of data to capture statistics (metadata)
- The decomposition of data values to meet domain restrictions
- The identifying, linking or merging of related entries within or across sets of data
- Is the same than data ingestion
Explanation
Data profiling is the process of analyzing data to understand its structure, quality, and content by capturing key statistics and metadata such as data types, patterns, frequency distributions, and completeness. This process helps organizations assess data quality and identify inconsistencies, anomalies, or potential data issues before it is used for analysis or integration. Data profiling provides essential insights that guide data cleaning, validation, and governance efforts, ensuring that data is accurate, reliable, and fit for its intended use.
9. What is the primary objective of the Data Understanding phase in a data science project?
- To implement machine learning algorithms for predictive modeling
- To gather and analyze data to comprehend its characteristics and assess its quality
- To visualize data trends and patterns for reporting purposes
- To deploy data solutions into production environments
Explanation
The Data Understanding phase aims to gather, explore, and assess the data to gain insights into its structure, content, and quality before further analysis. During this phase, data scientists identify key variables, detect anomalies, handle missing values, and evaluate data suitability for the project’s objectives. It ensures that subsequent phases, such as modeling and analysis, are based on accurate, relevant, and well-understood data. This step forms a crucial foundation for building reliable data-driven models.
10. 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.
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