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 Duration 21 hours

Course Outline

Foundations of Data Analytics and Large-Scale Data Environments

  • Defining the Characteristics of Large-Scale Data Sets
    • Velocity, Volume, Variety, and Veracity (the 4 V’s)
  • Constraints of Conventional Data Processing Systems
  • Distributed Computing Architectures
  • Statistical Methodologies
  • Categories of Machine Learning Models
  • Data Visualization Techniques

Professional Roles and Operational Responsibilities

  • System Administrators
  • Software Developers
  • Data Analysts

Programming Languages for Data Analysis

  • Python
    • Rationale for Selecting Python in Analytical Contexts
    • Data Manipulation, Processing, Cleaning, and Aggregation

Methodological Frameworks for Data Analysis

  • Statistical Analysis
    • Time Series Analysis
    • Predictive Modeling via Correlation and Regression
    • Inferential Statistics (Estimation Techniques)
    • Descriptive Statistics Applied to Large Data Sets (e.g., Mean Calculation)
  • Machine Learning
    • Supervised versus Unsupervised Learning Paradigms
    • Classification and Clustering Algorithms
    • Cost Estimation of Specific Methods
    • Data Filtering Strategies

Large-Scale Data Infrastructure

  • Data Storage Solutions
    • Relational Database Management Systems (SQL)
      • MySQL
      • PostgreSQL
      • Oracle
    • Advanced Data Model Nuances
      • Hierarchical Databases
      • Object-Oriented Databases
      • Document-Oriented Databases
      • Graph-Oriented Databases
      • Other Specialized Models

Future Trajectories in Large-Scale Data Management

Concluding Summary and Recommended Pathways

Requirements

  • Familiarity with fundamental mathematical principles
  • Basic proficiency in programming concepts
  • General understanding of database systems

Target Audience

  • Software Developers and Programmers
  • IT Consultants

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