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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
- R Language
- Why R for Data Analysis?
- Data manipulation, calculation and graphical display
- 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
- Natural Language Processing
- Processing text
- Understaing meaning of the text
- Automatic text generation
- Sentiment analysis / topic analysis
- Computer Vision
- Acquiring, processing, analyzing, and understanding images
- Reconstructing, interpreting and understanding 3D scenes
- Using image data to make decisions
Big Data Infrastructure
- Data Storage
- Relational databases (SQL)
- MySQL
- Postgres
- Oracle
- Non-relational databases (NoSQL)
- Cassandra
- MongoDB
- Neo4js
- Understanding the nuances
- Hierarchical databases
- Object-oriented databases
- Document-oriented databases
- Graph-oriented databases
- Other
- Relational databases (SQL)
- Distributed Processing
- Hadoop
- HDFS as a distributed filesystem
- MapReduce for distributed processing
- Spark
- All-in-one in-memory cluster computing framework for large-scale data processing
- Structured streaming
- Spark SQL
- Machine Learning libraries: MLlib
- Graph processing with GraphX
- Hadoop
- Scalability
- Public cloud
- AWS, Google, Aliyun, etc.
- Private cloud
- OpenStack, Cloud Foundry, etc.
- Auto-scalability
- Public cloud
Choosing the Right Solution for the Problem
The Future of Big Data
Summary and Conclusion
Requirements
- A general understanding of math.
- A general understanding of programming.
- A general understanding of databases.
Audience
- Developers / programmers
- IT consultants
35 Hours
Getuigenissen (3)
De trainer heeft de materialen en inhoud aangepast aan wat volgens hem het beste voor ons zou zijn en dat is gelukt. De kwaliteit van de opleiding was uitstekend.
Jorge Sanchez Hernandez - CSMART - Carnival
Cursus - QGIS for Geographic Information System
Machine Translated
Dużo cierpliwości
Mateusz - WestWind Energy Polska Sp. z o.o.
Cursus - ArcGIS for Spatial Analysis
I genuinely enjoyed the lots of labs and practices.