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Complete the Big Data Analysis & Advanced Data Science course and, optionally, get accredited as a Certified Big Data Scientist by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Because this course encompasses both the Big Data Professional and Big Data Scientist certifications, upon passing the exam you will also receive official Big Data Professional and Big Data Scientist digital accreditation certificates and certification badges from Acclaim/Credly, along with an account that can be used to verify your certification status.

If you already completed the Big Data Professional course modules, you can purchase a partial course (or a partial bundle) with only the modules specific to the Big Data Scientist track here.

The Big Data Analysis & Advanced Data Science course is comprised of the following 5 course modules, each of which has an estimated completion time of 10 hours:

  • Module 1: Fundamental Big Data Science & Analytics
  • Module 2: Big Data Analysis & Technology Concepts
  • Module 4: Big Data Analysis & Science
  • Module 5: Advanced Big Data Analysis & Science
  • Module 6: Big Data Analysis & Science Lab

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.
  1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
  2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
  3. For your reference: A set of printable PDF documents that you can keep (for all course workbooks and posters).
All three forms of access are subject to Arcitura’s *. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

Shown below are the digital contents and the topic outline for each course module:

Module 1: Fundamental Big Data Science & Analytics

This foundational course module provides a high-level overview of essential Big Data topic areas. A basic understanding of Big Data from business and technology perspectives is provided, along with an overview of common benefits, challenges, and adoption issues. The module content is divided into a series of modular sections, each of which is accompanied by one or more hands-on exercises.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster
  • Symbol Legend Poster

  • Patterns and Mechanisms Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Understanding Big Data
  • Fundamental Big Data Terminology and Concepts
  • Big Data Business Drivers and Technology Drivers
  • Traditional Enterprise Technologies Related to Big Data
  • OLTP, OLAP, ETL and Data Warehouses in relation to Big Data
  • Characteristics of Data in Big Data Environments
  • Dataset Types in Big Data Environments
  • Structured, Unstructured and Semi-Structured Data

  • Metadata and Data Veracity
  • Fundamental Analysis and Analytics
  • Quantitative and Qualitative Analysis
  • Machine Learning Types
  • Descriptive and Diagnostic Analytics
  • Predictive and Prescriptive Analytics
  • Business Intelligence and Big Data
  • Data Visualization and Big Data
  • Big Data Adoption and Planning Considerations

Module 2: Big Data Analysis & Technology Concepts

This course module explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The module content intentionally keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions, as well as a high-level understanding of the back-end components that enable these functions.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Big Data Analysis Lifecycle (from Business Case Evaluation to Data Analysis and Visualization)
  • A/B Testing and Correlation
  • Regression and Heat Maps
  • Time Series Analysis
  • Network Analysis and Spatial Data Analysis
  • Classification and Clustering
  • Filtering, including Collaborative Filtering and Content-based Filtering
  • Sentiment Analysis and Text Analytics

  • Clusters and Processing Batch and Transactional Workloads
  • How Cloud Computing relates to Big Data
  • Foundational Big Data Technology Mechanisms
  • Big Data Storage Devices and Processing Engines
  • Resource Managers, Data Transfer Engines and Query Engines
  • Analytics Engines, Workflow Engines and Coordinate Engines

Module 4: Big Data Analysis & Science

This course module provides an in-depth overview of essential topic areas pertaining to data science and analysis techniques relevant and unique to big data with an emphasis on how analysis and analytics need to be carried out individually and collectively in support of the distinct characteristics, requirements and challenges associated with big data datasets.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Data Science, Data Mining & Data Modeling
  • Big Data Dataset Categories
  • High-Volume, High-Velocity, High-Variety, High-Veracity, High-Value Datasets
  • Exploratory Data Analysis (EDA)
  • EDA 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, Covariance, Hypothesis Testing, etc.
  • Measures of Variation or Dispersion, Interquartile Range & Outliers, Z-Score, etc.
  • Probability, Frequency, Statistical Estimators, Confidence Interval, etc.
  • Data Munging and Machine Learning

