Springboard your Big Data career. Master fundamentals of NoSQL, Big Data, and Apache Spark with hands-on job-ready skills in machine learning and data engineering.

4.3

estrellas

52 calificaciones

Rav Ahuja +5 instructores más

2044 ya inscrito

ofrecido por

Programa especializado: NoSQL, Big Data, and Spark FoundationsIBM

Qué aprenderás

Work with NoSQL databases to insert, update, delete, query, index, aggregate, and shard/partition data.
Develop hands-on NoSQL experience working with MongoDB, Apache Cassandra, and IBM Cloudant.
Develop foundational knowledge of Big Data and gain hands-on lab experience using Apache Hadoop, MapReduce, Apache Spark, Spark SQL, and Kubernetes.
Perform Extract, Transform and Load (ETL) processing and Machine Learning model training and deployment with Apache Spark.

Habilidades que obtendrás

Apache Hadoop
Mongodb
Big Data
Apache Spark
NoSQL Databases
NoSQL
Cloud Database
Cloudant
Cassandra
SparkSQL
SparkML

Acerca de este Programa Especializado

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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. Three information-packed courses cover popular NoSQL databases like MongoDB and Apache Cassandra, the widely used Apache Hadoop ecosystem of Big Data tools, as well as Apache Spark analytics engine for large-scale data processing. You start with an overview of various categories of NoSQL (Not only SQL) data repositories, and then work hands-on with several of them including IBM Cloudant, MonogoDB and Cassandra. You’ll perform various data management tasks, such as creating & replicating databases, inserting, updating, deleting, querying, indexing, aggregating & sharding data. Next, you’ll gain fundamental knowledge of Big Data technologies such as Hadoop, MapReduce, HDFS, Hive, and HBase, followed by a more in depth working knowledge of Apache Spark, Spark Dataframes, Spark SQL, PySpark, the Spark Application UI, and scaling Spark with Kubernetes. In the final course, you will learn to work with Spark Structured Streaming Spark ML - for performing Extract, Transform and Load processing (ETL) and machine learning tasks. This specialization is suitable for beginners in the fields of NoSQL and Big Data – whether you are or preparing to be a Data Engineer, Software Developer, IT Architect, Data Scientist, or IT Manager.

Proyecto de aprendizaje aplicado

The emphasis in this specialization is on learning by doing. As such, each course includes hands-on labs to practice & apply the NoSQL and Big Data skills you learn during lectures.

In the first course, you will work hands-on with several NoSQL databases- MongoDB, Apache Cassandra, and IBM Cloudant to perform a variety of tasks: creating the database, adding documents, querying data, utilizing the HTTP API, performing Create, Read, Update & Delete (CRUD) operations, limiting & sorting records, indexing, aggregation, replication, using CQL shell, keyspace operations, & other table operations.

In the next course, you’ll launch a Hadoop cluster using Docker and run Map Reduce jobs. You’ll explore working with Spark using Jupyter notebooks on a Python kernel. You’ll build your Spark skills using DataFrames, Spark SQL, and scale your jobs using Kubernetes.

In the final course you will use Spark for ETL processing, and Machine Learning model training and deployment using IBM Watson.

Certificado para compartir

Obtén un certificado al finalizar

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Cronograma flexible

Establece y mantén fechas de entrega flexibles.

Nivel principiante

The courses in the specialization require that you have basic computer and data literacy skills, as well as some programming background with languages such as with Python and SQL. No prior knowledge or experience of Big Data and NoSQL is required.

Aproximadamente 4 meses para completarse

Ritmo sugerido de 3 horas/semana

Inglés (English)

Subtítulos: Inglés (English)

Certificado para compartir

Obtén un certificado al finalizar

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Cronograma flexible

Establece y mantén fechas de entrega flexibles.

Nivel principiante

Aproximadamente 4 meses para completarse

Ritmo sugerido de 3 horas/semana

Inglés (English)

Subtítulos: Inglés (English)

Cómo funciona el programa especializado

Toma cursos

Un programa especializado de Coursera es un conjunto de cursos que te ayudan a dominar una aptitud. Para comenzar, inscríbete en el programa especializado directamente o échale un vistazo a sus cursos y elige uno con el que te gustaría comenzar. Al suscribirte a un curso que forme parte de un programa especializado, quedarás suscrito de manera automática al programa especializado completo. Puedes completar solo un curso: puedes pausar tu aprendizaje o cancelar tu suscripción en cualquier momento. Visita el panel principal del estudiante para realizar un seguimiento de tus inscripciones a cursos y tu progreso.

Proyecto práctico

Cada programa especializado incluye un proyecto práctico. Necesitarás completar correctamente el proyecto para completar el programa especializado y obtener tu certificado. Si el programa especializado incluye un curso separado para el proyecto práctico, necesitarás completar cada uno de los otros cursos antes de poder comenzarlo.

