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Our Courses
Dataproc: Qwik Start - Command Line
This is a self-paced lab that takes place in the Google Cloud console. This lab shows you how to create a Google Cloud Dataproc cluster, run a simple Apache Spark job in the cluster, then modify the number of workers in the cluster, all from the gcloud Command Line. Watch these short videos, Dataproc: Qwik Start - Qwiklabs Preview and Run Spark and Hadoop Faster with Cloud Dataproc.
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Course by
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Self Paced
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English
Perform data science with Azure Databricks
In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data science workloads in the cloud. This is the fourth course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurec ertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning.
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Course by
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Self Paced
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26 hours
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English
Use the Apache Spark Structured Streaming API with MongoDB
By the end of this project, you will use the Apache Spark Structured Streaming API with Python to stream data from two different sources, store a dataset in the MongoDB database, and join two datasets.
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Course by
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Self Paced
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3 hours
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English
Data Engineering with MS Azure Synapse Apache Spark Pools
In this course, you will learn how to perform data engineering with Azure Synapse Apache Spark Pools, which enable you to boost the performance of big-data analytic applications by in-memory cluster computing. You will learn how to differentiate between Apache Spark, Azure Databricks, HDInsight, and SQL Pools and understand the use-cases of data-engineering with Apache Spark in Azure Synapse Analytics. You will also learn how to ingest data using Apache Spark Notebooks in Azure Synapse Analytics and transform data using DataFrames in Apache Spark Pools in Azure Synapse Analytics.
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Course by
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Self Paced
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8 hours
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English
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for
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Course by
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Self Paced
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9 hours
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English
Building Realtime Pipelines in Cloud Data Fusion
This is a self-paced lab that takes place in the Google Cloud console. In addition to batch pipelines, Data Fusion also allows you to create real-time pipelines, that can process events as they are generated. Currently, realtime pipelines execute using Apache Spark Streaming on Cloud Dataproc clusters. In this lab, you will learn how to build a streaming pipeline using Data Fusion.
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Course by
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Self Paced
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2 hours
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English
Machine Learning with Apache Spark
Explore the exciting world of machine learning with this IBM course. Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos. Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML.
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Course by
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Self Paced
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15 hours
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English
Apache Spark (TM) SQL for Data Analysts
Apache Spark is one of the most widely used technologies in big data analytics. In this course, you will learn how to leverage your existing SQL skills to start working with Spark immediately. You will also learn how to work with Delta Lake, a highly performant, open-source storage layer that brings reliability to data lakes. By the end of this course, you will be able to use Spark SQL and Delta Lake to ingest, transform, and query data to extract valuable insights that can be shared with your team.
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Course by
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Self Paced
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14 hours
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English
Microsoft Azure Databricks for Data Engineering
In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud. You will discover the capabilities of Azure Databricks and the Apache Spark notebook for processing huge files. You will come to understand the Azure Databricks platform and identify the types of tasks well-suited for Apache Spark. You will also be introduced to the architecture of an Azure Databricks Spark Cluster and Spark Jobs.
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Course by
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Self Paced
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22 hours
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English
Data Engineering and Machine Learning using Spark
NOTE: This course is currently replaced with IBM Machine Learning with Apache Spark.
Further your data engineering career with this self-paced course about machine learning with Apache Spark! Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.
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Course by
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Self Paced
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8 hours
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English
Big Data Analysis with Scala and Spark (Scala 2 version)
Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout.
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Course by
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Self Paced
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28 hours
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English
Functional Programming Principles in Scala (Scala 2 version)
Functional programming is becoming increasingly widespread in industry. This trend is driven by the adoption of Scala as the main programming language for many applications. Scala fuses functional and object-oriented programming in a practical package. It interoperates seamlessly with both Java and Javascript. Scala is the implementation language of many important frameworks, including Apache Spark, Kafka, and Akka.
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Course by
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Self Paced
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45 hours
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English
Dataproc: Qwik Start - Console
This is a self-paced lab that takes place in the Google Cloud console. This lab shows you how to create a Google Cloud Dataproc cluster, run a simple Apache Spark job in the cluster, then modify the number of workers in the cluster, all from the gcloud Command Line. Watch these short videos, Dataproc: Qwik Start - Qwiklabs Preview and Run Spark and Hadoop Faster with Cloud Dataproc.
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Course by
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Self Paced
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1 hour
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English
Big Data Analytics
Learn key technologies and techniques, including R and Apache Spark, to analyse large-scale data sets to uncover valuable business information.
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Course by
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English
Working with Big Data
By the end of this project, you will set up an environment for Big Data Development using Visual Studio Code, MongoDB and Apache Spark.
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Course by
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Self Paced
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3 hours
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English
Learn SQL Basics for Data Science
This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more.
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Course by
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Self Paced
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English
Data Engineering Capstone Project
Showcase your skills in this Data Engineering project! In this course you will apply a variety of data engineering skills and techniques you have learned as part of the previous courses in the IBM Data Engineering Professional Certificate. You will demonstrate your knowledge of Data Engineering by assuming the role of a Junior Data Engineer who has recently joined an organization and be presented with a real-world use case that requires architecting and implementing a data analytics platform. In this Capstone project you will complete numerous hands-on labs.
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Course by
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Self Paced
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13 hours
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English
Functional Programming in Scala
Discover how to write elegant code that works the first time it is run. This Specialization provides a hands-on introduction to functional programming using the widespread programming language, Scala. It begins from the basic building blocks of the functional paradigm, first showing how to use these blocks to solve small problems, before building up to combining these concepts to architect larger functional programs.
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Course by
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Self Paced
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English
Data Processing with Azure
This Azure training course is designed to equip students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as Python, R, and Apache Spark.
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Course by
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Self Paced
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13 hours
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English
NoSQL, Big Data, and Spark Foundations
Big Data Engineers and professionals with NoSQL skills are highly sought after in the data management industry. This Specialization is designed for those seeking to develop fundamental skills for working with Big Data, Apache Spark, and NoSQL databases.
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Course by
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Self Paced
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English
Scalable Machine Learning on Big Data using Apache Spark
This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner.
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Course by
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Self Paced
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7 hours
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English
Applied Data Science for Data Analysts
In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.
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Course by
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Self Paced
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17 hours
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English
Machine Learning with Spark on Google Cloud Dataproc
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to implement logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset.
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Course by
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Self Paced
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2 hours
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English
Distributed Image Processing in Cloud Dataproc
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to use Apache Spark on Cloud Dataproc to distribute a computationally intensive image processing task onto a cluster of machines.
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Course by
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Self Paced
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1 hour
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English
Introduction to Big Data with Spark and Hadoop
This self-paced IBM course will teach you all about big data! You will become familiar with the characteristics of big data and its application in big data analytics. You will also gain hands-on experience with big data processing tools like Apache Hadoop and Apache Spark. Bernard Marr defines big data as the digital trace that we are generating in this digital era. You will start the course by understanding what big data is and exploring how insights from big data can be harnessed for a variety of use cases.
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Course by
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Self Paced
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24 hours
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English