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    Results for "data engineering"

    • Status: AI skills
      AI skills

      Meta

      Meta Data Analyst

      Skills you'll gain: Data Storytelling, Business Metrics, Key Performance Indicators (KPIs), Data Management, Data Collection, Data Governance, Bayesian Statistics, Data Analysis, Descriptive Statistics, Statistical Hypothesis Testing, Information Privacy, Data Cleansing, Pandas (Python Package), Data Visualization Software, Statistical Inference, Spreadsheet Software, Correlation Analysis, Google Sheets, Exploratory Data Analysis, Data Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.4K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • IBM

      Python Project for Data Science

      Skills you'll gain: Dashboard, Pandas (Python Package), Data Visualization Software, Web Scraping, Jupyter, Matplotlib, Data Analysis, Data Science, Data Processing, Data Manipulation, Python Programming, Data Collection

      4.5
      Rating, 4.5 out of 5 stars
      ·
      4.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    • IBM

      IBM Data Analytics with Excel and R

      Skills you'll gain: Data Storytelling, Interactive Data Visualization, Shiny (R Package), Data Wrangling, Exploratory Data Analysis, Relational Databases, Big Data, Data Visualization Software, Ggplot2, Database Design, Data Analysis, IBM Cognos Analytics, Statistical Analysis, Data Presentation, Data Mining, Dashboard, Excel Formulas, Data Manipulation, Web Scraping, Microsoft Excel

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      28K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • University of Michigan

      Applied Data Science with Python

      Skills you'll gain: Matplotlib, Network Analysis, Feature Engineering, Data Visualization Software, Interactive Data Visualization, Scientific Visualization, Pandas (Python Package), Applied Machine Learning, Supervised Learning, Text Mining, Visualization (Computer Graphics), Statistical Visualization, Scikit Learn (Machine Learning Library), Network Model, Jupyter, NumPy, Graph Theory, Data Manipulation, Natural Language Processing, Data Analysis

      4.5
      Rating, 4.5 out of 5 stars
      ·
      34K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Google

      Data Analysis with R Programming

      Skills you'll gain: Rmarkdown, Ggplot2, R Programming, Data Analysis, Tidyverse (R Package), Statistical Programming, Data Visualization Software, Data Cleansing, Data Manipulation, Exploratory Data Analysis, Data Import/Export, Package and Software Management, Data Structures

      4.8
      Rating, 4.8 out of 5 stars
      ·
      11K reviews

      Beginner · Course · 1 - 3 Months

    • Johns Hopkins University

      Data Science: Foundations using R

      Skills you'll gain: Rmarkdown, Exploratory Data Analysis, Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Data Wrangling, Data Visualization, Plot (Graphics), Statistical Programming, Ggplot2, Big Data, Git (Version Control System), Data Integration, Knitr, Data Analysis, Data Sharing, Statistical Reporting

      4.6
      Rating, 4.6 out of 5 stars
      ·
      48K reviews

      Beginner · Specialization · 3 - 6 Months

    • IBM

      Data Science Methodology

      Skills you'll gain: Jupyter, Peer Review, Data Modeling, Data Science, Data Cleansing, Business Analysis, Data Mining, Predictive Modeling, Data Quality, Data Storytelling, Analytical Skills, User Feedback, Decision Tree Learning, Stakeholder Engagement

      4.6
      Rating, 4.6 out of 5 stars
      ·
      21K reviews

      Beginner · Course · 1 - 4 Weeks

    • Duke University

      Virtualization, Docker, and Kubernetes for Data Engineering

      Skills you'll gain: Site Reliability Engineering, Docker (Software), Containerization, Kubernetes, Virtualization, Devops Tools, Microservices, Development Environment, Software Development Tools, Application Deployment, Virtual Machines, Software Development, Cloud Development, Database Management, GitHub, Scalability

      4
      Rating, 4 out of 5 stars
      ·
      38 reviews

      Advanced · Course · 1 - 4 Weeks

    • IBM

      Applied Software Engineering Fundamentals

      Skills you'll gain: Software Development Life Cycle, Linux Commands, Unit Testing, Bash (Scripting Language), Shell Script, Git (Version Control System), GitHub, Development Environment, Version Control, File Management, Jupyter, Software Engineering, Software Development Tools, Application Deployment, Collaborative Software, Automation, Flask (Web Framework), Web Scraping, Python Programming, Software Testing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      47K reviews