  • Variables and Basic Mathematical Notations
  • Statistical Measures and Statistical Inference
  • Confirmatory Data Analysis (CDA)
  • CDA Hypothesis Testing, Null Hypothesis, Alternative Hypothesis, Statistical Significance, etc.
  • Distributions and Data Processing Techniques
  • Data Discretization, Binning and Clustering
  • Visualization Techniques, including Bar Graph, Line Graph, Histogram, Frequency Polygons, etc.
  • Prediction Linear Regression, Mean Squared Error and Coefficient of Determination R2, etc.
  • Clustering k-means, Cluster Distortion, Missing Feature Values, etc.
  • Numerical Summaries

Module 5: Advanced Big Data Analysis & Science

This course module delves into a range of advanced data analysis practices and analysis techniques that are explored within the context of big data. The module 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.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Modeling, Model Evaluation, Model Fitting and Model Overfitting
  • Statistical Models, Model Evaluation Measures
  • Cross-Validation, Bias-Variance, Confusion Matrix and F-Score
  • Machine Learning Algorithms and Pattern Identification
  • Association Rules and Apriori Algorithm
  • Data Reduction, Dimensionality Feature Selection
  • Feature Extraction, Data Discretization (Binning and Clustering)
  • Advanced Statistical Techniques
  • Parametric vs. Non-Parametric, Clustering vs. Non-Clustering
  • Distance-Based, Supervised vs. Semi-Supervised
  • Linear Regression and Logistic Regression for Big Data

  • Classification Rules for Big Data
  • Logistics Regression, Naïve Bayes, Laplace Smoothing, etc.
  • Decision Trees for Big Data
  • Tree Pruning, Feature Splitting, One Rule (1R) Algorithm
  • Pattern Identification, Association Rules, Apriori Algorithm
  • Time Series Analysis, Trend, Seasonality
  • K Nearest Neighbor (kNN), K-means
  • Text Analytics for Big Data
  • Bag of Words, Term Frequency, Inverse Document Frequency, Cosine Distance, etc.
  • Outlier Detection for Big Data
  • Statistical, Distance-Based, Supervised and Semi-Supervised Techniques

Module 6: Big Data Analysis & Science Lab

This course module covers a series of exercises and problems designed to test the participant’s ability to apply knowledge of topics covered previously in module modules 4 and 5. Completing this lab will help highlight areas that require further attention, and will further prove proficiency in big data analysis and science practices as they are applied and combined to solve real-world problems.

Course Module Contents

  • Lab Exercise Booklet
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Exercise Booklet and Poster (printable)

Topics Covered

  • Reading Exercise 6.1: TMC Case Study Background
  • Lab Exercise 6.2: Analysis for Enhancing Product Quality
  • Lab Exercise 6.3: Analysis for Lowering Total Cost of Ownership
  • Reading Exercise 6.4: PLGM Case Study Background
  • Lab Exercise 6.5: Analysis for High-Yield Marketing Plan

  • Lab Exercise 6.6: Analyze Items Layout and Credit Card Data
  • Reading Exercise 6.7: LHL Case Study Background
  • Lab Exercise 6.8: Enhance Patient Diagnosis Capability
  • Reading Exercise 6.9: SWP Case Study Background
  • Lab Exercise 6.10: Enhance Risk Management and Understand Demand Patterns

Learn About Arcitura: Take the Video Tour

Watch these helpful informational videos to learn about Arcitura programs, courses and certifications.

About Arcitura

About Arcitura Courses

About Arcitura Certifications

What’s in an Arcitura Course


Each course provides a comprehensive curriculum with 2-8 modules and 20-80 hours of training.

More Than Just
Video Lessons

In addition to standard video lessons, courses include full-color workbooks and reference posters for all lessons.

Interactive & Graded

Courses also include interactive and graded exercises, interactive and graded self-tests and other supplements.

The Arcitura Difference


  • is authored by a dedicated courseware development team
  • has a self-test, accreditation exam and professional certification
  • is available via two different eLearning platforms


  • undergo a common development process
  • are authored to be consistent in quality, structure and style
  • share a common vocabulary and symbol notation
  • are authored in collaboration with subject matter experts

Take Your Skills Anywhere

Regardless of whether you are an individual looking to boost your career or an organization looking to up-skill a team, Arcitura courses and certifications provide a sound investment.

Because both courses and accreditations are vendor-neutral, they empower you with skills and credentials that you can take to wherever you need to go.

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