Data Analytics - Course 2 Vocab

What is problem-solving in data analysis?

The process of identifying issues and applying data to find solutions.

What is a prediction problem type in data analysis?

It involves forecasting future outcomes using historical and real-time data.

What does categorizing things mean in data analysis?

Grouping data based on shared features to enable easier interpretation.

What is outlier detection?

Spotting unusual or unexpected data points in a dataset.

What is the goal of identifying themes in data?

To synthesize data into higher-level concepts for strategic understanding.

What does discovering connections mean in analysis?

Finding relationships or shared challenges across different entities.

What is pattern recognition in data analysis?

Identifying repeating trends in data over time.

Why is it important to understand problem types in data analysis?

Each type guides the choice of tools, methods, and presentation strategies.

What is a leading question?

A question that suggests a particular answer, which can bias results.

What does the SMART framework stand for?

Specific, Measurable, Action-oriented, Relevant, Time-bound.

What is a measurable question?

A question that yields quantifiable, countable data.

What is fairness in question design?

Creating clear, unbiased questions without assumptions.

What is data-driven decision-making?

Making decisions based on direct insights from data analysis.

What is data-inspired decision-making?

Using insights from multiple data sources to guide direction.

What's the difference between data and information?

Data is raw facts; information is data processed with context.

What is knowledge in data analytics?

Applying interpreted data to make informed decisions.

What are limitations of data analytics?

Incomplete access to data or inconsistent measurement methods.

What is quantitative data?

Numerical, objective data used to measure how many, how much, etc.

What is qualitative data?

Descriptive, non-numerical data that explains reasons and opinions.

Why use both qualitative and quantitative data?

Together, they show what is happening and explain why.

What is a report in data presentation?

A static summary of data usually shared periodically.

What is a dashboard in data presentation?

A dynamic, real-time visual display of key metrics.

What is a pivot table?

A spreadsheet tool used to summarize and visualize large datasets.

What is a metric?

A single, quantifiable data point used to evaluate performance.

What is ROI and its formula?

Return on Investment = (Net Profit ÷ Cost of Investment) × 100.

What is customer retention rate?

A metric tracking the percentage of repeat customers.

What are the three types of dashboards?

Strategic, Operational, Analytical.

Who uses strategic dashboards?

Executives for long-term planning and performance tracking.

What do operational dashboards monitor?

Short-term performance and daily operations.

What are analytical dashboards used for?

Deep data exploration and modeling by analysts.

What is mathematical thinking?

Breaking problems into logical steps to find patterns and relationships.

What is small data?

Data with limited scope over a short period, used for everyday decisions.

What is big data?

Large, long-term datasets used for complex problems.

What is a bed occupancy rate?

A metric measuring inpatient days vs. available hospital beds.

What is a spreadsheet formula?

A user-created expression for calculation using operators.

What is a spreadsheet function?

A preset formula that performs specific calculations (e.g., SUM).

What is the AVERAGE function used for?

To find the mean of selected values.

What does the MIN function do?

Finds the lowest number in a selected range.

What does the MAX function do?

Identifies the highest number in a dataset.

What is structured thinking?

A step-by-step method for defining and solving problems clearly.

What is a problem domain?

The specific subject or area a data analysis project investigates.

What is a Scope of Work (SOW)?

A document outlining tasks, timelines, deliverables, and expectations.

What are deliverables in a data project?

The tangible results expected from analysis work.

What is data context?

The conditions under which data is collected and interpreted.

What are the four types of data insights?

Descriptive, Diagnostic, Predictive, Prescriptive.

What is data bias?

The influence of personal or cultural factors on data interpretation.

Why is objectivity important in data analysis?

It ensures accurate, fair, and unbiased conclusions.

What does asking "why" help identify in data analysis?

Possible agendas or biases behind data collection.