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EMC DEA-7TT2 certification exam is an associate-level certification program that aims to equip individuals with the essential knowledge and skills to handle big data and apply data science techniques in a real-world scenario. Associate - Data Science and Big Data Analytics v2 Exam certification exam is designed for individuals who are interested and passionate about data science and big data analytics. It is a comprehensive program that provides an in-depth understanding of key data analytics concepts such as data querying, data cleansing, data transformation, and data visualization.
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The DEA-7TT2 exam covers data mining techniques. This section tests the candidate’s ability to use data mining techniques to extract meaningful insights from large datasets. Candidates are also tested on their knowledge of machine learning algorithms and statistical analysis methods.
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To earn the EMC DEA-7TT2 certification, candidates must pass a rigorous exam that tests their understanding of various data science and big data analytics concepts. DEA-7TT2 exam consists of 60 multiple-choice questions that must be completed within 90 minutes. Candidates must score at least 60% to pass the exam and earn the certification. Associate - Data Science and Big Data Analytics v2 Exam certification is ideal for IT professionals who want to gain expertise in data science and big data analytics and stand out in the competitive job market.
EMC DEA-7TT2 exam is an Associate-level certification exam for professionals seeking to validate their skills and knowledge in the field of Data Science and Big Data Analytics. DEA-7TT2 exam is designed to test candidates' understanding of the fundamental concepts and principles of data analysis, statistical modeling, and machine learning, with a focus on practical applications in real-world scenarios.
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| Section | Weight | Objectives |
|---|---|---|
| Advanced Analytics - Theory, Application, and Interpretation | 40% | - Linear and logistic regression - Classification and clustering techniques - Unsupervised and supervised learning concepts - Model evaluation and validation methods - Association rules and time series analysis |
| Big Data, Analytics, and the Data Scientist Role | 5% | - Characteristics of Big Data - Business drivers for Big Data analytics - Role and required skills of a Data Scientist |
| Operationalizing an Analytics Project and Data Visualization | 10% | - Visualization best practices and tools - Guidelines for communicating findings - Deployment and integration of models |
| Advanced Analytics for Big Data - Technology and Tools | 22% | - Hadoop ecosystem and its components - Advanced SQL: window functions, aggregates, MADlib - Spark and distributed processing frameworks - In-database analytics and SQL fundamentals |
| Data Analytics Lifecycle | 8% | - Purpose and phases of the lifecycle - Model planning phase activities and roles - Data preparation phase activities and roles - Communicating results and operationalization - Model building phase activities and roles - Discovery phase activities and roles |
| Initial Analysis of the Data | 15% | - Statistical measures and data visualization - Hypothesis testing concepts and application - Basic R commands for data exploration |
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