Schița de curs
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
Cerințe
- A general understanding of math.
- A general understanding of programming.
- A general understanding of databases.
Audience
- Developers / programmers
- IT consultants
Mărturii (5)
Profesionist și foarte practic, util în munca de zi cu zi
Jozefin Rékasi - SC Automobile Dacia SA
Curs - Advanced Data Analysis with TIBCO Spotfire
Tradus de catre o masina
A acoperit domeniile de care am spus că sunt interesat înainte de curs: relații de date, utilizarea scriptului python. Conectarea la bazele de date va fi acoperită în modulul avansat.
Cristian Tudose - SC Automobile Dacia SA
Curs - Introduction to Spotfire
Tradus de catre o masina
Dużo cierpliwości
Mateusz - WestWind Energy Polska Sp. z o.o.
Curs - ArcGIS for Spatial Analysis
Formatorul a adaptat materialele și conținutul la ceea ce credea el că ar fi cel mai bine pentru noi și a reușit. Calitatea formării a fost excelentă.
Jorge Sanchez Hernandez - CSMART - Carnival
Curs - QGIS for Geographic Information System
Tradus de catre o masina
I genuinely enjoyed the lots of labs and practices.