Enterprise Big Data Scientist


Course Duration ± 40 Hours

The Enterprise Big Data Scientist (EBDS) course is the epitome of modern data science, offering participants comprehensive training in advanced statistical and computational techniques to extract valuable insights from vast large datasets. This cutting-edge course establishes a strong theoretical foundation for aspiring data scientists, with a special emphasis on the latest machine learning methods.


The Enterprise Big Data Scientist (EBDS®) course provides a solid theoretical foundation of data science and provides the tools to design and develop algorithms to extract value from massive data sets. In this course, you will get and in-depth overview of the theory and tools required for data communication, machine learning and modeling. With these tools, you will build up an elaborate toolkit with which you can design robust models and algorithms, and will be able to develop the best suited models to tackle diverse business problems.

The Enterprise Big Data Scientist course provides an in-depth overview of statistical modeling and modeling techniques for more complex enterprise problems. Participants will learn statistical and machine learning theory, and will be provided with an additional practically-focused syllabus predominantly in Python that runs in parallel with the theory for hands-on learning. The objective of this parallel syllabus is to not just learn the best practices of modern data science, but how to harness and apply algorithms to extract valuable information from real-world situations.

Topics in this course include statistical methods, a range of machine learning methods from supervised learning to deep learning, data communication & visualization, and applied data science in the real world, and more! We encourage you to view the syllabus overview below to see what each module specifically includes.

The Enterprise Big Data Scientist course is the third 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 technologies, algorithms and processes.

The Enterprise Big Data Scientist qualification is a practitioner course aimed at data analysts and aspiring data scientists who wish to obtain an in-depth understanding of the latest cutting-edge methods in data science. In this course, we will focus heavily on the scientific aspects, so that every participant will have the knowledge and skills to design algorithmic solutions to enterprise problems. In order to become an iron-clad data scientist, mastering statistical, computational, and communication techniques in data is of crucial importance.

The course will predominantly demonstrate practical techniques in Python (with R and SQL being briefly used too). Although this certification will not test programming skills, familiarity with Python and notebooks will be necessary during this course. In the corresponding examination, the emphasis is on the correct application of the theoretical models. While this is the case, participants are still required to understand Python scripts and their respective outputs to interpret results.

This course positions learners to successfully complete the Enterprise Big Data Scientist certification exam.

Course Objectives

The course objectives of the Enterprise Big Data Scientist program include an advanced understanding of statistical and computational techniques to extract meaningful information from large datasets. Moreover, an Enterprise Big Data Scientist must be able to optimize models for accuracy, and select the best potential model to solve a business problem.

A certified Enterprise Big Data Scientist has proficiency in the design of advanced algorithms for classification, regression, and clustering problems. Moreover, they should be able to select the best design to optimize solutions for different input data. (S)he understands the theoretical difference between advanced statistical and machine learning models and is able to explain the most ideal way to measure performance. In a nutshell, a certified Enterprise Big Data Scientist can visualize the most effective solution when confronted with a particular business problem.

This Enterprise Big Data Scientist course will prepare participants to:

  • Understand tools for statistical modeling for large datasets.
  • Understand the different ways of quantifying performance for different applications.
  • Understand the fundamental underlying techniques in statistics, machine learning, distributed technologies, and business acumen.
  • Apply parametric and non-parametric models to fit datasets from real-world problems and asses the quality of these models.
  • .Understand the deployment of machine learning models with supervised learning, unsupervised learning, and deep learning, depending on the data and business application.
  • Understand how to use binary and categorical data into linear regression problems.
  • Understand how to solve classification problem using the Bayes optimal classifier, logistic regression and Linear Discriminant Analysis (LDA).
  • Analyse the accuracy of different classification approaches using confusion matrices, different types of error rates, and the ROC of a classifier.
  • Understand dataset manipulation techniques for statistical learning problems, including validation techniques, bootstrapping, bagging, and other dataset techniques.
  • Apply bolstering techniques to individual weaker models with the goal of creating stronger ensemble models.
  • Apply and understand non-linear classification techniques, including Support Vector Machines (SVM).
  • Understand the potential advantages and challenges associated with neural networks, including convolutional, recurrent, and generative networks.
  • Apply and understand the best-practices of data visualizations that are ideal to the nature in which the human brain perceives qualitative and quantitative data.
  • Understand the process of handling big data in distributed system environments.
  • Applying distributed system knowledge to determine ideal cloud systems and distributed environment architecture.
  • Understand the ethical considerations and challenges behind big data, AI, and maneuvering in accordance with laws and regulations.


This qualification is aimed at individuals who are involved in enterprise Big Data analysis, analytics or data science, and who aspire to be become professional data scientistic. Please note that this is an advanced course, requiring significant efforts to pass the exam and obtain the qualification.

The target audience of the Enterprise Big Data Scientist qualification therefore includes the following roles:

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

Learning Materials

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

  • 40 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 Scientist is an advanced level course, that will require working experience in data analysis techniques. The Enterprise Big Data Analyst level is a mandatory prerequisite. Participant who have a similar pre-requisite can request a waiver of the pre-requisite from the Enterprise Big Data Framework Alliance Educational Board.


