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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
- Relational Database Management Systems (SQL)
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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Equipped with examples