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- Data Analytics Lifecycle (9%)
- Big Data Analytics and the Data Scientist Role (7%)
- Initial Analysis of the Data (15%)
- Advanced Analytics for Big Data - Technology and Tools (20%)
- Operationalizing an Analytics Project and Data Visualization Techniques (9%)
- Advanced Analytics for Big Data - Theory and Methods (40%)
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EMC E20-007 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Ethics, Governance, and Business Applications | - Data privacy and ethical considerations - Data governance principles - Business use cases of data science |
| Big Data and Data Science Fundamentals | - Roles and responsibilities in data science projects - Big Data concepts and characteristics (Volume, Velocity, Variety) - Data Science lifecycle and methodologies |
| Data Visualization and Interpretation | - Data visualization techniques and tools - Communicating insights from data - Exploratory data analysis (EDA) |
| Statistical and Analytical Methods | - Inferential statistics fundamentals - Data sampling and distributions - Descriptive statistics and probability basics |
| Machine Learning and Predictive Analytics | - Supervised and unsupervised learning concepts - Classification and regression techniques overview - Model evaluation and validation basics |
| Data Management and Processing | - Data ingestion and data preparation techniques - Hadoop ecosystem overview (HDFS, MapReduce) - Structured vs unstructured data handling |
