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    • Apache Spark

    Apache Spark Courses Online

    Master Apache Spark for big data processing and analytics. Learn to use Spark for real-time data processing and machine learning.

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    Explore the Apache Spark Course Catalog

    • G

      Google Cloud

      Creating a Streaming Data Pipeline With Apache Kafka

      Skills you'll gain: Apache Kafka, Data Pipelines, Google Cloud Platform, Java, Public Cloud

      3.8
      Rating, 3.8 out of 5 stars
      ·
      27 reviews

      Beginner · Project · Less Than 2 Hours

    • G

      Google Cloud

      Smart Analytics, Machine Learning, and AI on Google Cloud

      Skills you'll gain: Google Cloud Platform, MLOps (Machine Learning Operations), Tensorflow, Unstructured Data, Applied Machine Learning, Data Pipelines, Artificial Intelligence and Machine Learning (AI/ML), Big Data, Machine Learning, Jupyter, Natural Language Processing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.2K reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Performance Testing with JMeter for absolute beginners

      Skills you'll gain: Performance Testing, Test Tools, Test Execution Engine, Application Performance Management, Test Data

      4.6
      Rating, 4.6 out of 5 stars
      ·
      70 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • G

      Google Cloud

      Serverless Data Processing with Dataflow: Develop Pipelines

      Skills you'll gain: Dataflow, Data Pipelines, Data Processing, Real Time Data, Data Transformation, Jupyter, Google Cloud Platform, JSON, SQL

      4
      Rating, 4 out of 5 stars
      ·
      41 reviews

      Advanced · Course · 1 - 3 Months

    • D

      Duke University

      MLOps Platforms: Amazon SageMaker and Azure ML

      Skills you'll gain: AWS SageMaker, MLOps (Machine Learning Operations), Microsoft Azure, Exploratory Data Analysis, Data Pipelines, Amazon Web Services, Feature Engineering, Cloud Solutions, Artificial Intelligence and Machine Learning (AI/ML), Data Analysis, Predictive Modeling, Serverless Computing, Machine Learning, Machine Learning Algorithms, Containerization

      3.6
      Rating, 3.6 out of 5 stars
      ·
      48 reviews

      Advanced · Course · 1 - 3 Months

    • G

      Google Cloud

      Machine Learning with Spark on Google Cloud Dataproc

      Skills you'll gain: Apache Spark, Google Cloud Platform, Predictive Modeling, Big Data, Applied Machine Learning, Cloud Computing, Statistical Modeling

      Intermediate · Project · Less Than 2 Hours

    • É

      École Polytechnique Fédérale de Lausanne

      Functional Programming Principles in Scala

      Skills you'll gain: Scala Programming, Programming Principles, Object Oriented Programming (OOP), Functional Design, Computer Programming, Data Structures, Integrated Development Environments, Javascript, Algorithms

      4.8
      Rating, 4.8 out of 5 stars
      ·
      8.3K reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Illinois Urbana-Champaign

      Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

      Skills you'll gain: Big Data, Apache Spark, Data Storage Technologies, Apache Hadoop, Distributed Computing, Data Storage, Data Processing, NoSQL, Apache Kafka, Real Time Data, Cloud Computing, Databases, Analytics, Deep Learning, Scalability, Machine Learning Algorithms, Graph Theory, Live Streaming

      4.3
      Rating, 4.3 out of 5 stars
      ·
      340 reviews

      Mixed · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Diabetes Prediction With Pyspark MLLIB

      Skills you'll gain: Data Cleansing, Apache Spark, PySpark, Data Manipulation, Applied Machine Learning, Data Processing, Classification And Regression Tree (CART), Predictive Modeling, Regression Analysis, Machine Learning, Google Cloud Platform

      4.6
      Rating, 4.6 out of 5 stars
      ·
      22 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • R

      Rice University

      Distributed Programming in Java

      Skills you'll gain: Apache Kafka, Apache Spark, Apache Hadoop, Distributed Computing, Java, Software Architecture, Systems Architecture, Programming Principles, Servers, Algorithms, Network Protocols

      4.6
      Rating, 4.6 out of 5 stars
      ·
      500 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of California San Diego

      Big Data Integration and Processing

      Skills you'll gain: Apache Spark, Apache Hadoop, Data Integration, Big Data, Data Pipelines, Data Processing, Data Management, NoSQL, Analytics, Splunk, Database Systems, MongoDB, Pandas (Python Package), SQL, PostgreSQL

      4.4
      Rating, 4.4 out of 5 stars
      ·
      2.4K reviews

      Beginner · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Graduate Admission Prediction with Pyspark ML

      Skills you'll gain: Data Cleansing, PySpark, Data Processing, Pandas (Python Package), Applied Machine Learning, Predictive Modeling, Regression Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      34 reviews

      Intermediate · Guided Project · Less Than 2 Hours

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    In summary, here are 10 of our most popular apache spark courses