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

Detailed Course Outline

Introduction to the Enterprise Big Data Scientist Certification

  1. Introduction to Data Science

  2. Python Review

  3. EBDF Review

Statistical Methods for Data Science and Machine Learning

  1. Probability Distributions

    1. Discrete & Continuous Distributions
    2. Distribution Types
    3. Sampling
    4. Logic & Fuzzy Logic
    5. Bayes’ Theorem & Bayesian Networks
    6. Feature Scaling
  2. Parametric Models

    1. Parametric vs. Nonparametric Models
    2. Calculus
    3. Loss Functions
  3. Assessing Model Quality

    1. The Bias-Variance Tradeoff
    2. Curse of Dimensionality
    3. Confusion Matrices

Advanced Techniques for Machine Learning

  1. Machine Learning Review

    1. The Machine Learning Pipeline
    2. Algorithms Review
  2. Data Mining

  3. Measuring Model Performance

    1. Classification Performance Metrics
    2. Regression Performance Metrics
    3. Setting Hyperparameters
  4. Dealing with Black Boxes

Supervized Machine Learning

  1. Feature Selection and Validation

    1. Feature Selection and Engineering
    2. Validation Set Approach
    3. K-Fold Cross Validation
    4. Bootstrapping
    5. Forward/Backward Stepwise Selection
  2. Linear Classifiers

    1. Hyperplanes and Max Margin Hyperplanes
    2. Support Vector Classifiers and Machines
    3. Logistic Regression
    4. Linear Discriminant Analysis
  3. Non-Linear Classifiers

    1. Tree-Based Methods

Unsupervized Machine Learning

  1. Clustering

    1. K-Means Clustering
    2. Hierarchical Clustering
    3. Density-Based Clustering
    4. Evaluating Clustering Results
  2.  Principal Component Analysis (PCA)

    1. Introducing PCA
    2. The PCA Algorithm
    3. Practical Applications of PCA
    4. Interpreting PCA Results
  3. Generative Models

    1. Types of Generative Models
    2. Applications of Generative Models
    3. Evaluating Generative Models

Data Visualization and Communication

  1. Effective Data Visualization& Communication

    1. Data Abstraction
    2. Visual Perception & Cognition
    3. Color Perception and Use in Visualizations
    4. Design Principles for Effective Visualizations
    5. Visualization Ethics and Responsible Practices
    6. Cutting-Edge Visualization Techniques
  2. Visualization Tools

    1. Python
    2. R
    3. Power BI
    4. Tableau
  3. Data Storytelling

Distributed Systems

  1. Distributed Cloud Computing Review

    1. Cloud Computing Fundamentals
    2. Parallel and Distributed Computing
    3. Scalability and Fault Tolerance
    4. Distributed File Systems
    5. Distributed Processing Frameworks
  2. Apache Spark Introduction

    1. Spark Architecture
    2. Spark Data Processing
    3. Machine Learning with Spark
  3. Spark Deployment

    1. Cluster Management Systems
    2. Infrastructural Considerations
    3. Monitoring and Debugging
    4. Scaling Spark Deployments
    5. Deployment Best Practices

Deep Learning

  1. Introduction to Deep Learning

    1. Neural Network Architecture
    2. Deep Learning vs. Machine Learning
  2. Deep Learning Techniques

    1. Convolutional Neural Networks
    2. Recurrent Neural Networks
    3. Transfer Learning
    4. Generative Adversarial Networks

Applied Data Science

  1. Bringing Data Science to Life in Web3

  2. Big Data Case Studies

    1. Consumer Goods and Services
    2. Tech
    3. Healthcare
    4. The Enterprise Big Data Framework Alliance
  3. Data Ethics

    1. Privacy and Transparency
    2. AI and its Baggage: Bias and Fairness
    3. Accountability

Exam Prep Materials

Whether you prefer to prep on your own time or with the additional guidance and interaction that comes with live, expert instruction, EBDFA has the right test prep solutions for every professional. Choose what works for your schedule and your studying needs.

Enterprise Big Data Scientist E-Learning

Sign Up for the E-Learning Course

The Enterprise Big Data Scientist E-Learning Course with Exam contains the complete set of all course materials that you will need in order to prepare for the Enterprise Big Data Scientist certification. The EBDFA Self Paced Online Course has been specifically designed for people who prefer to study training materials in their own pace in their own time. The Enterprise Big Data Scientist E-Learning course contains the materials that students would normally obtain in a classroom as well as a number of supplementary resources to help you prepare for the examination.


Attend a Live Training Course

The Enterprise Big Data Alliance has has made agreements with the most reputable training providers across the world to host classroom Big Data training. Through our extensive partner network of partners all over the world, you can attend all the modules of the Enterprise Big Data Framework, taught by accredited Big Data Experts.

Big Data Training

Request a Customized Corporate Group Training

In today’s competitive market, corporate training isn’t an employee luxury, it’s a necessity. Our expert-led training solutions recognize your staff’s individual expertise levels, helping them learn everything from core competencies to the latest best practices. In case you are interested in upskilling groups of people in your organization, please contact us to discuss the details.