Overview

The Enterprise Big Data Analyst (EBDA) course discusses advanced techniques for the analysis of Big Data. In this course, you will learn how you can obtain value from data through statistical and machine learning techniques and how this analysis should be presented in a reproductible manner.

The Enterprise Big Data Analyst course discusses advanced data analysis techniques in the context of Big Data. Working is a structure and reproductible manner, this course provides an overview of the most common algorithms for exploratory data analysis, statistical inference, predictive modelling and machine learning techniques (classification and clustering). Course participants will learn the underlying theory of the different algorithms, and how each algorithm can be applied in practice in the R programming language.

The Enterprise Big Data Analyst course is the second level of the Big Data Framework course curriculum and certification program, that is globally accredited by APMG-International. The curriculum provides a vendor-neutral and objective understanding of Big Data architectures, technologies and processes.

The Enterprise Big Data Analyst qualification is a practitioner course for all data professionals that aim to an in-depth understanding of Big Data analysis techniques and models, core data analysis processes steps, and best practices to retrieve value from data.

The course will provide an overview of statistical and machine learning models, which are illustrated in the R programming language. This certification will not test programming skills. The emphasis is on the correct application of the theoretical models, however participants are required to understand the output of programming languages in order to draw conclusions from the results of analysis.

Skills Covered

The Enterprise Big Data Analyst (EBDA®) qualification shows that candidates possess the skill to analyze Big Data and are able to understand key data analysis concepts and techniques. Moreover, an Enterprise Big Data Analyst is able to correctly interpret data and draw conclusions. The Enterprise Big Data Analyst qualification builds upon the first level of the Big Data Framework qualification scheme (EBDP) in which fundamental knowledge and elementary concepts related to Big Data were covered.

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.

Certified candidates should be able to deduce value out of Big Data sets but may not be sufficiently skilled to do this autonomously for all types of problems. Their Big Data experience, complexity of the problem, and the support provided in their work environment would all be factors that impact what the certified candidate can achieve.

Specifically, (s)he should be able to demonstrate this understanding by being able 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.

Who Should Attend

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

Course Curriculum

Prerequisites

The pre-requisites for taking the Enterprise Big Data Analyst examination is passing the Enterprise Big Data Professional certificate.

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