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EMC DEA-7TT2 (Associate - Data Science and Big Data Analytics v2) Exam is a certification exam that measures one's proficiency in data science and big data analytics. DEA-7TT2 exam covers various topics such as data mining, statistical analysis, machine learning, and data visualization. Associate - Data Science and Big Data Analytics v2 Exam certification is intended for professionals who want to validate their skills and knowledge in the field of data science and big data analytics.
The DEA-7TT2 exam is designed to test the candidate’s understanding of big data analytics technologies, data mining techniques, and statistical analysis methods. DEA-7TT2 exam also covers topics such as data visualization, machine learning, and data management. DEA-7TT2 exam is divided into six sections, each focusing on a specific topic related to big data analytics.
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EMC DEA-7TT2 (Associate - Data Science and Big Data Analytics v2) is a certification exam that validates the skills and knowledge of professionals in the field of data science and big data analytics. DEA-7TT2 exam is designed for individuals who want to demonstrate their expertise in using big data tools and techniques to solve complex business problems. Associate - Data Science and Big Data Analytics v2 Exam certification is offered by Dell EMC, a multinational technology company that provides data storage, information security, cloud computing, and other services.
EMC DEA-7TT2 exam is a valuable certification for professionals seeking to validate their expertise in the field of data science and big data analytics. It is a rigorous exam that requires a comprehensive understanding of statistical tools and techniques, as well as practical experience in using these tools to solve real-world problems. Passing DEA-7TT2 exam can help professionals advance their careers and demonstrate their commitment to staying current with the latest trends and technologies in this rapidly evolving field.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Advanced Analytics - Theory, Application, and Interpretation | 40% | - Model evaluation and validation methods - Linear and logistic regression - Classification and clustering techniques - Association rules and time series analysis - Unsupervised and supervised learning concepts |
| Topic 2: Initial Analysis of the Data | 15% | - Basic R commands for data exploration - Hypothesis testing concepts and application - Statistical measures and data visualization |
| Topic 3: Advanced Analytics for Big Data - Technology and Tools | 22% | - Advanced SQL: window functions, aggregates, MADlib - In-database analytics and SQL fundamentals - Spark and distributed processing frameworks - Hadoop ecosystem and its components |
| Topic 4: Big Data, Analytics, and the Data Scientist Role | 5% | - Role and required skills of a Data Scientist - Business drivers for Big Data analytics - Characteristics of Big Data |
| Topic 5: Data Analytics Lifecycle | 8% | - Discovery phase activities and roles - Model planning phase activities and roles - Communicating results and operationalization - Purpose and phases of the lifecycle - Model building phase activities and roles - Data preparation phase activities and roles |
| Topic 6: Operationalizing an Analytics Project and Data Visualization | 10% | - Visualization best practices and tools - Guidelines for communicating findings - Deployment and integration of models |
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