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    • Data Engineering
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    879 results for "data engineering"

    • IBM

      IBM

      IBM Data Engineering

      Skills you'll gain: Algorithms, Apache, Big Data, Business Analysis, Cloud Computing, Cloud Engineering, Computational Logic, Computational Thinking, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Data Architecture, Data Management, Data Model, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Database Theory, Databases, DevOps, Distributed Computing Architecture, Extract, Transform, Load, Graph Theory, Hardware Design, IBM Cloud, Kubernetes, Leadership and Management, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Microarchitecture, Network Architecture, Network Security, NoSQL, PostgreSQL, Professional Development, Programming Principles, Project Management, Python Programming, Relational Database, SQL, Security Engineering, Security Strategy, Software Architecture, Software Engineering, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.6

      (34.8k reviews)

      Beginner · Professional Certificate

    • IBM

      IBM

      Data Engineering Foundations

      Skills you'll gain: Apache, Big Data, Computational Logic, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Data Architecture, Data Lake, Data Management, Data Model, Data Warehousing, Database Administration, Database Application, Databases, Distributed Computing Architecture, Extract, Transform, Load, Information Engineering, Leadership and Management, Mathematical Theory & Analysis, Mathematics, Network Security, NoSQL, PostgreSQL, Professional Development, Project Management, Python Programming, SQL, Security Engineering, Security Strategy, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.6

      (34.4k reviews)

      Beginner · Specialization

    • Duke University

      Duke University

      Python, Bash and SQL Essentials for Data Engineering

      Skills you'll gain: Apache, Big Data, Computer Programming, Data Management, Data Structures, Databases, Python Programming, SQL, Statistical Programming, Theoretical Computer Science

      4.2

      (51 reviews)

      Beginner · Specialization

    • Google Cloud

      Google Cloud

      Data Engineering, Big Data, and Machine Learning on GCP

      Skills you'll gain: Apache, Applied Machine Learning, Bayesian Statistics, Big Data, Business Psychology, Cloud Computing, Cloud Platforms, Computer Architecture, Computer Networking, Computer Programming, Data Lake, Data Management, Deep Learning, Distributed Computing Architecture, Entrepreneurship, Feature Engineering, Full-Stack Web Development, General Statistics, Google Cloud Platform, Hardware Design, Machine Learning, Mathematical Optimization, Network Architecture, Probability & Statistics, Python Programming, Statistical Programming, Web Development

      4.6

      (16.5k reviews)

      Intermediate · Specialization

    • Microsoft

      Microsoft

      Microsoft Azure Data Engineering Associate (DP-203)

      Skills you'll gain: Accounting, Analytics, Apache, Application Development, Big Data, Business Psychology, Cloud Computing, Cloud Storage, Computer Networking, Computer Programming, Continuous Integration, Data Architecture, Data Management, Data Warehousing, Databases, DevOps, Entrepreneurship, Extract, Transform, Load, Leadership and Management, Microsoft Azure, Network Security, NoSQL, Organizational Development, SQL, Security, Security Engineering, Security Strategy, Software Engineering, Software Engineering Tools, Statistical Programming

      4.4

      (249 reviews)

      Intermediate · Professional Certificate

    • IBM

      IBM

      IBM Data Warehouse Engineer

      Skills you'll gain: Apache, Big Data, Business Analysis, Cloud Computing, Cloud Engineering, Computer Architecture, Computer Networking, Computer Programming, Data Architecture, Data Lake, Data Management, Data Model, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Databases, DevOps, Distributed Computing Architecture, Extract, Transform, Load, Hardware Design, Leadership and Management, Microarchitecture, Network Security, NoSQL, PostgreSQL, Professional Development, Programming Principles, Project Management, Python Programming, SQL, Security Engineering, Security Strategy, Software Architecture, Software Engineering, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.6

      (16.6k reviews)

