Big Data & Data Analysis
- 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.
Who should attend?
- 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 PR knowledge includes basic programming experience and analyzing data in python, knowledge of basic database technologies, and awareness of analytics driven business initiatives
Course Outcomes
- 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
- 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
Course Outline
Introduction to Big Data Analytics
- 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 Examples: Netflix, LinkedIn, Facebook, Google, Orbitz, Dell, others…
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
- Data-warehousing versus Data Mart
Computing Big Data
- How to access Big Data
- Role of cloud computing
- Data movement risk
- Networking and colocation
- Big Data Extract, Transform, Load (ETL)Big Data compute technologies
- Hadoop continued
- Map Reduce and beyond
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
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