EBDS study guide · 2026

The 9 EBDS domains, explained

9 areas and 27 topics. Enterprise Big Data Framework Alliance does not publish weights for these areas, so each gets equal space here. Each guide explains what the area covers, how it is assessed and where candidates slip.

Start with Domain 1 See all domains
Questions
80
Time
150 minutes
Passing score
65% (52 of 80 questions)
Outline
Enterprise Big Data Scientist course syllabus, 9 modules (current public version; no version number or effective date published, page updated 2025-08)

Exam blueprint

Enterprise Big Data Framework Alliance does not publish weights, so every area is drawn the same size.

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  1. 1 Introduction to the Enterprise Big Data Scientist Certification
  2. 2 Statistical Methods for Data Science and Machine Learning
  3. 3 Advanced Techniques for Machine Learning
  4. 4 Supervised Machine Learning
  5. 5 Unsupervised Machine Learning
  6. 6 Data Visualization and Communication
  7. 7 Distributed Systems
  8. 8 Deep Learning
  9. 9 Applied Data Science

All 9 domains

27 official topics 90–135 hrs suggested study

  1. Domain 01

    Introduction to the Enterprise Big Data Scientist Certification

    The opening module resets three foundations the rest of the EBDS course assumes: a working definition of data science, Python fluency for reading notebook code, and the Enterprise Big Data Framework vocabulary you met at Analyst level.

    Inside this domain

    • Introduction to Data Science
    • Python Review
    • EBDF Review

    Watch for Model equals prescriptive

    Topics
    3
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  2. Domain 02

    Statistical Methods for Data Science and Machine Learning

    This module is the statistical engine under every model in the EBDS course: how data is distributed and sampled, how parametric models learn through loss functions, and how to judge model quality before you trust it.

    Inside this domain

    • Probability Distributions
    • Parametric Models
    • Assessing Model Quality

    Watch for Ignoring the base rate

    Topics
    3
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  3. Domain 03

    Advanced Techniques for Machine Learning

    This module turns statistics into a working machine learning practice: an ordered pipeline that avoids leakage, data mining patterns, the right metric for each problem, disciplined hyperparameter tuning, and ways to explain models nobody can read directly.

    Inside this domain

    • Machine Learning Review
    • Data Mining
    • Measuring Model Performance
    • Dealing with Black Boxes

    Watch for Tuning on the test set

    Topics
    4
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  4. Domain 04

    Supervised Machine Learning

    Supervised learning is where the EBDS syllabus gets most specific: how to choose and validate features, how margin-based, probabilistic and discriminant classifiers draw boundaries, and how tree ensembles trade interpretability for accuracy.

    Inside this domain

    • Feature Selection and Validation
    • Linear Classifiers
    • Non-Linear Classifiers

    Watch for More trees for correlated trees

    Topics
    3
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  5. Domain 05

    Unsupervised Machine Learning

    Without labels, the questions change: how many groups exist, which variables carry the structure, and how the data itself is generated. This module covers clustering, PCA and generative models, and how to judge results with no ground truth.

    Inside this domain

    • Clustering
    • Principal Component Analysis (PCA)
    • Generative Models

    Watch for K-means by default

    Topics
    3
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  6. Domain 06

    Data Visualization and Communication

    A model nobody understands changes nothing. This module covers how people perceive charts, how to choose encodings and colors that do not mislead, which tools fit which job, and how to build a data story that leads to a decision.

    Inside this domain

    • Effective Data Visualization & Communication
    • Visualization Tools
    • Data Storytelling

    Watch for Pie chart for precise comparison

    Topics
    3
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  7. Domain 07

    Distributed Systems

    This is where big data stops being a buzzword: how cloud and distributed storage spread data across machines, how Apache Spark plans and runs work in parallel, and how a scientist deploys, monitors and scales Spark jobs.

    Inside this domain

    • Distributed Cloud Computing Review
    • Apache Spark Introduction
    • Spark Deployment

    Watch for More executors for skew

    Topics
    3
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  8. Domain 08

    Deep Learning

    Deep learning is the EBDS answer for images, text, audio and other unstructured big data. This module covers how neural networks learn, when they beat classic machine learning, and which architecture fits which data.

