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

• 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.
  
  
  
  

• 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

• 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