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Course Outline
Introduction to Data Analysis and Big Data
- What Makes Big Data "Big"?
- Velocity, Volume, Variety, Veracity (VVVV).
- Limits to Traditional Data Processing.
- Distributed Processing.
- Statistical Analysis.
- Types of Machine Learning Analysis.
- Data Visualization.
Big Data Roles and Responsibilities
- Administrators.
- Developers.
- Data Analysts.
Languages Used for Data Analysis
- Python
- Why Python for Data Analysis?
- Manipulating, processing, cleaning, and crunching data.
Approaches to Data Analysis
- Statistical Analysis
- Time Series analysis.
- Forecasting with Correlation and Regression models.
- Inferential Statistics (estimating).
- Descriptive Statistics in Big Data sets (e.g., calculating mean).
- Machine Learning
- Supervised vs unsupervised learning.
- Classification and clustering.
- Estimating cost of specific methods.
- Filtering.
Big Data Infrastructure
- Data Storage
- Relational databases (SQL)
- MySQL.
- Postgres.
- Oracle.
- Understanding the nuances
- Hierarchical databases.
- Object-oriented databases.
- Document-oriented databases.
- Graph-oriented databases.
- Other.
- Relational databases (SQL)
The Future of Big Data
Summary and Next Steps
Requirements
- A basic understanding of mathematics.
- A basic understanding of programming.
- A basic understanding of databases.
Target Audience
- Developers / programmers.
- IT consultants.
21 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
