Understanding Data Science and Big Data
Both Data Science and Big Data have risen to prominence recently. Whilst they are not immutably linked, it is certainly true that many data scientists work extensively with big data. Both topics are so new that they are poorly understood; nevertheless there is considerable interest in them and there is a significant shortfall in the number of trained data scientists in the job market. This course introduces both the job role of the data scientist and big data itself.
This course is vendor neutral; it is not about how to use any one vendor's products, it is about the fundamental underpinnings of these two important subject areas. This course is aimed at people who are trying to understand data science and big data and want to know about the range of skills, technologies and techniques that are appropriate to these new areas.
Suitability - Who should attend?
This course is intended for people aspiring to be data scientists and/or to work with Big Data. Others who may take this course include Business Intelligence (BI) professionals who want to work with big data and/or are looking to move into Data Science. People coming into the course are expected to have at least 3 years experience working in the IT field-typically in the areas of databases, BI, analytics or related areas.
- Knowledge of at least one relational database engine (Oracle, SQL Server, DB2 etc.)
- An understanding of relational database modelling and design
- Basic understanding of data and how business systems use both data and information; this would be gained by at least a year's experience in IT or business systems development.
Training Course Content
At the end of this course you will be able to:
- Understand the role of the data scientist
- Understand big data and what makes it different
- Utilise CAP theorem to choose a database engine for a given situation
- Select a specific NoSQL database engine
- Use the analytical language R
- Identify continuous and discontinuous data
- Understand normal distributions, mean, mode, median and standard deviations
- Take good samples from populations of data
- Understand and perform Chi squared and t tests
- Understand the difference between schema and schema-less storage
- Decide which storage mechanism to apply to which data
- Distinguish between different data mining algorithms
- Perform data mining appropriately using the CRISP methodology
- Identify false positives
- Understand and apply Monte Carlo simulations
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