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Advanced Data Science with IBM
As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll un…
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English
CUDA at Scale for the Enterprise
This course will aid in students in learning in concepts that scale the use of GPUs and the CPUs that manage their use beyond the most common consumer-grade GPU installations. They will learn how to manage asynchronous workflows, sending and receiving events to encapsulate data transfers and control signals.
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Self Paced
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English
Serverless Data Processing with Dataflow: Operations em Português Brasileiro
Na última parte da série de cursos do Dataflow, vamos abordar os componentes do modelo operacional do Dataflow. Veremos ferramentas e técnicas para solucionar problemas e otimizar o desempenho do pipeline. Depois analisaremos as práticas recomendadas de teste, implantação e confiabilidade para pipelines do Dataflow. Por fim, faremos uma revisão dos modelos, que facilitam o escalonamento dos pipelines do Dataflow para organizações com centenas de usuários. Essas lições garantem que a plataforma de dados seja estável e resiliente a circunstâncias imprevistas.
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Self Paced
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Portuguese
GPU Programming
This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.
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English
Serverless Data Processing with Dataflow
It is becoming harder and harder to maintain a technology stack that can keep up with the growing demands of a data-driven business. Every Big Data practitioner is familiar with the three V’s of Big Data: volume, velocity, and variety. What if there was a scale-proof technology that was designed to meet these demands? Enter Google Cloud Dataflow. Google Cloud Dataflow simplifies data processing by unifying batch & stream processing and providing a serverless experience that allows users to focus on analytics, not infrastructure.
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English
用Python玩转数据 Data Processing Using Python
本课程 (Please click https://www.coursera.org/learn/python-data-processing for English version) 主要面向非计算机专业学生，从Python基本语法开始，到Python中如何从本地和网络上进行数据获取，如何解析和表示数据，再到如何利用Python开源生态系统SciPy对数据进行基础和高级的统计分析及可视化，包括数据探索和预处理的具体方法，到最后如何设计一个简单的GUI界面来表示和处理数据，层层推进。 整个课程以财经数据为基础，通过构建一个个喜闻乐见的案例，让大家可以以更直观的方式领略Python的简洁、优雅和健壮，同时探讨Python除了在商业领域之外在文学、社会学和新闻等人文社科类领域以及在数学和生物等理工类领域同样拥有便捷高效的数据处理能力，并可以触类旁通将其灵活应用于各专业中。 近期（2019年11月6日已更新完毕）本课程进行了全面改版，新版主要在以下几个方面做了改变： 1. 丰富了Python基础的案例实际操作和讲解； 2. 增加和扩展了如NumPy包的矢量运算和广播思想及常见应用，数据探索与预处理的多个环节，基于pandas的数据分析及数据挖掘案例等。
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Self Paced
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Chinese
Preparing for Google Cloud Certification: Cloud Data Engineer
This program provides the skills you need to advance your career in data engineering and provides a pathway to earn the industry-recognized Google Cloud Professional Data Engineer certification.
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Self Paced
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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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Self Paced
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English
Data Engineering, Big Data, and Machine Learning on GCP
This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning.
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Self Paced
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English
Serverless Data Processing with Dataflow:Foundations Español
Este curso corresponde a la 1ª parte de una serie de 3 cursos llamada Serverless Data Processing with Dataflow. Para comenzar, en el primer curso haremos un repaso de qué es Apache Beam y cómo se relaciona con Dataflow. Luego, hablaremos sobre la visión de Apache Beam y los beneficios que ofrece su framework de portabilidad. Dicho framework hace posible que un desarrollador pueda usar su lenguaje de programación favorito con su backend de ejecución preferido. Después, le mostraremos cómo Dataflow le permite separar el procesamiento y el almacenamiento y, a la vez, ahorrar dinero.
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Self Paced
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Spanish
Serverless Data Processing Dataflow em Português Brasileiro
Está se tornando cada vez mais difícil manter uma pilha de tecnologia que possa acompanhar as crescentes demandas de um negócio orientado a dados. Todo praticante de Big Data está familiarizado com os três V’s do Big Data: volume, velocidade e variedade. E se houvesse uma tecnologia à prova de escala projetada para atender a essas demandas? Entre no Google Cloud Dataflow. O Google Cloud Dataflow simplifica o processamento de dados unificando o processamento em lote e fluxo e fornecendo uma experiência sem servidor que permite que os usuários se concentrem na análise, não na infraestrutura.
