Introduction to Big Data faq

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learnersLearners: 1,336
instructor Instructor: Natasha Balac instructor-icon
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Discover the power of Big Data with this introductory course! Learn the fundamentals of the Big Data landscape, the V's of Big Data, and how to use the Hadoop framework to analyze and transform data. No prior programming experience is needed. Get started today and unlock the potential of Big Data!

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Coursera

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

8th May, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [June 30th, 2023]

Introduction to Big Data is a course designed for those new to data science and interested in understanding the Big Data landscape. This course will provide an overview of the terminology and core concepts behind big data problems, applications, and systems. It will also introduce the Hadoop framework, which has made big data analysis easier and more accessible. At the end of this course, students will be able to describe the Big Data landscape, explain the V’s of Big Data, get value out of Big Data, identify big data problems, explain the architectural components and programming models used for scalable big data analysis, and summarize the features and value of core Hadoop stack components. No prior programming experience is necessary, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware and software requirements include a quad core processor, 8 GB RAM, 20 GB disk free, Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+, and VirtualBox 5+.

[Applications]
Upon completion of this course, participants will have a better understanding of the Big Data landscape and the core concepts behind big data problems, applications, and systems. They will be able to identify what are and what are not big data problems and be able to recast big data problems as data science questions. Participants will also be able to explain the V’s of Big Data and get value out of Big Data by using a 5-step process to structure their analysis. Additionally, they will be able to install and run a program using Hadoop.

[Career Paths]
One job position path recommended to learners of this course is Big Data Engineer. Big Data Engineers are responsible for designing, developing, and maintaining the infrastructure and systems that enable the collection, storage, and analysis of large datasets. They must have a deep understanding of the various technologies and tools used in Big Data, such as Apache Hadoop, Apache Spark, and Apache Kafka. They must also be able to develop and maintain data pipelines, create data models, and develop algorithms to process and analyze data.

The development trend of Big Data Engineering is towards automation and scalability. Big Data Engineers must be able to design and develop systems that can scale to handle large datasets and automate the data processing and analysis process. They must also be able to develop systems that can integrate with existing systems and be able to quickly adapt to changing requirements. Additionally, Big Data Engineers must be able to develop systems that are secure and reliable, and that can be easily maintained and monitored.

[Education Paths]
The recommended educational path for learners interested in Big Data is to pursue a Bachelor's degree in Computer Science or a related field. This degree will provide learners with the foundational knowledge and skills needed to understand and work with Big Data. The curriculum typically includes courses in programming, data structures, algorithms, databases, operating systems, computer networks, and software engineering. Additionally, learners may take courses in artificial intelligence, machine learning, and data mining.

The development trend for Big Data is to focus on the use of advanced analytics and machine learning techniques to extract insights from large datasets. This includes the use of predictive analytics, natural language processing, and deep learning. Additionally, the use of cloud computing and distributed computing technologies is becoming increasingly important for Big Data applications. As the demand for Big Data professionals continues to grow, the need for specialized skills and knowledge in these areas will become increasingly important.

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faq FAQ for Big Data Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a free certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: Can I take this course for free?

Yes, this is a free course offered by Coursera, please click the "go to class" button to access more details.

Q4: How many people have enrolled in this course?

So far, a total of 1336 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q5: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
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(Please note that the following steps should be performed on Coursera's official site.)
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Coursera may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Big Data courses and certifications, our extensive collection at azclass.net will help you.

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