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 Methodology
This course will be highly technical with group discussions, hands-on practical exercises, and group activities being the core focus.
Course Objectives
By 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
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
Course Outline
Big Data Analytics Use Cases
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
Choosing datasets for course projects
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
Data-warehousing versus Data Mart
Intro to Apache Spark
Big data SQL hands-on-labs
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
Apache Spark data engineering
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
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