Data Engineering
This foundational course introduces learners to the Linux command line and version control using Git and GitHub. These skills are essential for collaborating on code and managing projects in a data engineering environment.
Program Overview
The Professional Certificate in Data Engineering is a rigorous, 3-month program that builds a complete, project-ready data engineer from the ground up. This industry-focused curriculum starts with foundational tools like Git, Linux, and Python, then expands into data pipelines, SQL, data warehouses, cloud platforms, and modern deployment tools such as Docker, Terraform, and Airflow.
Learners gain hands-on experience building real-world data infrastructure, managing data workflows, and deploying scalable solutions in cloud environments (AWS/GCP). The program emphasizes applied learning with labs, capstone projects, and exposure to the tools and practices used by top data teams.
Core Courses
1. Command Line, Git, and GitHub for Data Engineering
Estimated Learning Hours: 18
Prerequisites: None
Course Summary
This foundational course introduces learners to the Linux command line and version control using Git and GitHub. These skills are essential for collaborating on code and managing projects in a data engineering environment.
Learning Outcomes
- Navigate and manage files using Linux commands
- Use Git for version control and branching workflows
- Collaborate using GitHub (push/pull/merge/PR)
- Understand typical Git workflows used in teams
2. Python Programming for Data Engineering
Estimated Learning Hours: 24
Prerequisites: None
Course Summary
This course covers core Python programming skills with an emphasis on writing clean, reusable, and scalable code for data pipelines and automation.
Learning Outcomes
- Write Python scripts for data manipulation and automation
- Use libraries like pandas, os, requests, and logging
- Work with JSON, CSV, and APIs
- Handle exceptions, logging, and modular code design
3. SQL for Data Analysis and Engineering
Estimated Learning Hours: 24
Prerequisites: None
Course Summary
Students learn how to query, clean, and transform data using SQL. Emphasis is placed on real-world datasets, joins, subqueries, and optimization.
Learning Outcomes
- Write complex SQL queries using joins, subqueries, CTEs
- Clean and transform datasets in relational databases
- Aggregate and summarize business metrics
- Understand database design and indexing basics
4. ETL Pipelines and Data Transformation with Python
Estimated Learning Hours: 18
Prerequisites: Python, SQL
Course Summary
This course focuses on building robust ETL (Extract, Transform, Load) pipelines using Python. Learners automate data ingestion, transformation, and loading from multiple sources.
Learning Outcomes
- Design ETL workflows from scratch
- Use Python to connect to APIs, databases, and files
- Transform messy data into clean formats
- Implement logging, retries, and error handling
5. Data Warehousing and Data Modeling
Estimated Learning Hours: 18
Prerequisites: SQL
Course Summary
Students learn to design and implement data warehouses using dimensional modeling techniques. Real-world schemas are created and deployed to cloud data warehouses like BigQuery or Redshift.
Learning Outcomes
- Design Star and Snowflake schemas
- Build fact and dimension tables
- Load data into cloud data warehouses
- Optimize for analytics and reporting
Course Objectives
- Solidify Python Fundamentals
Master Python’s syntax, core data types, and libraries to effortlessly design and implement AI-driven solutions. - AI-Focused Coding Skills
Develop and automate workflows using Python libraries (like TensorFlow, Keras, or PyTorch) specifically geared toward machine learning and deep learning projects. - Build End-to-End AI Applications
Go beyond coding snippets—gain the expertise to integrate, test, and deploy fully functional AI applications that address real-world challenges. - Optimize Performance & Scalability
Understand the nuances of handling big data, improving model performance, and ensuring your AI solutions can scale alongside your organization’s growth. - Foster Collaborative Innovation
Learn best practices for version control, code reviews, and team-based development, all essential skills in modern tech environments.
Course Learning Outcomes
Upon completing TBS’s AI & Python program, you will be able to:
- Confidently Code in Python
Write clear, efficient, and well-structured Python code that meets professional standards and supports quick adaptation to emerging AI technologies. - Integrate AI Models into Real Projects
Seamlessly incorporate machine learning and deep learning algorithms into Python applications, with a focus on delivering tangible business value. - Automate and Streamline Complex Tasks
Harness Python’s rich ecosystem of libraries to simplify data cleaning, accelerate predictive analytics, and optimize day-to-day operations. - Visualize and Communicate Insights
Present analytical findings effectively using Python’s visualization tools, transforming dense data into compelling narratives for stakeholders. - Strategize AI Deployments
Plan, execute, and manage AI initiatives that align with organizational objectives—becoming the catalyst for innovation in any team you join.
Ready to Transform Your Career?
At TBS, we view data engineering not just as a technical role—but as a critical driver of innovation, scalability, and business intelligence. Enroll in the TBS Data Engineering program to sharpen your analytical edge, streamline data infrastructure, and position yourself as the expert who transforms raw data into strategic insights. Join us at TBS and be prepared to lead the future of data-driven decision-making. Enroll now and accelerate your path to becoming a top-tier data engineering professional.