Recruiter's Glossary

Examples: Data Mining • Box Plot • Scatter Plot

Discriminant Analysis

Term from Analysis industry explained for recruiters

Discriminant Analysis is a method that helps businesses make smart decisions by sorting data into different groups. Think of it like a sorting hat that helps predict which category something belongs to. For example, it can help determine whether a customer is likely to buy a product or not, or classify employees into different performance groups. This method is particularly valuable in business, marketing, and human resources where professionals need to make data-driven decisions. Similar approaches include classification trees and logistic regression. It's often mentioned in resumes of people who work with data analysis, statistics, or business analytics.

Examples in Resumes

Used Discriminant Analysis to improve customer segmentation and increase marketing ROI by 25%

Applied Discriminant Analysis and DA techniques to optimize hiring decisions

Conducted Discriminant Analysis to classify market segments for targeted advertising campaigns

Typical job title: "Data Analysts"

Also try searching for:

Business Analyst Data Scientist Market Research Analyst Statistical Analyst Quantitative Analyst Research Analyst Marketing Analyst

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Example Interview Questions

Senior Level Questions

Q: How would you explain Discriminant Analysis to a non-technical stakeholder?

Expected Answer: Should be able to explain in simple terms how the method helps categorize things and make predictions, preferably using relevant business examples like customer segmentation or risk assessment.

Q: Can you describe a project where you used Discriminant Analysis to solve a business problem?

Expected Answer: Should demonstrate experience in applying the technique to real business situations, explaining the problem, approach, and measurable results achieved.

Mid Level Questions

Q: What factors do you consider when deciding to use Discriminant Analysis?

Expected Answer: Should be able to discuss when this method is appropriate versus other classification methods, considering factors like data type and business goals.

Q: How do you validate the results of your analysis?

Expected Answer: Should explain how they ensure their results are reliable and meaningful for business decision-making, including testing and validation methods.