Data Engineering
Understand, prepare, and transform data as a core component of business intelligence.
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At a Glance
- Enrollment:
- Open Enrollment
- Length:
- Eight weeks
- Format:
- Online
- Total CEUs:
- 5.4 CEUs
- Investment:
-
$2,800
Upcoming Dates
May Start
July Start
Students may register up to 7 days after the course start.
Learn about the history and principles of database systems and how to clean raw data.
The University of Chicago’s eight-week Data Engineering course will provide you with a technical overview of how to understand, leverage, and report on data. You will be taught how to source, prepare, and analyze historical data.
Designed For
Designed for professionals in associate level, non-technical roles who want to transition into the field of data science, data engineering, and analytics.
Learning objectives for the data engineering course
Often considered the most critical skill for data scientists, data engineering—also known as information engineering—looks at how to collect, analyze, and practically apply 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:
- Build and extract insights from document databases.
- Create reports and dashboards in Tableau using an analytical datastore.
- Develop a coherent, concise, and realistic analysis, and apply your knowledge and understanding of creating an automated end-to-end data pipeline.
Are You Prepared for Effective Reporting?
Learn to design a system used for reporting and data analysis.
I'm InterestedData Engineering Curriculum
Understand how to implement data collection techniques, such as web scraping and survey, and how to populate a graph database and extract insights from graph datasets.
You will learn to:
- Understand databases, data classification, data formats, and data profiles.
- Apply data privacy and security, data ingestion techniques, and data quality and preparation.
- Identify the principles and best practices of relational databases.
- Use SQL, the standard language for database management.
Methodologies and Techniques:
Python
Jupyter
MySQL
mongoDB
Neo4J
OpenRefine
Tableau
Google Cloud
Anaconda
Online course format to become a Big Data Engineer
- Eight weeks in length.
- Weekly, self-paced interactive learning modules and assignments are time-sensitive and should be completed by the set deadlines.
- Synchronous sessions and live question and answer sessions.
- Mentors will provide continuous support and encourage a dynamic and positive learning environment.
Weekly course schedule
Familiarize yourself with the types of data, file formats, data privacy, and security; learn how 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 datastores, relational database concepts, and data model design and implementation.
Acquire knowledge of the basics of SQL and the various types of SQL —how using SQL can manipulate, categorize, and summarize data—and learn to query relational databases for insights.
Explore advanced SQL, including database joins and subqueries; learn how to combine and aggregate data from multiple tables; and connect to a database using multiple clients.
Understand data warehousing and reporting; learn about business intelligence concepts, such as KPIs and metrics; and 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 learn how to manipulate, categorize, and summarize data.
Discover graph databases and applications; and learn to query highly connected data. You will work with the querying language called Cypher, which is 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.
Meet Your Instructors
Abid Ali, PhD
Customer Success Architect, Sigma Computing
Abid Ali has worked in data and analytics for years at major consulting firms, designing and delivering large-scale transformations worldwide in diverse industries. He leads internal initiatives and capabilities and works with C-suite executives to devise strategies for migration and transition to...
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
Data engineering is the fastest-growing occupation in the IT space, and data engineers are prized across industries and in a variety of settings. 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, among the most lucrative data-driven roles.
The average salary for a data engineer in the US
Data engineer's rank first among the fastest-growing jobs in tech
The year-over-year growth in the number of open data engineering positions
Potential Data Engineering job titles
- Big Data Engineer
- Business Intelligence Engineer
- Computer Vision Engineer
- Data Architect
- Machine Learning Engineer
Offered by The University of Chicago's Professional Education
Of Interest
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Learn to design and write high-performing Python code.
