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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 Next Steps
Requirements
- A general understanding of math
- A general understanding of programming
- A general understanding of databases
Audience
- Developers / programmers
- IT consultants
35 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 8000 € + VAT*
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Testimonials (7)
How big data work, data programs, greater knowledge of how our current world works using data
Ozayr Hussain - Vodacom
Course - A Practical Introduction to Data Analysis and Big Data
The practical side of the training.
Patrick - Vodacom PTy Ltd
Course - A Practical Introduction to Data Analysis and Big Data
Interactive topics and the style used by the lecture to simplified the topics for the students
Miran Saeed - Sulaymaniyah Asayish Agency
Course - A Practical Introduction to Data Analysis and Big Data
the trainer and his ability to lecture
ibrahim hamakarim - Sulaymaniyah Asayish Agency
Course - A Practical Introduction to Data Analysis and Big Data
Practical exercises
JOEL CHIGADA - University of the Western Cape
Course - A Practical Introduction to Data Analysis and Big Data
R programming
Osden Jokonya - University of the Western Cape
Course - A Practical Introduction to Data Analysis and Big Data
Overall the Content was good.
