Professional Big Data and Data Analytics Practitioner (PBDDAP)

Why Attend

Big data is a change agent that challenges the ways in which organizational leaders have traditionally made decisions. This course provides participants with the confidence to articulate big data architectures to support analytics driven solutions within their organizations. This course provides hands on experience with key big data technologies used to solve data intensive problems. Participants will gain the knowledge and skills they need to assemble and manage a large-scale big data analytics project. Lastly, participants will receive a conceptual introduction to the data structures that support machine learning algorithms and artificial intelligence use cases.

Participants will work to identify areas within their organization that can be improved through big data-driven implementations, and the types of improvements that can be made through analytical processes. Participants will be led through a series of hands-on exercises and workshops, where they will have the chance to apply the test methods and practical approaches that they are learning throughout the course. At the end of the course, participants will produce an actionable big data plan and architectural diagram to be used as a blueprint proposal within their own organization.

Overview

Course Outline

Schedule & Fees

Course Methodology

This course will be highly interactive with group discussions, case studies, hands-on practical exercises, and group activities being the core focus.

Course Objectives

By the end of the course, participants will be able to:

Design big data implementation plans and create strategies for data driven solutions
Explain the challenges of big data and traditional technologies like Excel
Discuss the main challenges and advantages of Hadoop ecosystem and other big data distributed architectures
Demonstrate and discuss key technologies for big data storage and compute, such as Postgre SQL and Mongo DB
Discuss popular machine learning algorithms and the importance of ethics in data analytics and artificial intelligence
Deliver an architectural diagram for analytics focused use cases

Target Audience

This course is ideal for data analysts, data engineers, data scientists, as well as technically-inclined management and administrative professionals seeking to understand big data strategies, technologies and use cases. Recommended pre-knowledge includes basic programming experience and analyzing data in python, knowledge of basic database technologies, and awareness of analytics driven business initiatives.

Target Competencies

Big data hands-on labs
Big Data analytics structures and technologies
Ethics and integrity for big data analytics
Big data storage and computer system implementation
Architecture diagram design

Introduction to Big Data Analytics

What is Big Data?

5 “V’s” of big data
How big data relates to data analytics
Big data impact on technologies
Open source revolution

Key big data concepts and data types

Text, audio, images

Big data professional roles
How can big data projects meet organizational needs
Big data Examples:

Netflix, LinkedIn, Facebook, Google, Orbitz, Dell, others.

Best practices in project design
Assessing the current state of your organization

Storing Big Data

Big data architectures and paradigms

The Hadoop Ecosystem

Overview of Hadoop
Hadoop Distributed File System (HDFS)

Massively parallel processing (MPP) versus distributed in-memory applications
RDBMSs vs No SQL DBs

Postgre SQL, Mongo DB, Cassandra

Streaming data

Data-warehousing versus Data Mart

Computing Big Data

How to access big data

Role of cloud computing
Data movement risk
Networking and co-location

Big data extract, transform, load (ETL)
Big data compute technologies

Hadoop continued

Map Reduce and beyond

Distributed compute
High performance clusters
Spark
Streaming: Storm, Spark structured streaming
Other big data technologies: Kafka, etc.

Cloud applications for big data

Big Data Projects

Basics of data analytics

Roles and objectives
Key math and statistics concepts
Supervised versus Unsupervised
Key technologies and applications

Getting value out of Big Data
5 P’s of data science
Importance of ethics
Programmability

Architecting Big Data Solutions

Identify analytical opportunities

Define and assess the problem
Describe the impact and use of data to address the problem
Identify potential data sources
Brainstorm an analytics strategy to implement

Storage and compute

Identify a cloud environment strategy
Brainstorm key storage systems and computer environments

Kuala Lumpur

Kuala Lumpur (Malaysia)2025-10-20$3775

Manila

Manila (Philippines)2026-05-18$3775