| Duration of Training | 2 Days |
| Prerequisites | - To have knowledge of data, data analysis, mathematics, statistics, computer science, database, database query.
- To have basic knowledge of Python.
- To have basic Linux knowledge.
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| Audience | Suitable for people who want: - Software developers, analysts and data scientists who need to apply data science and machine learning in Spark / Hadoop,
- To collect, analyze and interprete extremely big amounts of data,
- To use advanced analysis technologies,
- To use various analysis and reporting tools by collecting and analyzing data, identifying patterns, trends and relationships in data sets, who want to work on large amounts of data.
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| Training Goals | - Learning the basics including project life cycle, data collection, data evaluation, data transformation and data analysis,
- Theoretical learning of machine learning (supervised / unsupervised learning) algorithms,
- Learning the basics of working on data flowing with Spark used to perform in-memory analysis and analytical studies on big data and the use of machine learning algorithms,
- Sample application studies.
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| Syllabus | - Data Science Fundamentals
- Machine Learning Methods
- Spark Machine Learning (ML)
- Spark ML Lab Study
- Application Study
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