Специализация Big Data for Data Engineers

Начинается Oct 16

Специализация Big Data for Data Engineers

Build Your Data Engineering Skills. Learn how to tame the big data beast with the most popular tools assisted by top-notch practitioners

Об этой специализации

This specialization is made for people working with data (either small or big). If you are a Data Analyst, Data Scientist, Data Engineer or Data Architect (or you want to become one) — don’t miss the opportunity to expand your knowledge and skills in the field of data engineering and data analysis on the large scale. In four concise courses you will learn the basics of Hadoop, MapReduce, Spark, methods of offline data processing for warehousing, real-time data processing and large-scale machine learning. And Capstone project for you to build and deploy your own Big Data Service (make your portfolio even more competitive). Over the course of the specialization, you will complete progressively harder programming assignments (mostly in Python). Make sure, you have some experience in it. This course will master your skills in designing solutions for common Big Data tasks: - creating batch and real-time data processing pipelines, - doing machine learning at scale, - deploying machine learning models into a production environment — and much more! Join some of best hands-on big data professionals, who know, their job inside-out, to learn the basics, as well as some tricks of the trade, from them. Special thanks to Prof. Mikhail Roytberg (APT dept., MIPT), Oleg Sukhoroslov (PhD, Senior Researcher, IITP RAS), Oleg Ivchenko (APT dept., MIPT), Pavel Akhtyamov (APT dept., MIPT), Vladimir Kuznetsov, Asya Roitberg, Eugene Baulin, Marina Sudarikova.


5 courses

Следуйте предложенному порядку или выберите свой.


Поможет на практике применить полученные навыки.


Отметьте новые навыки в резюме и на LinkedIn.

Обзор проектов

Intermediate Specialization.
Some related experience required.
  1. 1-Й КУРС

    Big Data Essentials: HDFS, MapReduce and Spark RDD

    6 weeks of study, 6-8 hours/week

    О курсе

    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
  2. 2-Й КУРС

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

    6 weeks of study, 6-8 hours/week

    О курсе

    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.
  3. 3-Й КУРС

    Big Data Applications: Machine Learning at Scale

    5 weeks of study, 6-8 hours/week

    О курсе

    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
  4. 4-Й КУРС

    Big Data Applications: Real-Time Streaming

    Начинается October 2018
    4 weeks of study, 6-8 hours/week

    О курсе

    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 Hive. You should also have a working knowledge of Spark, Spark SQL and Python. Do you want to learn how to build Big Data applications that can withstand modern challenges? Jump right in!
  5. 5-Й КУРС

    Big Data Services: Capstone Project

    Начинается December 2018

    О курсе

    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.


  • 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.

  • Pavel Klemenkov

    Pavel Klemenkov

    Chief Data Scientist
  • Alexey A. Dral

    Alexey A. Dral

    Founder and Chief Executive Officer
  • Pavel Mezentsev

    Pavel Mezentsev

    Senior Data Scientist
  • Ilya Trofimov

    Ilya Trofimov

    Principal Data Scientist
  • Emeli Dral

    Emeli Dral

  • Natalia Pritykovskaya

    Natalia Pritykovskaya

  • Ivan Puzyrevskiy

    Ivan Puzyrevskiy

    Technical Team Lead
  • Vladimir Lesnichenko

    Vladimir Lesnichenko

  • Evgeny Frolov

    Evgeny Frolov

    Data Scientist, PhD Student @Skoltech
  • Evgeniy Riabenko

    Evgeniy Riabenko