Data engineering is a rapidly growing field, and the Data Engineering Foundations course from IBM equips learners with fundamental skills to kickstart a career in this domain.
The course covers an array of topics including the data engineering ecosystem, Python programming, and relational databases. Through engaging videos and hands-on practice, students will gain a working knowledge of data engineering, enabling them to apply these skills directly to a data career.
Upon completion, learners will have the practical knowledge and experience to progress into more advanced data engineering projects, making it an ideal starting point for those interested in pursuing a career in data engineering.
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This course consists of 5 modules covering various essential skills for data engineering, including data engineering ecosystem, Python programming, relational databases, and SQL for data science with Python.
List basic skills required for an entry-level data engineering role.
Discuss various stages and concepts in the data engineering lifecycle.
Describe data engineering technologies such as Relational Databases, NoSQL Data Stores, and Big Data Engines.
Summarize concepts in data security, governance, and compliance.
Describe Python Basics including Data Types, Expressions, Variables, and Data Structures.
Apply Python programming logic using Branching, Loops, Functions, Objects & Classes.
Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.
Access web data using APIs and web scraping from Python in Jupyter Notebooks.
Demonstrate your skills in Python for working with and manipulating data.
Implement webscraping and use APIs to extract data with Python.
Play the role of a Data Engineer working on a real project to extract, transform, and load data.
Use Jupyter notebooks and IDEs to complete your project.
Describe data, databases, relational databases, and cloud databases.
Describe information and data models, relational databases, and relational model concepts (including schemas and tables).
Explain an Entity Relationship Diagram and design a relational database for a specific use case.
Develop a working knowledge of popular DBMSes including MySQL, PostgreSQL, and IBM DB2.
Analyze data within a database using SQL and Python.
Create a relational database and work with multiple tables using DDL commands.
Construct basic to intermediate level SQL queries using DML commands.
Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.
Aggregate Data in SQL using MySQL Workbench
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