Exploring Data Analytics Concepts

What is the primary purpose of data analytics?

• To extract meaningful insights from data.

• To create complex algorithms.

• To visualize data in charts.

• To store data for future use.

Name the four types of data analytics.

• Historical, Current, Future, Comparative

• Qualitative, Quantitative, Mixed, Experimental

• Exploratory, Analytical, Statistical, Operational

• Descriptive, Diagnostic, Predictive, Prescriptive

How is descriptive analytics different from predictive analytics?

• Descriptive analytics predicts future trends; predictive analytics analyzes historical data.

• Descriptive analytics focuses on real-time data; predictive analytics looks at past data.

• Descriptive analytics uses machine learning; predictive analytics relies on statistical methods.

• Descriptive analytics explains past data; predictive analytics forecasts future events.

List two applications of data analytics in healthcare.

• 1. Predictive analytics for patient outcomes; 2. Operational efficiency analysis.

• Inventory management systems

• Patient satisfaction surveys

• Social media marketing strategies

What is the difference between structured and unstructured data?

• Structured data is organized and easily searchable, while unstructured data is unorganized and harder to analyze.

• Structured data is always in text format.

• Structured data cannot be stored in databases.

• Unstructured data is always numerical and easy to analyze.

What are some common methods for handling missing values in a dataset?

• Removing records, imputing values, using algorithms that support missing data, predicting values.

• Duplicating records with missing values

• Ignoring missing values completely

• Using only the mean of the dataset

Explain the concept of feature engineering in data preprocessing.

• Feature engineering is irrelevant to model performance.

• Feature engineering is only about data normalization.

• Feature engineering is crucial in data preprocessing as it enhances model accuracy by creating relevant features.

• Feature engineering is the process of cleaning data without creating new features.

What does ETL stand for in data analytics?

• Extract, Transform, Load

• Extract, Transform, Link

• Extract, Transform, List

• Extract, Transfer, Load

Which data visualization tool is known for its ease of use and interactivity?

• Tableau

• Excel Charts

• Power BI

• Google Data Studio

What is the importance of storytelling with data in presentations?

• Storytelling with data is only useful for marketing purposes.

• Data storytelling is irrelevant in scientific presentations.

• Storytelling with data complicates the interpretation of results.

• Storytelling with data is important because it makes data more engaging, understandable, and impactful.