Big Data Analytics – Mehmet Gönen
Big data is a relative term describing a situation where the volume, velocity, and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision-making. We start by describing real world big data problems including the three key sources of big data: people, organizations, and sensors. This course will provide an overview of machine learning techniques to explore, analyze, and leverage this kind of data.
- Explain the V’s of big data (volume, velocity, and variety) and why each impacts data collection, monitoring, storage, analysis, and reporting.
- Identify what are and what are not big data problems and be able to recast big data problems as data science questions.
- Provide an explanation of the architectural components and programming models used for scalable big data analysis.
- Identify the type of machine learning problem to apply the appropriate set of techniques.
- Construct models that learn from data using widely available open source tools.