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### Overview

Introduction: This exam assesses a candidate's analytical and data interpretation skills. The assessment covers several topics related to Data Manipulation using R, Modern Data Science with R, and Analytics with R and other Tools. With Data Science and Analytics being a multidisciplinary field, students will also learn about Data Visualization, Machine Learning Techniques, Data Science Aptitude, Exploratory Data Analysis, Regression Analysis, and Statistics.

The Data Science and Analytics Test will ensure students know how to identify alternative analytical interpretations as a means to minimize unanticipated outcomes. The assessment will help students develop skills in storing, retrieving, and manipulating data as a way to maximize the analysis of system capabilities and requirements. The exam will also assess the student's ability in using data visualization tools such as D3.js and Tableau, and also how they apply quantitative techniques such as descriptive and inferential statistics, and utilize R as an open-source language. Understanding Data Science and Analytics will also help students work toward expertise in Regression Analysis using the Generalized Linear Model, Tree-Based Methods, and Logistics.

These valuable skills are essential to the work role(s) of Data Scientists, Data Science Engineer, Data Science Developer, Data Science Associate, and Data Visualization Designer. Upon successful completion of this lab, students will be able to identify areas within their skillset that may require proficiency as it relates to working with Data Science and Analytics. This assessment can help students prepare for job interviews for positions requiring foundational knowledge of Data Science and Analytics.

Skill/Activity Breakdown The Data Science and Analytical Test is an assessment, which will focus on determining your knowledge of Data Science and Analytics. Although taking the assessment will not develop proficiency, you can use the assessment to gain a better understanding of your skillset as it relates to the Data Science and Analytics platform. The topics covered in the exam will help further your knowledge and provide you with the appropriate tools you can immediately apply and begin to develop.

The Data Science and Analytics Test covers topics that relate to identifying alternative analytical interpretations as a means to minimize unanticipated outcomes. This skill is important for the Data Scientist, Data Associate, Data Science Developer, All Source Analyst, Target Developer, and Mission Assessment Specialist work roles.

The Data Science and Analytics Test covers topics that relate to developing skills in storing, retrieving, and manipulating data as a way to maximize analysis of system capabilities and requirements. These skills are important for the Software Developer, Systems Developer, Data Science Engineer, and Data Science Developer work roles.

The Data Science and Analytics Test covers topics that relate to using data visualization tools such as D3.js and Tableau. This skill is important to the Data Scientist and Data Visualization Designer work roles.

The Data Science and Analytics Test covers topics that relate to applying quantitative techniques such as descriptive and inferential statistics and using R as an open-source language. This skill is important to the Data Scientist work role.

The Data Science and Analytics Test will also help students work toward expertise in Regression Analysis using the Generalized Linear Model, Tree-Based Methods, and Logistics. This skill is important for the Data Scientist work role.

By completing the Data Science and Analytics Test, students will have gained a foundational understanding of the Data Science and Analytics platform. They will have skills in identifying alternative analytical interpretations as a means to minimize unanticipated outcomes as well as skills in storing, retrieving, and manipulating data as a way to maximize analysis of system capabilities and requirements. Students will know data visualization tools such as D3.js and Tableau and can apply quantitative techniques such as descriptive and inferential statistics using R as an open-source language. Students will have work toward expertise in Regression Analysis using the Generalized Linear Model, Tree-Based Methods, and Logistics.

These skills are important for several work roles, including Data Scientists, Data Science Engineer, Data Science Developer, Data Science Associate, Data Visualization Designer, Software Developer, Systems Developer, All Source Analyst, Target Developer, and Mission Assessment Specialist.

Conclusion: The Data Science and Analytics Test is presented by Cybrary and was created by Interview Mocha. This lab assesses knowledge and skills related to working with Data Science and Analytics. Completing the lab gives the student an objective assessment of their understanding and abilities as it relates to working with Data Science and Analytics.

Click on the Data Science and Analytics Test to assess your knowledge within the platform.