    Inside this domain

    • Introduction to Deep Learning
    • Deep Learning Techniques

    Watch for Deep learning on small tabular data

    Topics
    2
    Questions est.
    ~9
    Study time est.
    10–15 hrs
  9. Domain 09

    Applied Data Science

    The final module tests judgment: applying the full EBDS toolkit to Web3 data and industry case studies, and deciding what should be done with data, not only what can be done, through privacy, fairness and accountability.

    Inside this domain

    • Bringing Data Science to Life in Web3
    • Big Data Case Studies
    • Data Ethics

    Watch for Fairness through unawareness

    Topics
    3
    Questions est.
    ~9
    Study time est.
    10–15 hrs

Suggested study order

No official weights are published, so follow the outline order, across about 90–135 hrs of study.

  1. 1 Introduction to the Enterprise Big Data Scientist Certification 10–15 hrs
  2. 2 Statistical Methods for Data Science and Machine Learning 10–15 hrs
  3. 3 Advanced Techniques for Machine Learning 10–15 hrs
  4. 4 Supervised Machine Learning 10–15 hrs
  5. 5 Unsupervised Machine Learning 10–15 hrs
  6. 6 Data Visualization and Communication 10–15 hrs
  7. 7 Distributed Systems 10–15 hrs
  8. 8 Deep Learning 10–15 hrs
  9. 9 Applied Data Science 10–15 hrs

Try one

Practice questionDomain 1: Introduction to the Enterprise Big Data Scientist Certification

An insurer's data team produces three outputs: a monthly claims dashboard, a model that scores each new policy's probability of a claim, and an engine that sets each policy's premium to maximize expected margin within regulatory limits. Which output is prescriptive analytics?

One trap from every domain

The answer that looks right, and the clue that gives it away.

  • Domain 1

    Model equals prescriptive

    Clue Check whether the output recommends what to do or only estimates what will happen.

  • Domain 2

    Ignoring the base rate

    Clue A low prevalence such as 1% or 1 in 1,000 appears in the stem.

  • Domain 3

    Tuning on the test set

    Clue The stem says the test set was used to compare or select configurations.

  • Domain 4

    More trees for correlated trees

    Clue One strong predictor dominates the top split of every tree.

  • Domain 5

    K-means by default

    Clue Irregular or elongated shapes, outliers and an unknown number of clusters.

  • Domain 6

    Pie chart for precise comparison

    Clue Many categories with similar values and a need to compare or rank them.

  • Domain 7

    More executors for skew

    Clue Most tasks in the stage finish quickly while one or a few run far longer.

  • Domain 8

    Deep learning on small tabular data

    Clue Few rows, structured columns, and no images, audio or text.

Other Data & Analytics certifications

Study guides for exams that overlap with EBDS.

Practise what you read

EBDS domains: common questions

How many domains are in the EBDS exam?

EBDS has 9 domains with 27 official topics between them: Introduction to the Enterprise Big Data Scientist Certification, Statistical Methods for Data Science and Machine Learning, Advanced Techniques for Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning, Data Visualization and Communication, Distributed Systems, Deep Learning and Applied Data Science.

What is the EBDS exam format?

Questions: 80. Time allowed: 150 minutes. Passing score: 65% (52 of 80 questions).

How long should I study for EBDS?

Our planning estimate is 90–135 hrs across all domains, split by weight. It is an editorial estimate, not an official figure, so adjust it to your experience and to how you score on practice questions.

Where should I start?

Work through the areas in order, since each builds on the one before. Use the checklist on each page to confirm you can do what it asks before moving on.

Which outline is this guide based on?

This guide follows Enterprise Big Data Scientist course syllabus, 9 modules (current public version; no version number or effective date published, page updated 2025-08), published by Enterprise Big Data Framework Alliance. It was last reviewed in October 2026.

Domain names, weights and topics come from Enterprise Big Data Framework Alliance's official outline (Enterprise Big Data Scientist course syllabus, 9 modules (current public version; no version number or effective date published, page updated 2025-08)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.