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Note: This new course is coming soon and is currently available for pre-order.

The Data Science Governance course builds knowledge and skills in Data Science Governance, focusing on machine learning, AI and big data solutions. It covers governance concepts, risks, challenges and key roles, and further explores the analytics pipeline governance lifecycle, detailing over 70 precepts and processes. The course also describes over 80 additional precepts and processes in analytics platform, machine learning and AI pipeline governance, mapping relevant roles to each stage.

Complete the Data Science Governance course and, optionally, get accredited as a Certified Data Science Governance Specialist 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 Data Science Governance Specialist certifications, upon passing the exam you will also receive official Big Data Professional and Data Science Governance Specialist 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 Data Science Governance Specialist track here.

The Data Science Governance 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 17: Fundamental Data Science Governance
  • Module 18: Advanced Data Science Governance
  • Module 19: Data Science Governance 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.

Exam Details

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 watermarked 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.

The course is comprised of a set of modules. Each module has a set of lessons and is further supplemented with exercises to help reinforce your understanding of key topics. 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)
  • Interactive Exercises
  • 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)
  • Interactive Exercises

  • 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 17: Fundamental Data Science Governance

This course module explores introductory topics pertaining to the field of developing data processing solutions–data engineering–in the context of Big Data environments. Specifically it covers concepts, techniques and technologies related to the processing and storage of Big Data datasets including MapReduce and NoSQL. It highlights the unique challenges faced when processing and storing Big Data datasets. The MapReduce data processing engine, which is the de facto framework for batch processing of large amounts of data, is also explained in detail.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Interactive Exercises
  • Mind Map Poster

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

Topics Covered

  • Big Data Engineering – Big Data Engineering Challenges
  • Big Data Storage Terminologies (including sharding, replication, CAP theorem, ACID, BASE)
  • Big Data Storage Requirements
  • On-Disk Storage (including distributed file system – databases)
  • Introduction to NoSQL – NewSQL
  • NoSQL Rationale – Characteristics

  • NoSQL Database Types (including key-value, document, column-family and graph databases)
  • Big Data Processing Requirements
  • Big Data Processing (including batch mode and realtime mode)
  • Introduction to MapReduce for Big Data Processing (batch mode)
  • MapReduce Explained (including map, combine, partition, shuffle and sort, and reduce)

Module 18: Advanced Data Science Governance

This course module builds upon Module 17 by exploring advanced topics pertaining to the storage and processing of Big Data datasets. Specifically it covers advanced Big Data engineering mechanisms, in-memory data storage and realtime data processing. It presents further considerations for developing MapReduce algorithms and also introduces the Bulk Synchronous Parallel (BSP) processing engine, along with a discussion of graph data processing. The Big Data mechanisms required for developing Big Data pipelines, its stages and the design process involved in developing Big Data processing solutions are also explored.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Interactive Exercises
  • Mind Map Poster

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

Topics Covered

  • Advanced Big Data Engineering Mechanisms (including serialization & compression engines)
  • In-Memory Storage Devices, In-Memory Data Grids & In-Memory Databases
  • Read-Through, Read-Ahead, Write-Through & Write-Behind Integration Approaches
  • Polyglot Persistence (including Explanation, Issues & Recommendations)
  • Realtime Big Data Processing Concepts (including Speed Consistency Volume (SCV), Event Stream Processing (ESP) & Complex Event Processing (CEP))

  • General Realtime Big Data Processing & Realtime Big Data Processing & MapReduce
  • Advanced MapReduce Algorithm Design
  • Bulk Synchronous Parallel (BSP) Processing Engine & BSP vs. MapReduce
  • Graph Data & Graph Data Processing using BSP
  • Big Data Pipelines (including Definition and Stages)
  • Big Data with Extract-Load-Transform (ELT)
  • Big Data Solutions (including Characteristics, Design Considerations & Design Process)

Module 19: Data Science Governance 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 course modules 17 and 18. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data engineering 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)

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

Comprehensive
Coverage

Each course provides a comprehensive curriculum with 2-3 modules and 20-40 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
Challenges

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

The Arcitura Difference

EACH COURSE

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

ALL COURSES

  • 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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