Sample interview questions: Can you discuss your familiarity with data mining and machine learning techniques applied to sociological data?
Sample answer:
- Data Mining:
- Supervised Learning:
- Linear and Logistic Regression: Predicting numeric or categorical outcomes from a set of predictors.
Decision Trees: Constructing hierarchical decision-making models to classify or predict outcomes.
Unsupervised Learning:
- Clustering algorithms (k-means, hierarchical clustering): Grouping similar observations into clusters based on their characteristics.
Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) to reduce the number of features while preserving essential information.
Association Mining:
- Apriori algorithm: Discovering frequent itemsets and association rules in transactional data, often used in market basket analysis.
- Machine Learning:
- Supervised Learning:
- Support Vector Machines (SVM): Classifying data points into different classes based on their attributes.
- Random Forests: Ensemble learning method combining multiple decision trees, gaining robustness and accuracy.
Gradient Boosting: Building sequential decision trees, iteratively improving predictions by learning from previous errors.
Unsupervised Learning:
- k-Nearest Neighbors (k-NN): Assigning labels to new data points based on the labels of their nearest neighbors in the feature space.
Self-Organizing Maps (SOM): Projecting high-dimensional data onto a low-dimensional grid while preserving topological relationships.
Reinforcement Learning:
- Q-learning: Learning optimal policies for sequential decision-making problems through trial and… Read full answer