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Exam Number: D-DS-FN-23

Available Languages: English, French, Japanese

Certification Overview

This exam focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with R, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques.

Target Audience

Systems Engineers; Technical Consultants

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Practice tests are unproctored web based exams. Practice tests are not available for Achievements.

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Achievement exams are web based. Skills certification Exams are proctored exams administered though Pearson Vue testing centers and OnVUE.

Exam Topics

The table below lists the topics that are included in this exam, and the approximate percentage of the exam devoted to each topic.

Category	Topics	Coverage
Big Data, Analytics, and the Data Scientist Role	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	5%
Data Analytics Lifecycle	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	8%
Initial Analysis of the Data	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	15%
Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods	Describe 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	40%
Advanced Analytics for Big Data - Technology and Tools	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	22%
Operationalizing an Analytics Project and Data Visualization Techniques	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	10%

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Data Science Foundations