Global Learning for Operational Leaders
Classroom
15 sessions
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Online / Live
15 sessions
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Introduction
Course overview
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 store, process, analyze and present big data use cases within their organizations. This course provides a multitude of hands-on labs with Spark, a key big data technology 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 work through advanced machine learning and deep learning use cases.
This is our most advanced course in our big data series following Certified Big Data and Data Analytics Practitioner (CBDDAP) and Certificate in Big Data Fundamentals (CBDF). Participants will aim to identify areas within their organization that can be improved through big data use cases, and work on an individual chosen data project during the course. By the end of the course, participants will be able to work through multiple methods and practical approaches to leverage Spark for advanced big data analytics.
Course MethodologyThis course will be highly technical with group discussions, hands-on practical exercises, and group activities being the core focus.
Course ObjectivesBy the end of the course, participants will be able to:
- Understand key big data technologies, including a deep dive into Apache Spark
- Describe the main challenges and advantages of Hadoop map-reduce
- Demonstrate and discuss key technologies for big data storage and compute, such as PostgreSQL and object storage
- Discuss popular machine learning algorithms, deep learning techniques and the importance of ethics in data analytics and artificial intelligence
- Deliver a presentation demonstrating the analytics lifecycle and Spark
This is an advanced level course. It is expected that participants either have a number of years of experience utilizing big data, or have previously attended the Certified Big Data and Data Analytics Practitioner (CBDDAP) course. This course is ideal for data engineers, AI engineers and data scientists. Recommended pre-knowledge includes some python programming experience and data visualization practice.
Target Competencies- Big data utilization
- Big data analytics structures and technologies
- Ethics and integrity for big data and AI development
- Big data storage
- Apache Spark best practices
What you will achieve
Learning objectives
- Understand key big data technologies, including a deep dive into Apache Spark
- Describe the main challenges and advantages of Hadoop map-reduce
- Demonstrate and discuss key technologies for big data storage and compute, such as PostgreSQL and object storage
- Discuss popular machine learning algorithms, deep learning techniques and the importance of ethics in data analytics and artificial intelligence
- Deliver a presentation demonstrating the analytics lifecycle and Spark
Who should attend
Target audience
- This is an advanced level course. It is expected that participants either have a number of years of experience utilizing big data, or have previously attended the Certified Big Data and Data Analytics Practitioner (CBDDAP) course. This course is ideal for data engineers, AI engineers and data scientists. Recommended pre-knowledge includes some python programming experience and data visualization practice.
- Target Competencies
- Big data utilization
- Big data analytics structures and technologies
- Ethics and integrity for big data and AI development
- Big data storage
- Apache Spark best practices
Methodology
Learning approach
- This course will be highly technical with group discussions, hands-on practical exercises, and group activities being the core focus.
Course content
Course outline and key learning areas
Module 1
Big Data Analytics Use Cases
- How can big data projects meet organizational needs
- Big data examples:
- Netflix
- Orbitz
- Dell
- Others
- Best practices in project design
Module 2
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 NoSQL DBs
- PostgreSQL
- MongoDB
- Cassandra
- Streaming data
Module 3
Computing Big Data
- How to access big data
- Role of cloud computing
- Data movement risk
- Networking and co-location
- Apache Spark lab
- Big data extract, transform, load (ETL) big data compute technologies
- Distributed compute
- High performance clusters vs Apache Spark
- Streaming: Storm, Spark structured streaming
- Apache Spark ETL labs
Module 4
Big Data Advanced Analytics and AI
- Analytics Lifecycle
- Apache Spark vs Pandas
- Big data machine learning & deep learning in Spark
- Importance of ethics in AI
- Automl & Hyperparameter tuning
Module 5
Course Big Data Projects
- Identify analytical opportunities in an organization
- Define and assess the problem
- Describe the impact and use of data to address the problem
- Identify potential data sources
- Design a data analytics project
- Access, explore, analyze and visualize chosen dataset for project
- Present project insights in course
FAQ
Frequently asked questions
What does Certificate in Advanced Big Data and Data Analytics (CABDDA) (DMBI-011) cover?
This course covers Data Management and Business Intelligence through a structured five-day outline focused on practical application, discussion, and implementation planning.
When is the next available session?
The next scheduled session starts on 19 - 23 October 2026, with additional classroom dates and mirrored Online / Live options listed in the course schedules section.
Who should attend this course?
This is an advanced level course. It is expected that participants either have a number of years of experience utilizing big data, or have previously attended the Certified Big Data and Data Analytics Practitioner (CBDDAP) course. This course is ideal for data engineers, AI engineers and data scientists. Recommended pre-knowledge includes some python programming experience and data visualization practice., Target Competencies, Big data utilization
Is bilingual course delivery available?
Yes. All course materials and presentations are provided in English, while bilingual explanation and discussion support may be available depending on the selected location and trainer availability. For example, when attending a course in Istanbul, you may request a Turkish-speaking trainer to explain the English course content in Turkish. Arabic explanation support may also be requested at this location. Other languages are available in selected cities. You can choose your preferred language support as an optional preference during registration.
How can I register for a session?
Use any Register button next to the available course dates to open the participant registration page and submit your booking request for the selected session.
Is this course available online as well as classroom-based?
Yes. The course detail page includes both classroom sessions and Online / Live sessions, with online options aligned to the same course dates for easier planning.
Where are classroom sessions delivered?
Current classroom venues include London, Munich, Amsterdam, Barcelona, Frankfurt, Rome, Kuala lumpur.
How long is the training programme?
The programme runs for five days, with up to five training hours per day, from 9:00 AM to 2:00 PM. Each programme includes a total of 25 accredited training hours.
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