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Course Outline

Overview of Data Analytics and Large-Scale Data Systems

  • Defining Characteristics of Large-Scale Data
    • Velocity, Volume, Variety, and Veracity
  • Constraints of Conventional Data Processing Methods
  • Distributed Computing Frameworks
  • Statistical Evaluation Techniques
  • Categories of Algorithmic Learning Models
  • Data Representation and Visualization

Professional Roles and Accountabilities in Large-Scale Data Management

  • System Administrators
  • Software Developers
  • Data Analysts

Programming Languages for Analytical Work

  • Python
    • Rationale for Using Python in Analytics
    • Data manipulation, processing, cleansing, and computation

Methodologies for Data Analysis

  • Statistical Evaluation
    • Temporal analysis techniques
    • Prediction models using correlation and regression
    • Inferential statistics for estimation
    • Descriptive metrics within large datasets (e.g., mean calculation)
  • Algorithmic Learning
    • Distinguishing supervised from unsupervised approaches
    • Classification and clustering techniques
    • Evaluating the resource cost of specific methods
    • Data filtering processes

Architecture for Large-Scale Data Systems

  • Data Storage Solutions
    • Relational Database Management Systems (SQL)
      • MySQL
      • PostgreSQL
      • Oracle
    • Evaluating Distinct Architectural Models
      • Hierarchical database structures
      • Object-oriented databases
      • Document-based databases
      • Graph-based databases
      • Alternative formats

The Future of Large-Scale Data Applications

Summary and Subsequent Actions

Requirements

  • Familiarity with foundational mathematical concepts
  • Proficiency in software development principles
  • Knowledge of database management systems

Target Audience

  • Software engineers and developers
  • Information technology consultants
 21 Hours

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