About this Course
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다음 전문 분야의 5개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 71시간 필요

권장: 6 weeks of study, 6-8 hours/week...

영어

자막: 영어, 한국어

귀하가 습득할 기술

GraphsHiveApache HiveApache Spark
Course을(를) 수강하는 학습자
  • Data Engineers
  • Data Scientists
  • Technical Leads
  • Data Analysts
  • Technical Solutions Engineers

다음 전문 분야의 5개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 71시간 필요

권장: 6 weeks of study, 6-8 hours/week...

영어

자막: 영어, 한국어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 22분 필요

Welcome to the Second Course: Big Data Analysis

8개 동영상 (총 12분), 1 reading
8개의 동영상
What is BigData Analysis?1m
Tools For BigData Analysis1m
Graph Data Analysis2m
Meet Alexey Dral2m
Meet Pavel Mezentsev37
Meet Natalia Pritykovskaya40
Meet Pavel Klemenkov40
1개의 읽기 자료
Slack Channel is the quickest way to get answers to your questions10m
완료하는 데 3시간 필요

Big Data SQL: Hive

15개 동영상 (총 105분), 3 quizzes
15개의 동영상
HTTP Web Service: Access Log Format4m
Business Use Cases: Solution with Hive6m
(optional) SQL: likbez10m
Hive Data Definition Language (DDL)11m
Hive Data Manipulation Language (DML)6m
Hive Analytics: RegexSerDe, Views7m
(optional) Regular Expressions, Likbez9m
Hive Analytics: UDF, UDAF, UDTF7m
Hive Streaming4m
Hive PTF (Window Functions)5m
Hive Optimization: Partitioning, Bucketing and Sampling8m
Hive Map-Side Joins: Plain, Bucket, Sort-Merge5m
Hive Optimization: Data Skew4m
Hive Optimization: Row-Columnar File Formats, Compression8m
3개 연습문제
Hive: SQL over Hadoop MapReduce20m
Hive Analytics with UDF and Streaming20m
Hive final20m
2
완료하는 데 6시간 필요

Big Data SQL: Hive (practice week)

3개 동영상 (총 11분), 4 readings, 5 quizzes
3개의 동영상
How to Install Docker on Windows 7, 8, 104m
How to submit your first Hadoop assignment3m
4개의 읽기 자료
Assignments. General requirements10m
Hive assignment. Intro and instructions10m
Grading System: Instructions and Common Problems10m
Docker Installation Guide10m
3
완료하는 데 2시간 필요

Spark SQL and Spark Dataframe

14개 동영상 (총 82분), 2 quizzes
14개의 동영상
What is Pandas DataFrame and how to create it4m
How to process a DataFrame as SQL4m
Working with Hive4m
Reading and Writing Files7m
RDD vs. DF vs. SQL3m
Projection and Filtering5m
Functions5m
Aggregates6m
Join8m
User Defined Functions8m
Time Processing4m
Window Functions7m
Two-Dimensional Distributions4m
2개 연습문제
Introducing DataFrame and SQL16m
Spark SQL and Spark Dataframe18m
4
완료하는 데 4시간 필요

Graph Analysis from Big Data Perspective

13개 동영상 (총 83분), 5 quizzes
13개의 동영상
Graph representation7m
Counting common friends. Part I2m
Counting common friends. Part II10m
Counting common friends. Part III5m
GraphFrames: Introduction6m
Motif Finding: DSL6m
Motif Finding: Counting Mutual Friends6m
Motif Finding: Under The Hood. Part 114m
Motif Finding: Under The Hood. Part 24m
Triangles Count: Introduction3m
Triangles Count: Edge Lists6m
Triangles Count: GraphFrame6m
4개 연습문제
Graph Representations10m
Motif Finding18m
Triangles Count8m
Graph Analysis from Big Data Perspective20m
4.1
29개의 리뷰Chevron Right

33%

이 강좌를 수료한 후 새로운 경력 시작하기

25%

이 강좌를 통해 확실한 경력상 이점 얻기

Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames의 최상위 리뷰

대학: SMNov 13th 2018

content of the course is remarkable and the way they explained concepts is very lucid. I just want to give suggestions please give link to the data set they are using for illustrating the concepts.

대학: SSFeb 3rd 2018

I wish I could give more rating than 5 :). Excellent course. Thanks so much for such an excellent course. All the instructors are great.

강사

Avatar

Alexey A. Dral

Founder and Chief Executive Officer
BigData Team
Avatar

Pavel Klemenkov

Chief Data Scientist
NVIDIA

Yandex 정보

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....

Big Data for Data Engineers 전문 분야 정보

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....
Big Data for Data Engineers

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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