Pyramid E & C Interview Questions and Answers

Data collection is the process of gathering and organizing information for analysis and decision-making.

• Data collection involves systematically collecting data from various sources.

• It can be done through surveys, interviews, observations, or by extracting data from databases.

• The collected data is then organized and stored for analysis and interpretation.

• Examples of data collection include conducting customer satisfaction surveys, tracking website analytics, or collecting sens...read more

Feature engineering is the process of selecting, transforming, and creating new features from raw data to improve model performance.

• Feature selection involves choosing the most relevant features for the model

• Feature transformation includes scaling, normalization, and encoding categorical variables

• Feature creation involves generating new features based on existing ones, such as polynomial features or interaction terms

Data preprocessing is the process of cleaning, transforming, and organizing raw data before analysis.

• Removing irrelevant or duplicate data

• Handling missing values

• Normalizing or standardizing data

• Encoding categorical variables

• Feature scaling

• Data transformation (e.g. log transformation)

• Data reduction (e.g. PCA)

• Handling outliers

A cluster is a group of data points or objects that are similar to each other within the group and dissimilar to data points in other groups.

• Clusters are formed based on the similarity of data points within the group.

• Clustering is an unsupervised learning technique used in data science.

• Examples of clustering algorithms include K-means, hierarchical clustering, and DBSCAN.

Data mining is the process of discovering patterns and extracting useful information from large datasets.

• Data mining involves analyzing large datasets to uncover hidden patterns and relationships.

• It uses various techniques such as clustering, classification, and regression to extract valuable insights.

• Examples of data mining include market basket analysis, customer segmentation, and fraud detection.

• Data mining helps businesses make informed decisions, improve efficiency, and ...read more