Skills you'll gain: Communication, Leadership and Management, Agile Software Development, Apache, Budget Management, Computer Architecture, Computer Programming, Creativity, Data Analysis, Data Analysis Software, Data Management, Distributed Computing Architecture, Emotional Intelligence, Entrepreneurship, Finance, Other Programming Languages, Research and Design, Software Engineering
Mixed · Course · 1-3 Months
Skills you'll gain: Operating Systems, Security Engineering, System Security, Computer Architecture, Computer Networking, Data Visualization, Human Computer Interaction, Human Factors (Security), Network Model, Software Architecture, Software Engineering, Software Visualization, Theoretical Computer Science, Network Security
Beginner · Course · 1-3 Months
Skills you'll gain: Communication, Storytelling, Computer Architecture, Computer Networking, Cryptography, Entrepreneurship, Leadership and Management, Market Research, Marketing, Negotiation, Network Architecture, Research and Design, Sales, Security Engineering, Strategy, Strategy and Operations, Theoretical Computer Science, Creativity, Problem Solving, Resilience
Beginner · Course · 1-4 Weeks
Beginner · Course · 1-4 Weeks
Check out these two amazing free data engineering courses from Coursera Data Engineering Career Guide and Interview Preparation and Python Data Processing. These courses will give you a comprehensive introduction to the current data engineering landscape, helping you develop important skills to excel in this field.
If you are a data engineer looking to level-up or a beginner looking to get an introduction to the field, there are a few great resources available. The Introduction To Data Engineering course covers data engineering fundamentals for all stages of the pipeline. The Python and Pandas for Data Engineering course from Duke University provides an overview of Python libraries and data structures used for building and structuring data engineering applications. Alternatively, the Data Engineering and Machine Learning Using Spark course provides an in-depth look at applying Spark for data engineering and machine learning projects. The Python for Applied Data Science and AI course also covers similar topics related to data engineering but also goes into applications in AI and machine learning tasks. Finally, the IoT, Wireless and Cloud Computing course provides a good entry point to data engineering if you're looking to get familiar with those topics.
The GCP Data and Machine Learning Specialization provides a comprehensive understanding of the development and deployment of machine learning models in the cloud. Additionally, the Microsoft Azure Data Engineering Professional Certificate offers foundational cloud knowledge for aspiring data engineers in the workplace. Lastly, the Software Architecture and Big Data Specialization provides cutting-edge courses in distributed systems, engineering big data applications, and designing algorithms.
Data engineering is a subfield of data science responsible for designing, building, and maintaining data infrastructure to collect, process, store, and deliver data so that it can be used and analyzed at scale. Data engineering is extremely important for navigating today’s big data landscape because it enables organizations to generate timely data analysis to guide more effective decision-making.
Data engineers are tasked with the responsibility of preparing massive amounts of data for analysis by data scientists. By using frameworks like Apache Spark to pull data from Hadoop data lakes, data engineers can deliver data for analysis quickly. With the use of machine learning platforms such as TensorFlow, they can train and use neural networks to help decipher unstructured data like video, audio, and image files. And, by using cloud database platforms like Cloudera, data engineers can leverage the power and scalability of cloud-based approaches for their work.
Big data is changing the way we do business and creating a need for data engineers who can collect and manage large quantities of data. Learn more about the role of a data engineer and find out how to become one.
Data engineering is one of the fastest growing careers in tech, and salaries in this field are highly competitive. According to Glassdoor, the average base salary for data engineers is $102,864 per year.
Data engineers are in high demand across many industries, and the nature of their work may vary depending on the size of their company. At small companies, data engineers may be a one-person team, doing everything from data collection to analysis. At mid-sized companies, data engineers lead teams that focus on building data pipelines and data transformation. And, at large companies, data engineers may spend most of their time tuning databases for fast analysis.
Yes! Coursera offers a wide range of online courses and Specializations in data engineering and related topics like machine learning and data science. You’ll be taking these courses from top-ranked institutions and organizations like the University of California San Diego, the University of Colorado, Google Cloud, and IBM, so you don’t have to sacrifice the quality of your education to learn online. Coursera also offers the opportunity to get professional certificates in data engineering and data science from Google Cloud and IBM, so you can continue to add to your credentials on your own flexible schedule.
When starting to learn data engineering, you might need to already have strong experience in working with data projects. A four-year college degree in computer science would be highly beneficial, but more often than not, companies might be more interested in someone who has a strong understanding of the fundamentals of computers, software, coding, and programming languages. You will need to have a comprehension of the data engineering ecosystem, databases, and languages like Python, Sequel, and C. It would also help to possess a keen analytical ability to see through the data weeds to offer some insights and understanding to others in your organization.
The kind of people best suited for work that involves data engineering are often computer programmers who are also analytical self-starters and problem solvers. They are curious people who can look at the big picture and the small details and manage the testing and validation points of the data. Data engineers love working with distributed systems and large sets of data and are able to understand the fundamentals of data technologies, data pipelines, and the computer frameworks that are used to integrate them together. Data engineers might also know the basics of machine learning algorithms, as well as DevOps, DataOps, and other tools to decide how to manage data in production platforms.
You might find that learning data engineering appeals to you if you love data sets, have an interest in quantitative and qualitative sciences, and aspire to be in a high-paying engineering role within a data science team that creates and manages the tech infrastructure of a data platform. You might already be dabbling with data projects on your own. If this is the case, then taking the next step to learn computer programming languages or reading up on machine learning might be a natural evolution. If you'd like a career in a growing field in our world’s technology evolution, then learning data engineering may be the right fit for you.