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Our Courses
Healthcare Data Models
Career prospects are bright for those qualified to work in healthcare data analytics. Perhaps you work in data analytics, but are considering a move into healthcare where your work can improve people’s quality of life.
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Course by
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Self Paced
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12 hours
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English
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth 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. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regressi
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Course by
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Self Paced
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14 hours
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English
Creating a Real-time Data Pipeline using Eventarc and MongoDB Atlas
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will provision the MongoDB Atlas cluster and run a Cloud Function to simulate an IIOT sensor that publishes data to Pub/Sub.
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Course by
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Self Paced
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1 hour
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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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13 hours
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English
MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services
Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course - MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services.
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Course by
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English
Building Resilient Streaming Analytics Systems on Google Cloud
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using Google Cloud Skills Boost.
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Course by
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Self Paced
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8 hours
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English
Introduction to Data Engineering
Start your journey in one of the fastest growing professions today with this beginner-friendly Data Engineering course! You will be introduced to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in the ecosystem. You will begin this course by understanding what is data engineering as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in this exciting field.
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Course by
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Self Paced
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13 hours
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English
Google Cloud Big Data and Machine Learning Fundamentals
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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Course by
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Self Paced
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10 hours
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English
Smart Analytics, Machine Learning, and AI on Google Cloud
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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Course by
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Self Paced
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7 hours
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English
Building Batch Data Pipelines on Google Cloud
Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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Course by
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Self Paced
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17 hours
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English
Data Pipelines with TensorFlow Data Services
Bringing a machine learning model into the real world involves a lot more than just modeling.
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Course by
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Self Paced
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12 hours
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English
Modernizing Data Lakes and Data Warehouses with Google Cloud
The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series.
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Course by
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Self Paced
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7 hours
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English
Distributed Computing with Spark SQL
This course is all about big data. It’s for students with SQL experience that want to take the next step on their data journey by learning distributed computing using Apache Spark. Students will gain a thorough understanding of this open-source standard for working with large datasets. Students will gain an understanding of the fundamentals of data analysis using SQL on Spark, setting the foundation for how to combine data with advanced analytics at scale and in production environments.
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Course by
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14 hours
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English