Big Data Professional | JODAYN

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

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