Course Duration ± 32 Hours
“The Enterprise Big Data Analyst (EBDA®) course will teach participants advanced Big Data analysis techniques, and will guide participants how to build reproductible data analysis models.”
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 demand for qualified Big Data Analysts has been exploding in recent years. The skills required to perform structured data analysis are the cornerstone of the Big Data Analyst’s profession. This course provides a strong theoretical basis for everyone how aims to learn advanced data analysis techniques.
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.
This course positions learners to successfully complete the Enterprise Big Data Analyst certification exam.
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.
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
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
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.
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.
Detailed Course Outline
Introduction to Big Data Analysis
- What is Enterprise Big Data Analysis?
- The Objective of Enterprise Big Data Analysis
- The Data Analyst versus the Data Scientist
- The Big Data Analysis Toolbox
- Models, Algorithms and Intellectual Property
The Data Analysis Process
- The Business objective
- Types of Business Objectives
Data Ingestion – Importing and Reading Data
- Raw versus Processed Data
- Reading Local Data Sets
- Reading Online Data Set
- Reading Data Sets from Databases
Data Preparation – Cleaning and Wrangling Data
- Tidy Data
- Data Inspection – Review your Data
- Data Cleaning
- Data Wrangling
- Data and R Files for this Chapter
Data Analysis – Model Building
- Introduction to Data Analysis
- Exploratory Data Analysis
- Statistical Inference
- K-Nearest Neighbour (K-NN algorithm)
- Dimensions in the k-NN classifiers
- Naïve Bayes
- Naïve Bayes Classifier with multiple variables
- Laplace Smoothing
- Logistic Regression
- Classification Trees
- Building a Classification tree
- Model Overfitting and Accuracy
- Hierarchical Clustering
- Variations in hierarchical clustering
- Jaccard index
- K-Means Clustering
- Grubbs Outlier detection
- K-NN Outlier Detection
- Introduction to Reproducible Research
- Data Visualisation
Download the Enterprise Big Data Analyst guide for FREE