      Beginner · Specialization · 3 - 6 Months

    • IBM

      IBM Data Management

      Skills you'll gain: Dashboard, Data Storytelling, Data Warehousing, SQL, Data Governance, Data Security, Data Migration, Database Design, Data Literacy, Descriptive Statistics, Extract, Transform, Load, Data Mining, Cloud Storage, Data Visualization Software, Data Store, IBM DB2, Data Management, Relational Databases, MySQL, Excel Formulas

      4.7
      Rating, 4.7 out of 5 stars
      ·
      17K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • University of Colorado Boulder

      Power Electronics

      Skills you'll gain: Power Electronics, Control Systems, Semiconductors, Electronic Systems, Electrical Engineering, Electronic Components, Basic Electrical Systems, Electric Power Systems, Electrical Power, Electronics, Systems Analysis, Simulations, Electrical Systems, Mathematical Modeling, Systems Design, Graphical Tools, Engineering Analysis, Technical Design, Engineering Design Process, Performance Testing

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      4.2K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Google

      Foundations: Data, Data, Everywhere

      Skills you'll gain: Data Ethics, Data Analysis, Data-Driven Decision-Making, Google Sheets, Spreadsheet Software, Analytical Skills, Data Sharing, Data Cleansing, Data Processing, Data Visualization Software, SQL, Data Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      114K reviews

      Beginner · Course · 1 - 4 Weeks

    Searches related to data engineering

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    1…789…731

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

    • Meta Data Analyst: Meta
    • Python Project for Data Science: IBM
    • IBM Data Analytics with Excel and R: IBM
    • Applied Data Science with Python: University of Michigan
    • Data Analysis with R Programming: Google
    • Data Science: Foundations using R: Johns Hopkins University
    • Data Science Methodology: IBM
    • Virtualization, Docker, and Kubernetes for Data Engineering: Duke University
    • Applied Software Engineering Fundamentals: IBM
    • IBM Data Management: IBM

    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

    Coursera’s Data Engineering courses offer learners a deep dive into the technical skills required to design, build, and manage data infrastructure:

    • Techniques for designing data models and databases.
    • Skills in data warehousing and data lake construction and management.
    • Proficiency in big data technologies such as Hadoop, Spark, and Apache Kafka.
    • Best practices for data integration, ETL (extract, transform, load) processes, and pipeline construction.
    • Understanding of data quality management and data governance.
    • Application of automation and monitoring tools to maintain data systems.‎

    Data Engineering courses on Coursera cater to learners of all backgrounds:

    • Beginners can start with introductory courses that teach the basics of data infrastructure and SQL.
    • Those with some technical experience can focus on advanced topics like big data processing or cloud data services.
    • Advanced learners can explore specialized areas such as machine learning data pipelines or real-time data processing.‎

    Learners who complete Data Engineering courses on Coursera can earn credentials that may enhance their professional profiles:

    • Professional certificates that demonstrate a foundational understanding and practical skills in data engineering.
    • Specializations that focus on detailed aspects of data engineering, such as performance tuning, scalability, or security.
    • Certificates that can potentially be recognized by technology companies and other employers as evidence of expertise in data engineering.‎

    Skills in data engineering can lead to a variety of roles within the technology and analytics sectors:

    • Data Engineer, responsible for designing and managing scalable data systems.
    • Database Administrator, maintaining and optimizing database performance.
    • Cloud Solutions Architect, focusing on cloud storage and data services.
    • Data Architect, shaping strategic data practices and architectures.
    • Machine Learning Engineer, preparing data infrastructure for predictive models.‎

    Yes, Coursera offers a variety of free courses across many topics, including data engineering. While you can access most course materials for free by auditing the courses, this does not include graded assignments or a completion certificate. For those looking to obtain a certificate to showcase their learning or enhance their professional profile, Coursera provides the option to purchase the course. Additionally, Coursera offers free trials or financial aid to learners who qualify, making certifications more accessible to everyone.‎

    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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