Kompetenzen, die Sie erwerben: Data Management, Databases, Data Architecture, Data Structures, Big Data, Database Theory, SQL, Apache, Database Administration, Extract, Transform, Load, Python Programming, Data Model, Database Application, Data Warehousing, Data Analysis, NoSQL, Data Engineering, Distributed Computing Architecture, Database Design, Operating Systems, System Programming, System Software, Programming Principles, Statistical Programming, Algebra, Computer Architecture, PostgreSQL, Applied Machine Learning, Correlation And Dependence, Feature Engineering, General Statistics, Graph Theory, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Regression, Statistical Analysis, Statistical Machine Learning, Data Visualization, Data Visualization Software, Basic Descriptive Statistics, Exploratory Data Analysis, Cloud Applications, Cloud Computing, Data Science, DevOps, Kubernetes, Leadership and Management, Network Architecture, Network Security, Other Programming Languages, Professional Development, Security Engineering, Algorithms, Computational Logic, Computational Thinking, Computer Networking, Computer Programming, Computer Programming Tools, IBM Cloud, Linux, Mathematical Theory & Analysis, Mathematics, Microarchitecture, Project Management, Security Strategy, Software Architecture, Software Engineering, Strategy and Operations, Theoretical Computer Science
Beginner · Professional Certificate · 3-6 Months
Kompetenzen, die Sie erwerben: Data Structures, Data Management, Databases, SQL, Python Programming, Database Theory, Data Engineering, Data Analysis, Data Model, Database Application, Programming Principles, Extract, Transform, Load, Data Architecture, Algebra, Database Administration, Database Design, PostgreSQL, Statistical Programming, Basic Descriptive Statistics, Exploratory Data Analysis, Apache, Big Data, Data Science, Data Warehousing, Leadership and Management, Network Security, Professional Development, Security Engineering, Computational Logic, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Distributed Computing Architecture, Mathematical Theory & Analysis, Mathematics, NoSQL, Project Management, Security Strategy, Strategy and Operations, Theoretical Computer Science
Beginner · Specialization · 3-6 Months
Kompetenzen, die Sie erwerben: Computer Programming, Statistical Programming, Python Programming, Theoretical Computer Science, Data Management, Cloud Computing, Software Engineering, Databases, Software Architecture, Operating Systems, Human Computer Interaction, User Experience, Computer Programming Tools, Amazon Web Services, Linux, Extract, Transform, Load, Data Structures, SQL, Cloud Platforms, Google Cloud Platform, Machine Learning, Systems Design, Apache, Application Development, Big Data, Data Analysis, Data Engineering, DevOps, Programming Principles, Software Engineering Tools, Database Administration, Database Application, Applied Machine Learning, Cloud Applications, Cloud Engineering, Machine Learning Algorithms, Data Mining
Beginner · Specialization · 3-6 Months
Kompetenzen, die Sie erwerben: Data Engineering, Data Management, Extract, Transform, Load, Databases, Apache, Big Data, Data Analysis, Data Architecture, Data Warehousing, Leadership and Management, Network Security, Professional Development, SQL, Security Engineering, Computer Architecture, Computer Networking, Data Science, Database Administration, Distributed Computing Architecture, NoSQL, Project Management, Security Strategy, Statistical Programming, Strategy and Operations
Beginner · Course · 1-4 Weeks
Kompetenzen, die Sie erwerben: Cloud Computing, Microsoft Azure, Data Management, Big Data, Extract, Transform, Load, Data Warehousing, Cloud Storage, Databases, Data Analysis, Computer Networking, Statistical Programming, Apache, Cloud Infrastructure, Computer Architecture, Network Architecture, SQL, Accounting, Business Analysis, Financial Analysis, Computer Programming, Continuous Delivery, Continuous Integration, DevOps, NoSQL, Business Psychology, Entrepreneurship, Leadership and Management, Organizational Development, Application Development, Data Architecture, Network Security, Security Engineering, Security Strategy, Software Engineering, Software Engineering Tools
Intermediate · Professional Certificate · 3-6 Months
Kompetenzen, die Sie erwerben: Cloud Computing, Data Management, Computer Architecture, Cloud Platforms, Google Cloud Platform, Big Data, Distributed Computing Architecture, Machine Learning, SQL, Apache, Data Science, Hardware Design, Extract, Transform, Load, Cloud Storage, Full-Stack Web Development, Web Development, Databases, Information Technology, Python Programming, Statistical Programming, Computer Networking, Computer Programming, Data Analysis, Data Analysis Software, Data Visualization, Data Warehousing, Database Administration, Database Application, Database Theory, Kubernetes, Network Architecture, Operating Systems, Software Framework, System Programming, Theoretical Computer Science, Applied Machine Learning, Bayesian Statistics, Business Psychology, Cloud Applications, Cloud Infrastructure, Computational Thinking, Data Architecture, Data Model, Data Structures, Deep Learning, Entrepreneurship, Exploratory Data Analysis, Feature Engineering, General Statistics, Machine Learning Algorithms, Machine Learning Software, Probability & Statistics, Tensorflow
Intermediate · Specialization · 3-6 Months
Kompetenzen, die Sie erwerben: SQL, Statistical Programming, Databases, Computer Programming, Data Management, Python Programming, Programming Principles, Software Engineering, Software Engineering Tools, Database Application, Computer Programming Tools, Theoretical Computer Science, Algorithms, Data Analysis, Data Model, Data Structures, Data Visualization, Extract, Transform, Load, Research and Design, Database Design, Database Administration, Big Data, Business Analysis, Calculus, Data Visualization Software, Data Warehousing, Entrepreneurship, Market Research, Mathematics, Operations Research, Strategy and Operations, Algebra, Application Development, Communication, Computational Thinking, Computer Science, Database Theory, Django (Web Framework), Linux, Project Management, Tableau Software, Web Development
Beginner · Professional Certificate · 3-6 Months
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.