Data Science Essentials: Concepts, Differences, Bias, and Key Terms Explained for Freshers

Data Science Introduction: Data science involves interdisciplinary processes, algorithms, and techniques to extract insights from raw data through statistical and mathematical analysis. It encompasses data gathering, cleaning, processing, algorithm application, and visualized communication of results for business use.

Data Analytics vs. Data Science: Data science employs diverse tools for predictive modeling and innovation, while data analytics focuses on extracting current insights from historical data. Data science handles complex problems with various tools, while data analytics addresses specific issues using fewer statistical tools.

High and Low P-values: Low p-values (≤ 0.05) reject the null hypothesis, suggesting data is unlikely by chance. High p-values (≥ 0.05) favor the null hypothesis, implying data aligns with true null, while p = 0.05 indicates uncertainty.

Imbalanced Data: Imbalanced data occurs when categories lack equal distribution, leading to model inaccuracies due to skewed representation.

Expected Value vs. Mean Value: Expected value relates to random variables, while mean value pertains to probability distribution, with subtle contextual differences.

Survivorship Bias: Survivorship bias focuses on successful instances and neglects failures, leading to skewed conclusions.

Key Terms: KPI, Lift, Model Fitting, Robustness, DOE: KPI measures business objective achievement. Lift gauges model performance compared to randomness. Model fitting evaluates model match with data. Robustness assesses system resilience. DOE designs experiments for variable analysis.

Confounding Variables: Confounding variables influence both independent and dependent variables, causing false associations between unrelated factors.

Selection Bias: Selection bias arises when non-random participant selection occurs, resulting in skewed samples. Four types include sampling, time interval, data, and attrition bias.

Resampling Purpose: Resampling improves model accuracy by training on various data patterns, handling variations, and validating models with random subsets, ensuring better results and uncertainty quantification.