Course

Course Summary
Credit Type:
Course
ACE ID:
IBM-0026
Organization:
IBM
Location:
Online
Length:
20 weeks (200 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 Introduction to Programming with Python
Lower-Division Baccalaureate 3 Introduction to Relational Databases
Upper-Division Baccalaureate 3 Advanced SQL Programming
Upper-Division Baccalaureate 3 Practical Data Mining and Warehouse Implementation
Upper-Division Baccalaureate 3 Data Engineering Techniques and Tools
Description

Objective:

The course objective is to prepare for a career in data engineering. By receiving professional-level training from IBM, students will be able to demonstrate proficiency in portfolio-ready projects; earn an employer-recognized certificate from IBM; and qualify for in-demand job titles: database engineer, data engineer, and junior data engineer.

Learning Outcomes:

  • Create, design, and manage relational databases
  • Develop and execute SQL queries using SELECT, INSERT, UPDATE, DELETE statements, database functions, stored procedures, Nested Queries, and JOINs
  • Demonstrate working knowledge of NoSQL and Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, Spark Streaming
  • Implement ETL and Data Pipelines with Bash, Airflow and Kafka
  • Apply database administration (DBA) concepts to RDBMSes such as MySQL, PostgreSQL, and IBM Db2
  • Architect, populate, deploy Data Warehouses
  • Create BI reports and interactive dashboards

General Topics:

  • Introduction to Data Engineering
  • Python for Data Science, AI and Development
  • Python Project for Data Engineering
  • Introduction to Relational Databases (RDBMS)
  • Databases and SQL for Data Science with Python
  • Hands-on Introduction to Linux Commands and Shell Scripting
  • Relational Database Administration (DBA)
  • ETL and Data Pipelines with Shell, Airflow and Kafka
  • Getting Started with Data Warehousing and BI Analytics
  • Introduction to NoSQL Databases
  • Introduction to Big Data with Spark and Hadoop
  • Data Engineering and Machine Learning using Spark
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Computer Based Training
  • Discussion
  • Lectures
  • Practical Exercises
  • Programming Assignments

Methods of Assessment:

  • Quizzes

Minimum Passing Score:

70%
Supplemental Materials