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Self Paced
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Portuguese
Serverless Data Processing with Dataflow: Operations en Español
En esta última parte de la serie de cursos de Dataflow, presentaremos los componentes del modelo operativo de Dataflow. Examinaremos las herramientas y técnicas que permiten solucionar problemas y optimizar el rendimiento de las canalizaciones. Luego, revisaremos las prácticas recomendadas de las pruebas, la implementación y la confiabilidad en relación con las canalizaciones de Dataflow. Concluiremos con una revisión de las plantillas, que facilitan el ajuste de escala de las canalizaciones de Dataflow para organizaciones con cientos de usuarios.
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Self Paced
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Spanish
Serverless Data Processing with Dataflow: Develop Pipelines em Português Brasileiro
In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance.
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Self Paced
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Portuguese
Serverless Data Processing with Dataflow: Develop Pipelines en Español
En esta segunda parte de la serie de cursos sobre Dataflow, analizaremos en profundidad el desarrollo de canalizaciones con el SDK de Beam. Comenzaremos con un repaso de los conceptos de Apache Beam. A continuación, analizaremos el procesamiento de datos de transmisión con ventanas, marcas de agua y activadores. Luego, revisaremos las opciones de fuentes y receptores en sus canalizaciones, los esquemas para expresar datos estructurados y cómo realizar transformaciones con estado mediante las API de State y de Timer.
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Self Paced
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Spanish
Serverless Data Processing with Dataflow: Foundations em Português Brasileiro
Este é o primeiro de uma série de três cursos sobre processamento de dados sem servidor com o Dataflow. Nele, vamos relembrar o que é o Apache Beam e qual é a relação entre ele e o Dataflow. Depois, falaremos sobre a visão do Apache Beam e os benefícios do framework de portabilidade desse modelo de programação. Com esse processo, o desenvolvedor pode usar a linguagem de programação favorita com o back-end de execução que quiser. Em seguida, mostraremos como o Dataflow permite a separação entre a computação e o armazenamento para economizar dinheiro.
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Self Paced
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Portuguese
How Computers Work: Demystifying Computation
Explore the fundamentals of computing: computer architecture, binary logic, data processing, circuits & more.
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Self Paced
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12
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English
Data Science Companion
The Data Science Companion provides an introduction to data science. You will gain a quick background in data science and core machine learning concepts, such as regression and classification. You’ll be introduced to the practical knowledge of data processing and visualization using low-code solutions, as well as an overview of the ways to integrate multiple tools effectively to solve data science problems. You will then leverage cloud resources from Amazon Web Services to scale data processing and accelerate machine learning model training.
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Self Paced
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2 hours
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English
Vertex AI: Qwik Start
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use BigQuery for data processing and exploratory data analysis, and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value (CLV). The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. Starting with a local BigQuery and TensorFlow workflow, you will progress toward training and deploying your model in the cloud with Vertex AI.
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Self Paced
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2 hours
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English
Implement Real Time Analytics using Azure Stream Analytics
In this project, we are going to see how to "Implement Real Time Analytics using Azure Stream Analytics" Data processing is broadly categorized into two main categories: Batch processing & Real time processing.
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Self Paced
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3 hours
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English
Data Analysis Using Pyspark
One of the important topics that every data analyst should be familiar with is the distributed data processing technologies. As a data analyst, you should be able to apply different queries to your dataset to extract useful information out of it. but what if your data is so big that working with it on your local machine is not easy to be done. That is when the distributed data processing and Spark Technology will become handy.
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Self Paced
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3 hours
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English
Serverless Data Processing with Dataflow: Foundations
This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend.
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Self Paced
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3 hours
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English
AWS Data Processing
AWS: Data Processing Course is the second course of AWS Certified Data Analytics Specialty Specialization. This course focuses on providing data processing solutions. The entire course is designed to teach learners the concept of EMR and Extract, Transform and Load. This course also put emphasis on ETL services and Data Processing solutions in AWS. The course is divided into three modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 3:30-4:00 Hours Video lectures that provide both Theory and Hands -On knowledge.
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Self Paced
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6 hours
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English
Data Platform, Cloud Networking and AI in the Cloud
The Data Platform course aims to establish a strong foundation, and working knowledge of the fundamentals of data, including data mechanics, databases, and other foundational elements of data processing. This course will drill into the specific data management elements including relational taxonomy of data, data lifecycle and fundamentals databases and data processing and analysis. The course also covers the relevance of IA with respect to data in the cloud.
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Self Paced
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7 hours
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English
Design Thinking and Predictive Analytics for Data Products
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
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Self Paced
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8 hours
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English
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center
The Library of Integrative Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that lasted for 10 years from 2012-2021. The idea behind the LINCS program was to perturb different types of human cells with many different types of perturbations such as drugs and other small molecules, genetic manipulations such as single gene knockdown, knockout, or overexpression, manipulation of the extracellular microenvironment conditions, for example, growing cells on different surfaces, and more.
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Self Paced
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9 hours
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English