Course Objectives

The course objectives of the Enterprise Big Data Analyst course include an advanced understanding of Big Data analysis concepts and algorithms. Moreover, an Enterprise Big Data Analyst is able to correctly interpret data and draw conclusions.

A certified Enterprise Big Data Analyst has proficiency in key models and concepts that are required to analyze data on a day-to-day basis. (S)he understands the theoretical difference between different statistical and machine learning approaches and is able to explain the difference between models and select and apply the appropriate model when confronted with a particular business problem.

This Enterprise Big Data Analyst course will prepare participants to:

• Understand and explain the data analysis process, including all relevant steps included in enterprise big data analysis.
• Understand the difference and structure of common data sources (local, online and database connections) and the way these sources should be imported in order to perform data analysis.
• Apply and utilize fundamental data cleaning operations and the differences between different data cleaning techniques.
• Apply and utilize fundamental data wrangling operations and the differences between different data wrangling techniques.
• Understand and apply exploratory data analysis techniques that are required for model building, model validation and initial visualizations.
• Understand and apply the core concepts of statistical inference, including techniques required for hypothesis testing.
• Formulate and interpret predictive models based on statistical correlation and regression functions, including simple linear regression.
• Formulate and interpret machine learning models for classification, including K-Nearest Neighbour, Naïve Bayes, Logistic Regression and Classification Trees.
• Formulate and interpret machine learning models for clustering, including the Hierarchical clustering and K-means clustering techniques.
• Formulate and interpret outlier detection models, including Grubbs Outlier detection and K-NN Outlier Detection.
• Understand and apply the core data presentation, techniques including codebooks and visualizations to present the findings of their analysis.

Please note that an in-depth course outline with a description of all topics is available here.

This qualification is aimed at individuals who are involved in enterprise Big Data analysis, who require a working knowledge of the principles behind Big Data analysis techniques, and who need to know the different statistical and machine learning techniques to make the right decisions.The target audience of the Enterprise Big Data Analyst qualification therefore includes the following roles:

• Data Analysts
• Business Analysts
• Business Data Analysts
• Systems Analysts
• Data Management Analysts
• Business Analytics Consultants
• Data Scientists
• Data Modellers

Learning Materials

Participants to the Enterprise Big Data Analyst course will receive the following study materials:

• 32 hours of instructor-led training and exercise facilitation
• Learner Manual (excellent post-class reference)
• Participation in unique exercises designed to apply concepts
• Sample documents, templates, tools and techniques
• Access to additional value-added resources and communities

Prerequisites

The Enterprise Big Data Analyst is an advanced level course, that will require experience in the analysis of data and common data operations techniques. The Enterprise Big Data Professional level is a mandatory prerequisite.

Exam

Successfully passing (65%) the 150-minute examination, consisting of 80 complex multiple-choice questions, leads to the Enterprise Big Data Analyst (EBDA) Certificate. The examination and certification process is administered by APMG-International on behalf of the Enterprise Big Data Framework Alliance.