A Practical Introduction to Data Analysis and Big Data Training Course
Course
In City Of London
Description
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Type
Course
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Location
City of london
Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
Audience
Developers / programmers
IT consultants
Format of the course
Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress.
Facilities
Location
Start date
Start date
Reviews
Subjects
- Data analysis
Course programme
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
A Practical Introduction to Data Analysis and Big Data Training Course