Obtén un certificado

Cuando completes todos los cursos y el proyecto práctico, obtendrás un Certificado que puedes compartir con posibles empleadores y tu red profesional.

Hay 3 cursos en este Programa Especializado

Curso1

Curso 1

Introduction to NoSQL Databases

4.6

estrellas

90 calificaciones

•

19 reseña

This course will provide you with technical hands-on knowledge of NoSQL databases and Database-as-a-Service (DaaS) offerings. With the advent of Big Data and agile development methodologies, NoSQL databases have gained a lot of relevance in the database landscape. Their main advantage is the ability to effectively handle scalability and flexibility issues raised by modern applications.

You will start by learning the history and the basics of NoSQL databases and discover their key characteristics and benefits. You will learn about the four categories of NoSQL databases and how they differ from each other. You will explore the architecture and features of several different implementations of NoSQL databases, namely MongoDB, Cassandra, and IBM Cloudant. You will then get hands-on experience using those NoSQL databases to perform standard database management tasks, such as creating and replicating databases, loading and querying data, modifying database permissions, indexing and aggregating data, and sharding (or partitioning) data.

Curso2

Curso 2

Introduction to Big Data with Spark and Hadoop

4.2

estrellas

97 calificaciones

•

18 reseña

Bernard Marr defines Big Data as the digital trace that we are generating in this digital era. In this course, you will learn about the characteristics of Big Data and its application in Big Data Analytics. You will gain an understanding about the features, benefits, limitations, and applications of some of the Big Data processing tools. You’ll explore how Hadoop and Hive help leverage the benefits of Big Data while overcoming some of the challenges it poses.

Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hive, a data warehouse software, provides an SQL-like interface to efficiently query and manipulate large data sets residing in various databases and file systems that integrate with Hadoop. Apache Spark is an open-source processing engine that provides users new ways to store and make use of big data. It is an open-source processing engine built around speed, ease of use, and analytics. In this course, you will discover how to leverage Spark to deliver reliable insights. The course provides an overview of the platform, going into the different components that make up Apache Spark. In this course, you will also learn about Resilient Distributed Datasets, or RDDs, that enable parallel processing across the nodes of a Spark cluster.

Curso3

Curso 3

Data Engineering and Machine Learning using Spark

3.8

estrellas

48 calificaciones

•

17 reseña

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.

In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering. The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case. NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one.

Instructores

ofrecido por

IBM

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

Preguntas Frecuentes

¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Puedo inscribirme en un solo curso?
¡Sí! Para empezar, haz clic en la tarjeta del curso que te interesa e inscríbete. Puedes inscribirte y completar el curso para obtener un certificado que puedes compartir o puedes acceder al curso como oyente para ver los materiales del curso de manera gratuita. Cuando cancelas la suscripción de un curso que forma parte de un programa especializado, se cancela automáticamente la suscripción de todo el programa especializado. Visita el panel del estudiante para realizar un seguimiento de tu progreso.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
¿Puedo tomar este curso de manera gratuita?
Cuando te inscribes en el curso, tienes acceso a todos los cursos del programa especializado y obtienes un certificado cuando completas el trabajo. Si solo deseas leer y ver el contenido del curso, puedes participar del curso como oyente de manera gratuita. Si no puedes pagar la tarifa, puedes solicitar ayuda económica.
¿Este curso es 100 % en línea? ¿Necesito asistir a alguna clase en persona?
Este curso es completamente en línea, de modo que no necesitas ir a un aula en persona. Puedes acceder a tus lecciones, lecturas y tareas en cualquier momento y cualquier lugar a través de Internet o tu dispositivo móvil.
¿Cuánto tiempo se necesita para completar un programa especializado?
The specialization requires 36-42 hours of effort to complete. Working 6-8 hours a week, it can be completed within 1-2 months. Working 3-4 hours a week, it can be completed in 4-6 months.
What background knowledge is necessary?
Basic computer literacy, a grounding in IT systems, working experience with one or more Operating Systems, and programming languages such as Python, data literacy skills, some knowledge of SQL, and a willingness to self-learn online. No prior knowledge of Big Data or NoSQL is required.
Do I need to take the courses in a specific order?
It is recommended that you complete the courses in the order in which they occur in the Specialization. Course 2 is a pre-requisite for Course 3.
¿Recibiré crédito universitario por completar el programa especializado?
University credit is currently not available for this Specialization.
What will I be able to do upon completing the Specialization?
Upon successful completion of the Specialization, you will have the practical knowledge and experience to start tackling Data Engineering tasks involving NoSQL Databases, Big Data and Apache Spark.

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