Über dieses Spezialisierung

Kurse, die komplett online stattfinden

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexibler Zeitplan

Festlegen und Einhalten flexibler Termine.

Stufe „Mittel“

Ca. 8 Monate zum Abschließen

Empfohlen werden 8 Stunden/Woche

Englisch

Untertitel: Englisch, Koreanisch

Kompetenzen, die Sie erwerben

Apache HadoopRecommender SystemsMapreduceApache Spark

Kurse, die komplett online stattfinden

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexibler Zeitplan

Festlegen und Einhalten flexibler Termine.

Stufe „Mittel“

Ca. 8 Monate zum Abschließen

Empfohlen werden 8 Stunden/Woche

Englisch

Untertitel: Englisch, Koreanisch

So funktioniert das Spezialisierung

Kurse absolvieren

Eine Coursera-Spezialisierung ist eine Reihe von Kursen, in denen Sie eine Kompetenz erwerben. Um zu beginnen, melden Sie sich direkt für die Spezialisierung an oder überprüfen Sie deren Kurse und wählen Sie denjenigen Kurs aus, mit dem Sie beginnen möchten. Wenn Sie einen Kurs abonnieren, der Bestandteil einer Spezialisierung ist, abonnieren Sie automatisch die gesamte Spezialisierung Es ist in Ordnung, wenn Sie nur einen Kurs absolvieren möchten — Sie können Ihren Lernprozess jederzeit unterbrechen oder Ihr Abonnement kündigen. Gehen Sie zu Ihrem Kursteilnehmer-Dashboard, um Ihre Kursanmeldungen und Ihren Fortschritt zu verfolgen.

Praxisprojekt

Jede Spezialisierung umfasst ein Praxisprojekt. Sie müssen das Projekt/die Projekte erfolgreich abschließen, um die Spezialisierung abzuschließen und Ihr Zertifikat zu erwerben. Wenn die Spezialisierung einen separaten Kurs für das Praxisprojekt umfasst, müssen Sie zunächst alle anderen Kurse abschließen, bevor Sie damit beginnen können.

Zertifikat erwerben

Wenn Sie alle Kurse und das Praxisprojekt abgeschlossen haben, erhalten Sie ein Zertifikat, dass Sie für potenzielle Arbeitgeber und Ihr berufliches Netzwerk freigeben können.

how it works

Es gibt 5 Kurse in dieser Spezialisierung

Kurs1

Big Data Essentials: HDFS, MapReduce and Spark RDD

4.0
319 Bewertungen
86 Bewertungen
Have you ever heard about such technologies as HDFS, MapReduce, Spark? Always wanted to learn these new tools but missed concise starting material? Don’t miss this course either! In this 6-week course you will: - learn some basic technologies of the modern Big Data landscape, namely: HDFS, MapReduce and Spark; - be guided both through systems internals and their applications; - learn about distributed file systems, why they exist and what function they serve; - grasp the MapReduce framework, a workhorse for many modern Big Data applications; - apply the framework to process texts and solve sample business cases; - learn about Spark, the next-generation computational framework; - build a strong understanding of Spark basic concepts; - develop skills to apply these tools to creating solutions in finance, social networks, telecommunications and many other fields. Your learning experience will be as close to real life as possible with the chance to evaluate your practical assignments on a real cluster. No mocking, a friendly considerate atmosphere to make the process of your learning smooth and enjoyable. Get ready to work with real datasets alongside with real masters! Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting....
Kurs2

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

4.0
107 Bewertungen
20 Bewertungen
No doubt working with huge data volumes is hard, but to move a mountain, you have to deal with a lot of small stones. But why strain yourself? Using Mapreduce and Spark you tackle the issue partially, thus leaving some space for high-level tools. Stop struggling to make your big data workflow productive and efficient, make use of the tools we are offering you. This course will teach you how to: - Warehouse your data efficiently using Hive, Spark SQL and Spark DataFframes. - Work with large graphs, such as social graphs or networks. - Optimize your Spark applications for maximum performance. Precisely, you will master your knowledge in: - Writing and executing Hive & Spark SQL queries; - Reasoning how the queries are translated into actual execution primitives (be it MapReduce jobs or Spark transformations); - Organizing your data in Hive to optimize disk space usage and execution times; - Constructing Spark DataFrames and using them to write ad-hoc analytical jobs easily; - Processing large graphs with Spark GraphFrames; - Debugging, profiling and optimizing Spark application performance. Still in doubt? Check this out. Become a data ninja by taking this course! Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting....
Kurs3

Big Data Applications: Machine Learning at Scale

3.8
62 Bewertungen
14 Bewertungen
Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system. As a practical assignment, you will - build and apply linear models for classification and regression tasks; - learn how to work with texts; - automatically construct decision trees and improve their performance with ensemble learning; - finally, you will build your own recommender system! With these skills, you will be able to tackle many practical machine learning tasks. We provide the tools, you choose the place of application to make this world of machines more intelligent. Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting....
Kurs4

Big Data Applications: Real-Time Streaming

3.1
9 Bewertungen
3 Bewertungen
There is a significant number of tasks when we need not just to process an enormous volume of data but to process it as quickly as possible. Delays in tsunami prediction can cost people’s lives. Delays in traffic jam prediction cost extra time. Advertisements based on the recent users’ activity are ten times more popular. However, stream processing techniques alone are not enough to create a complete real-time system. For example to create a recommendation system we need to have a storage that allows to store and fetch data for a user with minimal latency. These databases should be able to store hundreds of terabytes of data, handle billions of requests per day and have a 100% uptime. NoSQL databases are commonly used to solve this challenging problem. After you finish this course, you will master stream processing systems and NoSQL databases. You will also learn how to use such popular and powerful systems as Kafka, Cassandra and Redis. To get the most out of this course, you need to know Hadoop and SQL. You should also have a working knowledge of bash, Python and Spark. Do you want to learn how to build Big Data applications that can withstand modern challenges? Jump right in!...
Kurs5

Big Data Services: Capstone Project

Are you ready to close the loop on your Big Data skills? Do you want to apply all your knowledge you got from the previous courses in practice? Finally, in the Capstone project, you will integrate all the knowledge acquired earlier to build a real application leveraging the power of Big Data. You will be given a task to combine data from different sources of different types (static distributed dataset, streaming data, SQL or NoSQL storage). Combined, this data will be used to build a predictive model for a financial market (as an example). First, you design a system from scratch and share it with your peers to get valuable feedback. Second, you can make it public, so get ready to receive the feedback from your service users. Real-world experience without any 3G-glasses or mock interviews....

Dozenten

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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

Partner in der Branche

Industry Partner Logo #0

Über 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....

Häufig gestellte Fragen

  • Ja! Um loszulegen, klicken Sie auf die Kurskarte, die Sie interessiert, und melden Sie sich an. Sie können sich anmelden und den Kurs absolvieren, um ein teilbares Zertifikat zu erwerben, oder Sie können als Gast teilnehmen, um die Kursmaterialien gratis einzusehen. Wenn Sie einen Kurs abonnieren, der Teil einer Spezialisierung ist, abonnieren Sie automatisch die gesamte Spezialisierung. Auf Ihrem Kursteilnehmer-Dashboard können Sie Ihren Fortschritt verfolgen.

  • Dieser Kurs findet ausschließlich online statt, Sie müssen also zu keiner Sitzung persönlich erscheinen. Sie können jederzeit und überall über das Netz oder Ihr Mobilgerät auf Ihre Vorträge, Lektüren und Aufgaben zugreifen.

  • 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.

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