IBM: Data Engineering Capstone Project
This Capstone Project is designed for you to apply and demonstrate your Data Engineering skills and knowledge in SQL, NoSQL, RDBMS, Bash, Python, ETL, Data Warehousing, BI tools and Big Data.
About this course
In this Capstone you’ll demonstrate your ability to perform like a Data Engineer. Your mission is to design, implement, and manage a complete data and analytics platform consisting of relational and non-relational databases, data warehouses, data pipelines, big data processing engines, and Business Intelligence (BI) tools.
This Capstone project will require that you apply and sharpen the skills and knowledge you developed in the various courses in the IBM Data Engineering Professional Certificate and utilize multiple tools and technologies to design databases, collect data from multiple sources, extract, transform and load data into a data warehouse, and utilize a cloud-based BI tool to create analytic reports and visualizations. You will also implement predictive analytics and machine learning models using big data tools and techniques.
This capstone requires significant amount of hands-on lab effort throughout the course. You’ll exhibit your knowledge and proficiency working with Python, Bash scripts, SQL, NoSQL, RDBMSes, ETL, MySQL, PostgreSQL, Db2, MongoDB, Apache Airflow, Apache Spark, and Cognos Analytics.
Upon successfully completing this Capstone, you should have the confidence and portfolio to take on real-world data engineering projects and showcase your abilities to perform as an entry-level data engineer.
At a Glance:
Institution: IBM
Subject: Computer Science
Level: Introductory
Prerequisites:
None
Associated programs:
Professional Certificate in Data Engineering
Language: English
Video Transcript: English
Associated skills:Python (Programming Language), MongoDB, Apache Airflow, Machine Learning, Data Warehousing, Bash (Scripting Language), MySQL, Business Intelligence, Big Data, IBM DB2, Relational Databases, Extract Transform Load (ETL), Relational Database Management Systems, PostgreSQL, NoSQL, Predictive Analytics, SQL (Programming Language), Data Engineering, Data Collection, Apache Spark
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