EMC DEA-7TT2 dumps - in .pdf

DEA-7TT2 pdf
  • Exam Code: DEA-7TT2
  • Exam Name: Associate - Data Science and Big Data Analytics v2 Exam
  • Updated: Aug 02, 2026
  • Q & A: 233 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: DEA-7TT2
  • Exam Name: Associate - Data Science and Big Data Analytics v2 Exam
  • Updated: Aug 02, 2026
  • Q & A: 233 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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EMC DEA-7TT2 dumps - Testing Engine

DEA-7TT2 Testing Engine
  • Exam Code: DEA-7TT2
  • Exam Name: Associate - Data Science and Big Data Analytics v2 Exam
  • Updated: Aug 02, 2026
  • Q & A: 233 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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What are the prerequisites for Dell DEA-7TT2 Certification Exam?

There are no formal prerequisites for Dell EMC DEA-7TT2 exam. However, it's recommended that you have:

  • 6 months experience in the IT industry and knowledge of data science fundamentals

  • 3 months hands-on experience with data science tools

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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EMC DEA-7TT2 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Operationalizing an Analytics Project and Data Visualization Techniques10%- Analytics project deployment and visualization
  • 1. Building project presentations for audiences
  • 2. Planning and creating effective data visualizations
  • 3. Communicating findings and operationalizing analytics projects
Topic 2: Big Data, Analytics, and the Data Scientist Role5%- Characteristics of Big Data
  • 1. Data Scientist role and related skills
  • 2. Business drivers for Big Data analytics and data science
Topic 3: Advanced Analytics for Big Data - Technology and Tools22%- Big Data technologies and analytics tools
  • 1. Advanced SQL methods and MADlib
  • 2. Hadoop ecosystem and product use cases
  • 3. MapReduce and Apache Hadoop
  • 4. In-database analytics and SQL essentials
  • 5. Big Data technology challenges
Topic 4: Initial Analysis of the Data15%- Data exploration and statistical analysis
  • 1. Statistical measures and data visualization
  • 2. Basic R commands for data analysis
  • 3. Hypothesis testing and model evaluation
Topic 5: Data Analytics Lifecycle8%- Data Analytics Lifecycle phases
  • 1. Model planning phase activities and roles
  • 2. Discovery phase activities and roles
  • 3. Data preparation phase activities and roles
  • 4. Model building phase activities and roles
Topic 6: Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods40%- Advanced analytics methods
  • 1. Decision trees
  • 2. Association rules
  • 3. Logistic regression
  • 4. K-means clustering
  • 5. Linear regression
  • 6. Naïve Bayesian classifiers
  • 7. Text Analytics
  • 8. Time Series Analysis

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