COURSE ENQUIRY

Big Data Professional – Riyadh

Sunday, Sep 19, 2021 – Monday, Sep 20, 2021

9:00am – 4:00pm

Course Location


Riyadh

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…

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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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