Certified Big Data Scientist

A Certified Big Data Scientist has demonstrated proficiency in the application of principles, processes, and techniques required for exploring and analysing large volumes of complex data with the goals of discovering novel insights, developing data products, and communicating analytic results that can drive decision making.


Along with a firm understanding of fundamental and advanced Big Data concepts and terminologies, a Certified Big Data Scientist is also required to have a thorough understanding of Big Data analysis lifecycle and foundational mechanisms essential for acquiring, processing and storing Big Data datasets. Exploratory data analysis (EDA) and confirmatory data analysis (CDA) techniques, statistical concepts, visualisation tools and machine learning algorithms are taught and assessed in this certification program.

A Certified Big Data Scientist understands the art of model development and evaluation and possesses an in-depth knowledge of both fundamental and advanced analysis techniques required for building descriptive and predictive models.

Note that the Big Data Scientist certification program is based on vendor-neutral coverage of technologies and a broad treatment of various statistical techniques and machine learning algorithms. The attainment of this certification, does not require any knowledge of specific products or the underlying mathematical formulas involved in performing analysis and developing models. This certification imparts the necessary skills and understanding required for successful exploration and interpretation of Big Data datasets. This knowledge establishes a sound foundation that can be further built upon with additional training, accreditation and experience.

  • Exam B90.01: Fundamental Big Data
  • Exam B90.02: Big Data Analysis & Technology Concepts
  • Exam B90.04: Fundamental Big Data Analysis & Science
  • Exam B90.05: Advanced Big Data Analysis & Science
  • Exam B90.06: Big Data Analysis & Science Lab

Big Data

Note that all certification requirements and course contents are reviewed regularly to stay in alignment with industry developments.

Course Objectives

Candidates can expect to gain knowledge and competencies in the following areas upon successful completion of the education and examination related to this certification:

  • Data Science, Data Mining and Data Modeling
  • Big Data Dataset Categories
  • Exploratory Data Analysis (EDA) (including numerical summaries, rules and data reduction)
  • EDA analysis types (including univariate, bivariate and multivariate)
  • Essential Statistics (including variable categories and relevant mathematics)
  • Statistics Analysis (including descriptive, inferential, correlation, covariance and hypothesis testing)
  • Data Munging and Machine Learning, Variables and Basic Mathematical Notations
  • Statistical Measures and Statistical Inference
  • Distributions and Data Processing Techniques
  • Data Discretization, Binning, Clustering
  • Visualization Techniques and Numerical Summaries
  • Statistical Models, Model Evaluation Measures (including cross-validation, bias-variance, confusion matrix & f-score)
  • Machine Learning Algorithms, Pattern Identification (including association rules and apriori algorithm)
  • Advanced Statistical Techniques (including parametric vs. non-parametric, clustering vs. non-clustering distance-based, supervised vs. semi-supervised)

Target Audience

This course delves into a range of advanced data analysis practices and analysis techniques that are explored within the context of Big Data. The course content focuses on topics that enable participants to develop a thorough understanding of statistical, modeling, and analysis techniques for data patterns, clusters, and text analytics, as well as the identification of outliers and errors that affect the significance and accuracy of predictions made on Big Data datasets.


There are no prerequisites for this course. Please note that this is an advanced course which requires an advanced level of mathematical skills.

Exam, Certification & Awards

This course prepares participants for the following five examinations:

  • Exam B90.01: Fundamental Big Data
  • Exam B90.02: Big Data Analysis & Technology Concepts
  • Exam B90.04: Fundamental Big Data Analysis & Science
  • Exam B90.05: Advanced Big Data Analysis & Science
  • Exam B90.06: Big Data Analysis & Science Lab

Only upon successful completion of all three examinations, will you receive the Certified Big Data Science Professional Certification. Every exam is multiple choice and can be taken online or in a Pearson Vue test centre. You will be provided with your exam voucher for the examination upon completion of the course.

Course Material

Participants will receive five official course booklets that are necessary to complete the course (one course booklet per day). The course booklets contain all the necessary study materials, slides and sample questions to adequately prepare for the examination.

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