DEA-7TT2: Data Science and Big Data Analytics Exam

This certification enables the learner to immediately participate in big data and other analytics projects. The certification validates the practical foundation skills required by a Data Scientist.

Exam Name: Dell EMC Data Science and Big Data Analytics

Exam Code: DEA-7TT2

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Dell EMC DEA-7TT2 Exam Summary:

Exam Name Dell EMC Data Science and Big Data Analytics 
Exam Code DEA-7TT2 
Exam Duration  90 minutes 
Exam Questions  60 
Passing Score  60% 
Exam Price  $230 (USD)
Books  Data Science and Big Data Analytics v2 – On-Demand Video 
Sample Questions  Dell EMC Data Science Associate Certification Sample Question 
Practice Exam   Dell EMC Data Science Associate Certification Practice Exam

Dell EMC DEA-7TT2 Exam Topics:

Objective Details 
Big Data, Analytics, and the Data Scientist Role (5%)

– Define and describe the characteristics of Big Data

– Describe the business drivers for Big Data analytics and data science

– Describe the Data Scientist role and related skills

Data Analytics Lifecycle (8%) 

– Describe the data analytics lifecycle purpose and sequence of phases

– Discovery -Describe details of this phase, including activities and associated roles

– Data preparation -Describe details of this phase, including activities and associated roles

– Model planning -Describe details of this phase, including activities and associated roles

– Model building -Describe details of this phase, including activities and associated roles 

Initial Analysis of the Data (15%)

– Explain how basic R commands are used to initially explore and analyze the data

– Describe and provide examples of the most important statistical measures and effective visualizations of data

– Describe the theory, process, and analysis of results for hypothesis testing and its use in evaluating a model

Advanced Analytics -Theory, Application, and Interpretation of Results for Eight Methods (40%)

Describe the theory, application, and interpretation of results for the following methods:

– K-means clustering

– Association rules

– Linear regression

– Logistic Regression

– Naïve Bayesian classifiers

– Decision trees

– Time Series Analysis

– Text Analytics

Advanced Analytics for Big Data -Technology and Tools (22%) 

– Describe the technological challenges posed by Big Data

– Describe the nature and use of MapReduce and Apache Hadoop

– Describe the Hadoop ecosystem and related product use cases

– Describe in-database analytics and SQL essentials

– Describe advanced SQL methods: window functions ordered aggregates, and MADlib

 

Operationalizing an Analytics Project and Data Visualization Techniques (10%)

– Describe best practices for communicating findings and operationalizing an analytics project

– Describe best practices for building project presentations for specific audiences

– Describe best practices for planning and creating effective data visualizations