Enterprise Data Engineer (EDE) course is a 13-15 days training program that super-charges Data Engineers with essential technical skills to master the fundamentals to execute any Data Science and AI systems and applications.
Enterprise Data Engineer
Upon completion, participants should be able to demonstrate each of the following:
Learn to design data models, build data warehouses and data lakes, and automate data pipelines
Understand the landscape of machine learning and implement machine learning in business applications
Comprehend the principles, techniques, and practices relevant to JavaScript programming
Reveal the hidden treasures stored in nodes and relationships between databases
Provides sufficient knowledge and skill to the implementation of NoSQL databases
Able to maintain clean, accurate, and structured data for complex data analytics and machine learning applications
Relational Database Design Advanced database management systems and methodologies give you the capability to design and develop real-world database applications in advanced SQL skills.
Intro to Machine Learning
Discover how machine intelligence technology fits into a business's strategic framework and identify a roadmap for integrating advanced analytics, AI and machine learning in your business to boost revenue and improve performance.
No SQL Essentials NoSQL Essentials gives you the capability to design and develop real-world NoSQL databases with full control, and flexibility for agile system development.
Big Data Analytics with Apache Spark
Build and maintain applications with faster startup, better parallelism, and better CPU utilization. Gain an in-depth and comprehensive understanding of big data analytics and AI project from project initiation to project completion.
Graph Databases Master how to create and manipulate graph databases faster and get more powerful analytical operations on data from a network of objects and users.
Extract, Transform, Load (ETL) Upgrade your ability to load data from multiple sources into a cloud data warehouse and transform that data for further analytics. Best way to ensure faster access to large amounts of transformed and integrated data to inform business decision-making.
JavaScript Master JavaScript to reduce system compatibility issues so that you can work with data from multiple sources and run algorithms in a streamlined fashion.
Data Science Development Tools Identify the tools catering to different stages of the data science lifecycle. Master and learn about what each tool is used for, what programming languages they can execute, their features and limitations.
Capstone Learn to meaningfully articulate findings through both narrative and visual strategies. This module will enable participants to drive change through effective communication of data insights.
