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What do You need To Know About The Dell DEA-7TT2 Certification Exam?
If you work in the information technology sector, you have probably heard a lot of people discussing their certifications. Since the beginning of IT certifications, the number of available certificates has been steadily rising. The Dell DEA-7TT2 certification exam is just one that you may want to consider. Let's take a closer look at what this certification entails. By looking at the different things that are covered by this certificate, you can make an informed decision about whether it is relevant to your career path. Dell DEA-7TT2 exam dumps will help you to get certified.
The Dell DEA-7TT2 certification has been developed to enhance individual skills and knowledge in Data Science and Big Data Analytics. The test will verify that the professionals have the ability to handle the complex tasks of Data Science and Big Data Analytics. It will also check if the candidates are well versed with the advanced features of Data Science and Big Data Analytics software. The certification program will ensure that the professionals associated with Dell services are highly qualified in design and implementation of data center systems.
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Reference: https://education.emc.com/content/dam/dell-emc/documents/en-us/DEA-7TT2_Associate-Data_Science_and_Big_Data_Analytics_v2_Exam.pdf
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If you work in IT, you probably know that receiving a Dell certification can be a great way to advance your career. In fact, many IT professionals look at Dell certifications as their goal when they begin their career. Even if you have no intention of staying with one employer your whole life, having a Dell certification on your resume makes you more marketable and can help you command higher salaries in the industry.
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EMC DEA-7TT2 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Initial Analysis of the Data | 15% | - Hypothesis testing concepts and application
- Statistical measures and data visualization
- Basic R commands for data exploration
|
| Operationalizing an Analytics Project and Data Visualization | 10% | - Visualization best practices and tools
- Deployment and integration of models
- Guidelines for communicating findings
|
| Advanced Analytics - Theory, Application, and Interpretation | 40% | - Model evaluation and validation methods
- Association rules and time series analysis
- Classification and clustering techniques
- Unsupervised and supervised learning concepts
- Linear and logistic regression
|
| Advanced Analytics for Big Data - Technology and Tools | 22% | - Spark and distributed processing frameworks
- Advanced SQL: window functions, aggregates, MADlib
- Hadoop ecosystem and its components
- In-database analytics and SQL fundamentals
|
| Data Analytics Lifecycle | 8% | - Model building phase activities and roles
- Data preparation phase activities and roles
- Communicating results and operationalization
- Purpose and phases of the lifecycle
- Model planning phase activities and roles
- Discovery phase activities and roles
|
| Big Data, Analytics, and the Data Scientist Role | 5% | - Business drivers for Big Data analytics
- Characteristics of Big Data
- Role and required skills of a Data Scientist
|