Data Science

Data Science is an interdisciplinary field, deeply rooted in Computer Science and statistics, that uses scientific methods, processes, algorithms, and systems to extract knowledge and actionable insights from structured and unstructured data. It encompasses the entire data lifecycle, from data collection, cleaning, and exploration to model building, machine learning, and the communication of results to inform decision-making. By leveraging computational power and statistical theory, data scientists uncover hidden patterns, make predictions, and solve complex analytical problems across a vast range of industries.

1. Foundations of Data Science
   1. What is Data Science
      1. Definition and Scope
      2. Data Science vs Statistics
      3. Data Science vs Business Intelligence
      4. Data Science vs Data Analytics
   2. Core Disciplines
      1. Statistics and Probability
         1. Role in Data Science
         2. Descriptive Statistics
         3. Inferential Statistics
         4. Probability Theory
         5. Statistical Modeling
      2. Computer Science
         1. Programming Fundamentals
         2. Algorithms and Data Structures
         3. Software Engineering Principles
         4. Database Systems
         5. Distributed Computing
      3. Domain Knowledge
         1. Importance of Subject Matter Expertise
         2. Industry-Specific Applications
         3. Integrating Domain Knowledge into Analysis
         4. Business Acumen
   3. The Data Science Process
      1. CRISP-DM Methodology
      2. KDD Process
      3. Team Data Science Process
   4. Data Science Roles and Career Paths
      1. Data Scientist
      2. Data Analyst
      3. Machine Learning Engineer
      4. Data Engineer
      5. Research Scientist
   5. The Data Science Lifecycle
      1. Business Understanding and Problem Formulation
         1. Defining Business Objectives
         2. Success Metrics
         3. Translating Business Problems into Data Science Problems
         4. Stakeholder Engagement
         5. Project Scoping
      2. Data Acquisition
         1. Identifying Data Sources
         2. Data Collection Methods
         3. Data Access and Permissions
         4. Data Governance
         5. Legal and Ethical Considerations
      3. Data Preparation and Wrangling
         1. Data Assessment
         2. Data Cleaning
         3. Data Transformation
         4. Data Integration
         5. Data Quality Assurance
      4. Exploratory Data Analysis
         1. Initial Data Exploration
         2. Statistical Summaries
         3. Data Visualization
         4. Identifying Patterns and Anomalies
         5. Hypothesis Generation
      5. Modeling
         1. Problem Type Identification
         2. Algorithm Selection
         3. Feature Engineering
         4. Model Training
         5. Hyperparameter Tuning
         6. Model Validation
      6. Evaluation
         1. Performance Metrics
         2. Model Comparison
         3. Statistical Significance Testing
         4. Business Impact Assessment
      7. Deployment and Communication
         1. Model Deployment Strategies
         2. Production Environment Setup
         3. Communicating Results to Stakeholders
         4. Documentation
         5. Monitoring and Maintenance
         6. Model Versioning