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

Learn Python - the most popular programming language and for Data Science and Software Development.

Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

Access and web scrape data using APIs and Python libraries like Beautiful Soup.

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

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.

Describe the Linux architecture and common Linux distributions and update and install software on a Linux system.

Perform common informational, file, content, navigational, compression, and networking commands in Bash shell.

Develop shell scripts using Linux commands, environment variables, pipes, and filters.

Schedule cron jobs in Linux with crontab and explain the cron syntax. 

Describe and contrast Extract, Transform, Load (ETL) processes and Extract, Load, Transform (ELT) processes.

Explain batch vs concurrent modes of execution.

Implement ETL workflow through bash and Python functions.

Describe data pipeline components, processes, tools, and technologies.

Job-ready data warehousing skills in just 6 weeks, supported by practical experience and an IBM credential.

Design and populate a data warehouse, and model and query data using CUBE, ROLLUP, and materialized views.

Identify popular data analytics and business intelligence tools and vendors and create data visualizations using IBM Cognos Analytics.

How to design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.

Create, query, and configure databases and access and build system objects such as tables.

Perform basic database management including backing up and restoring databases as well as managing user roles and permissions. 

Monitor and optimize important aspects of database performance. 

Troubleshoot database issues such as connectivity, login, and configuration and automate functions such as reports, notifications, and alerts. 

Gain real-world, hands-on experience in database administration that you can discuss in interviews and showcase in your portfolio.

Design and implement OLTP and data warehouse schemas, manage data, and perform queries using MySQL and PostgreSQL 

Perform ETL operations, extract data from RDBMS, NoSQL databases, and weblogs, and manage pipelines using Apache Airflow

Apply backup, restore policies, automate the process, and set up user access management policies, optimize SQL queries, and troubleshoot issues

Leverage various generative AI tools and techniques in data engineering processes across industries

Implement various data engineering processes such as data generation, augmentation, and anonymization using generative AI tools

Practice generative AI skills in hands-on labs and projects for data warehouse schema design and infrastructure setup

Evaluate real-world case studies showcasing the successful application of Generative AI for ETL and data repositories