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

EMC DEA-7TT2 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods	40%	- Advanced analytics methods 1. Text Analytics 2. Linear regression 3. K-means clustering 4. Association rules 5. Logistic regression 6. Decision trees 7. Naïve Bayesian classifiers 8. Time Series Analysis
Topic 2: Operationalizing an Analytics Project and Data Visualization Techniques	10%	- Analytics project deployment and visualization 1. Communicating findings and operationalizing analytics projects 2. Building project presentations for audiences 3. Planning and creating effective data visualizations
Topic 3: Advanced Analytics for Big Data - Technology and Tools	22%	- Big Data technologies and analytics tools 1. Hadoop ecosystem and product use cases 2. Advanced SQL methods and MADlib 3. MapReduce and Apache Hadoop 4. In-database analytics and SQL essentials 5. Big Data technology challenges
Topic 4: Initial Analysis of the Data	15%	- Data exploration and statistical analysis 1. Hypothesis testing and model evaluation 2. Statistical measures and data visualization 3. Basic R commands for data analysis
Topic 5: Big Data, Analytics, and the Data Scientist Role	5%	- Characteristics of Big Data 1. Data Scientist role and related skills 2. Business drivers for Big Data analytics and data science
Topic 6: Data Analytics Lifecycle	8%	- Data Analytics Lifecycle phases 1. Model planning phase activities and roles 2. Data preparation phase activities and roles 3. Model building phase activities and roles 4. Discovery phase activities and roles