      Beginner · Professional Certificate

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      IBM

      IBM

      Introduction to Data Engineering

      Skills you'll gain: Professional Development, Security Strategy, Computer Networking, Data Lake, Leadership and Management, Data Warehousing, Database Administration, NoSQL, Extract, Transform, Load, Relational Database, SQL, Network Security, Databases, Security Engineering, Data Architecture, Project Management, Distributed Computing Architecture, Big Data, Statistical Programming, Computer Architecture, Information Engineering, Data Management, Strategy and Operations, Apache

      4.8

      (849 reviews)

      Beginner · Course

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      Google Cloud

      Google Cloud

      Preparing for Google Cloud Certification: Cloud Data Engineer

      Skills you'll gain: Apache, Applied Machine Learning, Bayesian Statistics, Big Data, Business Psychology, Cloud Computing, Cloud Platforms, Computer Architecture, Computer Programming, Data Management, Deep Learning, Distributed Computing Architecture, Entrepreneurship, Feature Engineering, Full-Stack Web Development, General Statistics, Google Cloud Platform, Hardware Design, Machine Learning, Mathematical Optimization, Probability & Statistics, Python Programming, Statistical Programming, Web Development

      4.6

      (16.8k reviews)

      Intermediate · Professional Certificate

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      Google

      Google

      Google Data Analytics

      Skills you'll gain: Algorithms, Analysis, Application Development, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computational Logic, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Model, Data Structures, Data Visualization, Data Visualization Software, Database Administration, Database Design, Databases, Decision Making, Design and Product, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, General Statistics, Interactive Data Visualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Network Security, Other Programming Languages, Plot (Graphics), Privacy, Probability & Statistics, Problem Solving, Product Design, Programming Principles, Project Management, R Programming, Research and Design, SQL, Security Engineering, Security Strategy, Small Data, Software Engineering, Software Security, Spreadsheet Software, Statistical Analysis, Statistical Programming, Storytelling, Strategy and Operations, Theoretical Computer Science, Visual Design

      4.8

      (63.9k reviews)

      Beginner · Professional Certificate

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      Databricks

      Databricks

      Data Science with Databricks for Data Analysts

      Skills you'll gain: Algorithms, Apache, Applied Machine Learning, Big Data, Business Analysis, Computer Programming, Data Analysis, Data Management, Data Structures, Databases, Dimensionality Reduction, Exploratory Data Analysis, Feature Engineering, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, SQL, Statistical Analysis, Statistical Programming, Theoretical Computer Science

      4.5

      (378 reviews)

      Intermediate · Specialization

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      Duke University

      Duke University

      Scripting with Python and SQL for Data Engineering

      Skills you'll gain: Theoretical Computer Science, Data Structures, Python Programming, SQL, Computer Programming, Data Management, Statistical Programming, Databases

      4.6

      (23 reviews)

      Intermediate · Course

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      Rice University

      Rice University

      Introduction to Scripting in Python

      Skills you'll gain: Analysis, Computer Programming, Mathematics, Python Programming, Statistical Programming

      4.7

      (3.9k reviews)

      Beginner · Specialization

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    In summary, here are 10 of our most popular data engineering courses

    • IBM Data Engineering: IBM
    • Data Engineering Foundations: IBM
    • Python, Bash and SQL Essentials for Data Engineering: Duke University
    • Data Engineering, Big Data, and Machine Learning on GCP: Google Cloud
    • Microsoft Azure Data Engineering Associate (DP-203): Microsoft
    • IBM Data Warehouse Engineer: IBM
    • Introduction to Data Engineering: IBM
    • Preparing for Google Cloud Certification: Cloud Data Engineer: Google Cloud
    • Google Data Analytics: Google
    • Data Science with Databricks for Data Analysts: Databricks

    Skills you can learn in Software Development

    Programming Language (34)
    Google (25)
    Computer Program (21)
    Software Testing (21)
    Web (19)
    Google Cloud Platform (18)
    Application Programming Interfaces (17)
    Data Structure (16)
    Problem Solving (14)
    Object-oriented Programming (13)
    Kubernetes (10)
    List & Label (10)

    Frequently Asked Questions about Data Engineering

    • 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.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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