Big Data Professional

Overview

The Big Data Professional track is comprised of BDSCP Modules 1 and 2, the outlines for which are provided below. Completion of these courses as part of a virtual or on-site workshop results in each participant receiving an official digital Certificate of Completion, as well as a digital Training Badge from Acclaim/Credly.

The Big Data Professional Training 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.

It also explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The course 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 Objectives

A Certified Big Data Professional has a proven understanding of Big Data concepts and technologies, and has further demonstrated proficiency in fundamental areas of Big Data, including analysis, analytics, models and practices.

 

Who Should Attend?

  • Data Analysts
  • Project Managers
  • Business Analysts
  • Digital Marketeers
  • IT Professionals
  • Everyone who wants to learn more about Big Data

 

Exam:

  • BDSCP Exam B90.BDP: Big Data Professional (Partial Version)
    • Achieving a passing grade on the partial version of Exam B90.BDP is equivalent to achieving passing grades on BDSCP Exams B90.01 and B90.02 – which results in the issuance of the Certified Big Data Professional accreditation.

 

Course Outline

Day 1

Module 1

  • 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

 

Day 2

Module 2

  • 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