    • Creating a Streaming Data Pipeline With Apache Kafka: Google Cloud
    • Smart Analytics, Machine Learning, and AI on Google Cloud: Google Cloud
    • Performance Testing with JMeter for absolute beginners: Coursera Project Network
    • Serverless Data Processing with Dataflow: Develop Pipelines: Google Cloud
    • MLOps Platforms: Amazon SageMaker and Azure ML: Duke University
    • Machine Learning with Spark on Google Cloud Dataproc: Google Cloud
    • Functional Programming Principles in Scala: École Polytechnique Fédérale de Lausanne
    • Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud: University of Illinois Urbana-Champaign
    • Diabetes Prediction With Pyspark MLLIB: Coursera Project Network
    • Distributed Programming in Java: Rice University

    Skills you can learn in Machine Learning

    Python Programming (33)
    Tensorflow (32)
    Deep Learning (30)
    Artificial Neural Network (24)
    Big Data (18)
    Statistical Classification (17)
    Reinforcement Learning (13)
    Algebra (10)
    Bayesian (10)
    Linear Algebra (10)
    Linear Regression (9)
    Numpy (9)

    Frequently Asked Questions about Apache Spark

    Apache Spark is an open source analytics framework for large-scale data processing with capabilities for streaming, SQL, machine learning, and graph processing. Apache Spark is important to learn because its ease of use and extreme processing speeds enable efficient and scalable real-time data analysis.

    Apache Spark can process in-memory on dedicated clusters to achieve speeds 10-100 times faster than the disc-based batch processing Apache Hadoop with MapReduce can provide, making it a top choice for anyone processing big data. Spark is also easy to use, with the ability to write applications in its native Scala, or in Python, Java, R, or SQL. This versatility and accessibility helps startups harness the powerful data science they need for cutting edge innovation.

    Spark also provides the scalable machine learning needed by artificial intelligence (AI) engineers to create applications that can transform the way we interact with digital technology, from recommendation algorithms on services like Netflix and Spotify to automated medical screening.‎

    Many careers in data science benefit from skills in Apache Spark, as software development engineers, data scientists, data analysts, and machine learning engineers use Spark on a daily basis. These roles are in high demand and are thus highly compensated; according to Glassdoor, machine learning engineers earn an average salary of $114,121 per year.

    Machine learning engineers design and build self-learning software and monitor its iterations to fine tune how models perform when they are scaled up and put into service. These professionals need a background in both software engineering and data science, and are increasingly being hired in a wide variety of fields such as education, healthcare, and finance. As machine learning continues to expand into many more fields, the need for machine learning engineers will continue to grow.‎

    Yes! Coursera offers a wide range of popular online courses and Specializations on data science in general and Apache Spark specifically, including courses in related topics like scalable machine learning, distributed computing, and big data analysis. You’ll learn from top-ranked institutions and organizations like the University of California Davis, the University of California San Diego, École Polytechnique Fédérale de Lausanne, and IBM, so you don’t have to sacrifice the quality of your education for the flexibility of learning remotely.

    Coursera also offers the courses needed to work towards the IBM AI Engineering Professional Certificate. And, if you want to take your data science education to the next level, Coursera provides you with the opportunity to pursue a Master of Science in Data Science through the University of Colorado.‎

    Because Spark works in application programming interfaces like Scala, Java, and Python, it helps to have a good grasp of one or more of these programming languages. Other prerequisites may vary depending on the level of the course you're taking. While beginner-level courses allow you to become familiar with Apache Spark and develop skills as you go, intermediate or advanced courses may require additional skills or experience within data science or computer programming. As you progress with learning Apache Spark, you'll develop the skills needed to read and write data to a variety of sources, parse different types of data, work within the artificial intelligence and machine learning arena, and transform data to leverage insights from it.‎

    People with a passion for data science and a desire to gain increased access to big data are well suited to learning Apache Spark. This tool opens a variety of opportunities for users to explore big data and leverage it to solve key problems within organizations. Additionally, Spark offers a faster pace for machine learning workloads, with large scale data processing capability that's exponentially faster than other tools like Hadoop. Because Apache Spark is on the front lines of innovation within AI and big data, those with an innate sense of curiosity and a desire to innovate are among those best suited to learning Spark and working in relevant roles.‎

    If you want to work within big data, learning Apache Spark could be a good move for you. This unified analytics engine is particularly popular because of its speed, the libraries that come with it, robust APIs, and its support for multiple programming languages. Additionally, it could be a smart career move depending on your aspirations. Demand continues to surge for professionals who can leverage Spark's power. In February 2021, Indeed.com listed more than 1,800 open positions looking for full-time Apache Spark professionals across multiple industries. Additionally, according to Databricks, learning Apache Sparks could give you a boost in your earning potential.‎

    Online Apache Spark courses offer a convenient and flexible way to enhance your existing knowledge or learn new Apache Spark skills. With a wide range of Apache Spark classes, you can conveniently learn at your own pace to advance your Apache Spark career.‎

    When looking to enhance your workforce's skills in Apache Spark, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    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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