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Our Courses
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
Operational Analytics with Microsoft Azure Synapse Analytics
In this course, you will learn how to perform operational analytics against Azure Cosmos DB using the Azure Synapse Link feature within Azure Synapse Analytics. You will learn how hybrid transactional and analytical processing can help you perform operational analytics with Azure Synapse Analytics.
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Course by
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Self Paced
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12 hours
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English
Build an End-to-End Data Capture Pipeline using Document AI
This is a self-paced lab that takes place in the Google Cloud console. In this lab you use Cloud Functions and Pub/Sub to create an end-to-end document processing pipeline using Document AI. The Document AI API is a document understanding solution that takes unstructured data, such as documents and emails, and makes the data easier to understand, analyze, and consume. In this lab, you will create a document processing pipeline that will automatically process documents that are uploaded to Cloud Storage.
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Course by
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Self Paced
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1 hour
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English
Data Warehousing with Microsoft Azure Synapse Analytics
In this course, you will explore the tools and techniques that can be used to work with Modern Data Warehouses productively and securely within Azure Synapse Analytics. You will learn how Azure Synapse Analytics enables you to build Data Warehouses using modern architecture patterns and how the common schema is implemented in a data warehouse.
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Course by
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Self Paced
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15 hours
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English
Leveraging Unstructured Data with Cloud Dataproc on Google Cloud em Português Brasileiro
Este curso intensivo de uma semana baseia-se nos cursos anteriores da especialização Data Engineering on Google Cloud Platform.
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Course by
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Self Paced
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English
Process Documents with Python Using the Document AI API
This is a self-paced lab that takes place in the Google Cloud console.
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Course by
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Self Paced
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1 hour
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English
Prepare for DP-203: Data Engineering on Microsoft Azure Exam
Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. In this course, you will prepare to take the DP-203 Microsoft Azure Data Fundamentals certification exam. You will refresh your knowledge of how to use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.
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Course by
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Self Paced
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6 hours
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English
Data Storage in Microsoft Azure for Associate Developers
Azure provides a variety of ways to store data: unstructured, archival, relational, and more. In this course, you will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for the data you want to store in the cloud. This course part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta).
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Course by
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Self Paced
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16 hours
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English
Introduction to Microsoft Azure Synapse Analytics
In this course, you will learn how Azure Synapse Analytics enables you to perform different types of analytics through its’ components that can be used to build Modern Data Warehouses through to advanced analytical solutions. You will learn how Azure Synapse Analytics solves the issue of having a single service to fulfill the broad range of analytics requirements that organizations face today and take a tour of the core application used to interact with the various components of 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
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
Data Integration with Microsoft Azure Data Factory
In this course, you will learn how to create and manage data pipelines in the cloud using Azure Data Factory. This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services. It is ideal for anyone interested in preparing for the DP-203: Data Engineering on Microsoft Azure exam (beta).
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Course by
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Self Paced
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16 hours
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English
Microsoft Azure for Data Engineering
The world of data has evolved and the advent of cloud technologies is providing new opportunities for businesses to explore. In this course, you will learn the various data platform technologies available, and how a Data Engineer can take advantage of this technology to an organization's benefit. This course part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta).
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Course by
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Self Paced
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6 hours
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English
Creating a Data Transformation Pipeline with Cloud Dataprep
This is a self-paced lab that takes place in the Google Cloud console. Cloud Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. In this lab you will explore the Cloud Dataprep UI to build a data transformation pipeline.
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Course by
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Self Paced
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1 hour
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English
Business intelligence and data warehousing
Welcome to the specialization course Business Intelligence and Data Warehousing. This course will be completed on six weeks, it will be supported with videos and various documents that will allow you to learn in a very simple way how to identify, design and develop analytical information systems, such as Business Intelligence with a descriptive analysis on data warehouses.
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Course by
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Self Paced
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10 hours
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English
Innovating with Data and Google Cloud
Cloud technology on its own only provides a fraction of the true value to a business; When combined with data–lots and lots of it–it has the power to truly unlock value and create new experiences for customers.
In this course, you'll learn what data is, historical ways companies have used it to make decisions, and why it is so critical for machine learning. This course also introduces learners to technical concepts such as structured and unstructured data. database, data warehouse, and data lakes. It then covers the most common and fastest growing Google Cloud products around data.
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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 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
Cognitive Solutions and RPA Analytics
Millions of companies in the world today are processing endless documents in various formats. Although Robotic Process Automation (RPA) thrives in almost every industry and is growing fast, it works well only with structured data sources.
What about the data that’s not fully structured and comes in varying layouts? To address this problem, there is another aspect of RPA that is taking the industry by storm: cognitive automation.
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Course by
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Self Paced
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5 hours
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English
Data Storage in Microsoft Azure
Azure provides a variety of ways to store data: unstructured, archival, relational, and more. In this course, you will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for the data you want to store in the cloud. This course part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta).
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Course by
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Self Paced
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16 hours
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English
Azure Data Lake Storage Gen2 and Data Streaming Solution
In this course, you will see how Azure Data Lake Storage can make processing Big Data analytical solutions more efficient and how easy it is to set up. You will also explore how it fits into common architectures, as well as the different methods of uploading the data to the data store. You will examine the myriad of security features that will ensure your data is secure. Learn the concepts of event processing and streaming data and how this applies to Azure Stream Analytics.
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Course by
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Self Paced
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9 hours
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English
IBM Data Engineering
Prepare for a career in the high-growth field of data engineering. In this program, you’ll learn in-demand skills like Python, SQL, and Databases to get job-ready in less than 5 months. Data engineering is building systems to gather data, process and organize raw data into usable information.
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Course by
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Self Paced
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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
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python.
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Course by
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Self Paced
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English
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science.
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Course by
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Self Paced
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English
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications.
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Course by
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Self Paced
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English
Social Media Data Analytics
Learner Outcomes: After taking this course, you will be able to: - Utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr. - Process the collected data - primarily structured - using methods involving correlation, regression, and classification to derive insights about the sources and people who generated that data. - Analyze unstructured data - primarily textual comments - for sentiments expressed in them. - Use different tools for collecting, analyzing, and exploring social media data for resea
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Course by
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13 hours
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English