Data Engineering
Explore industry trends in leveraging data to solve business problems.
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At a Glance
- Enrollment:
- Open Enrollment
- Length:
- 8 weeks
Upcoming Dates
Students may register up to 7 days after the course start.
Learn how to collect, manage, and turn raw data into actionable insights.
The University of Chicago’s eight-week Data Engineering course provides a technical overview of data collection, storage, analysis, and usage. You will be taught how to source, prepare, and manage historical data. You will also learn about the history and principles of database systems, how to clean raw data, and how to use SQL to load and query data in databases.
Designed For
Designed for professionals in associate-level, non-technical roles who want to transition into data science, data engineering, and analytics.
Learning Objectives to Become a Data Engineer
Often considered the most critical skill for data scientists, data engineering—also known as information engineering—involves collecting, analyzing, and practically applying data. Expertise in data engineering is required across industries, and professionals equipped with this unique technical knowledge are an asset to any organization.
After completing the course, you will be able to:
- Understand databases and data classification, formats, and profiles.
- Apply data privacy and security, ingestion, and quality and preparation techniques.
- Explore NoSQL database types, supported formats, and data models using the MongoDB application.
- Implement data cleaning and validation techniques that ensure information reaches users properly for exploitation.
- Earn a credential certifying completion from the University of Chicago and become part of the UChicago network.
Are You Prepared for Effective Reporting?
Learn to design a system used for reporting and data analysis.
Enroll NowData Engineering Curriculum
You will learn to:
- Build and extract insights from document databases.
- Create reports and dashboards in Tableau using an analytical data store.
- Identify the principles and best practices of relational databases.
- Use SQL, the standard language for database management.
Methodologies and Techniques:
Anaconda
Google Cloud
Jupyter
mongoDB
MySQL
Neo4J
OpenRefine
Python
Tableau
Online Format Features
- Self-paced interactive learning modules with a variety of engaging learning activities, assignments, and resources.
- Live sessions that bring you, your peers, and your instructor together to learn collaboratively about the current state of the field, engage with real-world problems, and explore authentic solutions.
- Continuous support from your instructional assistant, who will accompany you on your journey through the content, answer your questions, and provide feedback on your work.
Weekly Course Schedule
Familiarize yourself with database languages, systems, and tools, including Python and Jupyter Notebook.
Explore data types, file formats, data privacy, and security, learn to collect and transform data, and discover the differences between on-premise and cloud platforms.
Learn to use database management systems and discover the different types of databases and data stores, relational database concepts, and data model design and implementation.
Acquire knowledge of SQL basics and types, discover how using SQL can transform, categorize, and summarize data, and learn to query relational databases for insights.
Explore advanced SQL, including database joins and subqueries, and learn how to combine and aggregate data from multiple tables and connect to a database using multiple clients.
Understand data warehousing and reporting, explore business intelligence concepts, such as KPIs and metrics, and discover how to use a dimensional data model for reporting.
Learn how to derive insights from semi-structured and unstructured data using NoSQL databases, document databases and applications, and how to manipulate, categorize, and summarize data.
Discover graph databases and applications and learn to query highly connected data using Cypher, the Neo4j query language.
Work on a practical business case, design an end-to-end data pipeline, and master reporting and visualization to provide actionable recommendations.
Earn a Credential in Data Engineering
After successful completion of this course, participants will receive credentials certified by the University of Chicago including a digital badge to recognize their achievement.
Meet Your Instructor
Our highly trained instructors are courageous thinkers and passionate leaders who leverage years of industry expertise and up-to-date knowledge of terminology, tools, and trends to deliver an unparalleled learning experience. Through their rigorous discourse, cross-disciplinary collaboration, and field-shaping contributions, they create practical solutions and pioneering innovations that enrich our world.
These instructors teach this course regularly. Please speak to your enrollment advisor if you wish to know who the current teacher is.
Abid Ali, PhD
Customer Success Architect, Sigma Computing
Abid Ali has spent a large part of his career working in data and analytics at major consulting firms, designing and delivering large-scale transformations worldwide across industries. He leads internal initiatives and capabilities and works with C-suite executives to devise strategies for migration...
Abhishek Chaturvedi, MSc
Data Scientist at Amazon
Abhishek Chaturvedi is a data science and product management professional dedicated to a wide range of sectors and functions. He has experience applying his expertise to marketing, people analytics, strategic planning, and technology R&D across the aerospace, defense, financial services, retail, and...
Career Outlook
In charge of building and maintaining an organization’s data infrastructure from databases and data warehouses to data pipelines, data engineers identify trends in data sets—a skill essential to managing and converting data into the information data scientists and business analysts need to drive results. Data engineering is a broad field with applications in practically every industry. As long as there is data—and the quantities are increasing every minute—data engineers will be in demand. A career in data engineering can be both challenging and rewarding, and, with the right skill set, is among the most lucrative data-driven roles.
The average salary for a data engineer in the United States.
Data engineer's rank first among the fastest-growing tech jobs.
The year-over-year growth in the number of open data engineering positions.
Potential Job Titles for Professionals with Data Engineering Skills
- Big Data Engineer
- Business Intelligence Engineer
- Computer Vision Engineer
- Data Architect
- Machine Learning Engineer
How Do I Get Started?
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Complete the form on the registration page.
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Pay the tuition fee through our secure gateway.
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Receive a welcome email with your login information for the virtual campus.
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Gain access to the course content prior to the start date.
Offered by The University of Chicago's Professional Education
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