About this Specialization
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100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approx. 8 months to complete

Suggested 8 hours/week

English

Subtitles: English, Korean

Skills you will gain

Apache HadoopRecommender SystemsMapreduceApache Spark

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approx. 8 months to complete

Suggested 8 hours/week

English

Subtitles: English, Korean

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 5 Courses in this Specialization

Course1

Big Data Essentials: HDFS, MapReduce and Spark RDD

4.0
359 ratings
97 reviews
Course2

Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames

4.0
122 ratings
25 reviews
Course3

Big Data Applications: Machine Learning at Scale

3.8
66 ratings
16 reviews
Course4

Big Data Applications: Real-Time Streaming

3.1
16 ratings
4 reviews

Instructors

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Pavel Klemenkov

Chief Data Scientist
NVIDIA
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Ivan Mushketyk

Software Engineer, ConsenSys
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Evgeny Frolov

Data Scientist, PhD Student @Skoltech
Computational and Data Intensive Science and Engineering
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Ilya Trofimov

Principal Data Scientist
Yandex
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Ivan Puzyrevskiy

Technical Team Lead
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Alexey A. Dral

Founder and Chief Executive Officer
BigData Team
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Pavel Mezentsev

Senior Data Scientist
PulsePoint inc
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Vladislav Goncharenko

DCAM MIPT, Skoltech
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Artyom Vybornov

Team Lead at Rambler&Co

Industry Partners

Industry Partner Logo #0

About Yandex

Yandex is a technology company that builds intelligent products and services powered by machine learning. Our goal is to help consumers and businesses better navigate the online and offline world....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • 6 months

  • - Programming experience in Python. It is required to complete programming assignments.

    - Unix basics. As the technologies covered throughout the specialization operate in Unix environment, we expect you to have basic understanding of the subject. Things like processes and files assumed to be familiar for the learner.

    - Basic linear algebra and probability theory. To grasp the “Big Data Applications: Machine Learning at Scale” course, you should be familiar with math primer or should complete an introductory course on machine learning.

  • It is expected to take course from the first to the last.

  • No, there are no University credits associated with this course

  • You will be able to present your portfolio project (Capstone project) to potential employers.

More questions? Visit the Learner Help Center.