課程信息
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第 1 門課程(共 1 門)

100% 在線

立即開始,按照自己的計劃學習。

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高級

完成時間大約為75 小時

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

英語(English)

字幕:英語(English), 韓語

您將獲得的技能

GraphsHiveApache HiveApache Spark

第 1 門課程(共 1 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

完成時間大約為75 小時

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

英語(English)

字幕:英語(English), 韓語

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 22 分鐘

Welcome to the Second Course: Big Data Analysis

...
8 個視頻 (總計 12 分鐘), 1 個閱讀材料
8 個視頻
Graph Data Analysis2分鐘
Meet Alexey Dral2分鐘
Meet Pavel Mezentsev37
Meet Natalia Pritykovskaya40
Meet Pavel Klemenkov40
1 個閱讀材料
Slack Channel is the quickest way to get answers to your questions10分鐘
完成時間為 3 小時

Big Data SQL: Hive

...
15 個視頻 (總計 105 分鐘), 3 個測驗
15 個視頻
(optional) SQL: likbez10分鐘
Hive Data Definition Language (DDL)11分鐘
Hive Data Manipulation Language (DML)6分鐘
Hive Analytics: RegexSerDe, Views7分鐘
(optional) Regular Expressions, Likbez9分鐘
Hive Analytics: UDF, UDAF, UDTF7分鐘
Hive Streaming4分鐘
Hive PTF (Window Functions)5分鐘
Hive Optimization: Partitioning, Bucketing and Sampling8分鐘
Hive Map-Side Joins: Plain, Bucket, Sort-Merge5分鐘
Hive Optimization: Data Skew4分鐘
Hive Optimization: Row-Columnar File Formats, Compression8分鐘
3 個練習
Hive: SQL over Hadoop MapReduce20分鐘
Hive Analytics with UDF and Streaming20分鐘
Hive final20分鐘
2
完成時間為 6 小時

Big Data SQL: Hive (practice week)

...
3 個視頻 (總計 11 分鐘), 4 個閱讀材料, 5 個測驗
4 個閱讀材料
Assignments. General requirements10分鐘
Hive assignment. Intro and instructions10分鐘
Grading System: Instructions and Common Problems10分鐘
Docker Installation Guide10分鐘
3
完成時間為 2 小時

Spark SQL and Spark Dataframe

...
14 個視頻 (總計 82 分鐘), 2 個測驗
14 個視頻
Working with Hive4分鐘
Reading and Writing Files7分鐘
RDD vs. DF vs. SQL3分鐘
Projection and Filtering5分鐘
Functions5分鐘
Aggregates6分鐘
Join8分鐘
User Defined Functions8分鐘
Time Processing4分鐘
Window Functions7分鐘
Two-Dimensional Distributions4分鐘
2 個練習
Introducing DataFrame and SQL16分鐘
Spark SQL and Spark Dataframe18分鐘
4
完成時間為 4 小時

Graph Analysis from Big Data Perspective

...
13 個視頻 (總計 83 分鐘), 5 個測驗
13 個視頻
Counting common friends. Part II10分鐘
Counting common friends. Part III5分鐘
GraphFrames: Introduction6分鐘
Motif Finding: DSL6分鐘
Motif Finding: Counting Mutual Friends6分鐘
Motif Finding: Under The Hood. Part 114分鐘
Motif Finding: Under The Hood. Part 24分鐘
Triangles Count: Introduction3分鐘
Triangles Count: Edge Lists6分鐘
Triangles Count: GraphFrame6分鐘
4 個練習
Graph Representations10分鐘
Motif Finding18分鐘
Triangles Count8分鐘
Graph Analysis from Big Data Perspective20分鐘
4.0
21 個審閱Chevron Right

33%

完成這些課程後已開始新的職業生涯

25%

通過此課程獲得實實在在的工作福利

來自Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames的熱門評論

創建者 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.

創建者 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.

講師

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Pavel Klemenkov

Chief Data Scientist
NVIDIA
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Pavel Mezentsev

Senior Data Scientist
PulsePoint inc
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Alexey A. Dral

Founder and Chief Executive Officer
BigData Team

關於 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....

關於 Big Data for Data Engineers 專項課程

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

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