Data Science and Big Data

1. Interdisciplinary Aspects
   1. Computer Science
      1. Algorithms
         1. Importance of efficient algorithms in processing large datasets
         2. Big O Notation and its application in data science
         3. Algorithmic optimization for scalable systems
      2. Data Structures
         1. Importance of proper data structuring for analysis
         2. Commonly used data structures (arrays, lists, trees, etc.)
         3. Data structures in databases and data warehouses
      3. Software Development
         1. Agile methodologies in data science projects
         2. Software lifecycle management for data-driven applications
         3. Integration of data science models into software applications
   2. Mathematics and Statistics
      1. Probability Theory
         1. Random variables and probability distributions
         2. Bayesian statistics and their application in modeling
         3. Use of probability in predictive modeling
      2. Statistical Inference
         1. Hypothesis testing and confidence intervals
         2. Regression analysis and its importance in data prediction
         3. Use of inferential statistics in drawing conclusions from data
      3. Linear Algebra
         1. Application of matrices and vectors in machine learning
         2. Singular value decomposition and its use in dimensionality reduction
         3. Eigenvectors and eigenvalues in data transformations
   3. Domain Expertise
      1. Subject Matter Knowledge
         1. Importance of domain specificity in data analysis
         2. Collaborations between data scientists and domain experts
         3. Tailoring data science solutions to fit specific industry needs
      2. Identifying key domain-specific KPIs and metrics
      3. Case studies of successful interdisciplinary collaborations
   4. Ethics in Data Science
      1. Fairness
         1. Ensuring equitable treatment and avoiding biases in model outputs
         2. Techniques for detecting and mitigating bias in datasets
         3. Ethical implications of decision-making based on data models
      2. Bias
         1. Understanding different types of biases in data collection and processing
         2. Addressing bias in training data to ensure model fairness
         3. Cognitive bias in data interpretation by data scientists
      3. Transparency
         1. Importance of explainability in data science models
         2. Techniques for improving model transparency and user trust
         3. Regulatory considerations for transparency in machine learning models