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.
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