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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 general understanding of math
- A general understanding of programming
- A general understanding of databases
Audience
- Developers / programmers
- IT consultants
21 Hours
Testimonials (2)
workshops, practical examples
Martin Stuparek - Orange Slovensko, a.s.
Course - Monitoring with Grafana
Doing Exercise
