Displaying 1-4 of 4 result(s).
About Course
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- What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.
- Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.
- Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.
Curriculum
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Section 1: You, This Course and Us |
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Lecture 1 |
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02:15 |
Section 2: Introduction to Spark |
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Lecture 2 |
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08:45 |
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Lecture 3 |
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12:23 |
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Lecture 4 |
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09:39 |
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Lecture 5 |
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15:37 |
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Lecture 6 |
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06:42 |
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Lecture 7 |
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04:50 |
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Lecture 8 |
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13:33 |
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Lecture 9 |
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10:13 |
Section 3: Resilient Distributed Datasets |
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Lecture 10 |
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12:35 |
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Lecture 11 |
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06:06 |
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Lecture 12 |
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11:08 |
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Lecture 13 |
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16:10 |
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Lecture 14 |
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05:50 |
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Lecture 15 |
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05:23 |
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Lecture 16 |
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15:10 |
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Lecture 17 |
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03:26 |
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Lecture 18 |
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06:25 |
Section 4: Advanced RDDs: Pair Resilient Distributed Datasets |
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Lecture 19 |
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14:45 |
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Lecture 20 |
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18:11 |
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Lecture 21 |
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11:53 |
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Lecture 22 |
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04:34 |
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Lecture 23 |
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14:03 |
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Lecture 24 |
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04:58 |
Section 5: Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes |
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Lecture 25 |
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13:35 |
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Lecture 26 |
See it in Action : Using an Accumulator variable
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02:40 |
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Lecture 27 |
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05:58 |
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Lecture 28 |
See it in Action : Running a Python script with Spark-Submit
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03:58 |
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Lecture 29 |
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14:30 |
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Lecture 30 |
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13:44 |
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Lecture 31 |
See it in Action : MapReduce with Spark
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02:05 |
Section 6: Java and Spark |
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Lecture 32 |
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15:58 |
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Lecture 33 |
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04:49 |
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Lecture 34 |
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03:49 |
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Lecture 35 |
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02:20 |
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Lecture 36 |
See it in Action : Running a Spark Job with Java
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05:08 |
Section 7: PageRank: Ranking Search Results |
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Lecture 37 |
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16:44 |
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Lecture 38 |
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06:15 |
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Lecture 39 |
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12:01 |
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Lecture 40 |
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07:27 |
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Lecture 41 |
See it Action : The PageRank algorithm using Spark
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03:46 |
Section 8: Spark SQL |
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Lecture 42 |
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16:04 |
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Lecture 43 |
See it in Action : Dataframes and Spark SQL
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04:49 |
Section 9: MLlib in Spark: Build a recommendations engine |
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Lecture 44 |
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12:19 |
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Lecture 45 |
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11:39 |
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Lecture 46 |
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07:51 |
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Lecture 47 |
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16:05 |
Section 10: Spark Streaming |
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Lecture 48 |
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09:55 |
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Lecture 49 |
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10:54 |
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Lecture 50 |
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09:26 |
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Lecture 51 |
See it in Action : Spark Streaming
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04:17 |
Section 11: Graph Libraries |
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Lecture 52 |
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18:01 |
What you will get from this course?
Who should buy this course?
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- Analysts who want to leverage Spark for analyzing interesting datasets
- Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
- Engineers who want to use a distributed computing engine for batch or stream processing or both.
- The course assumes knowledge of Python. You can write Python code directly in the PySpark shell. If you already have IPython Notebook installed, we'll show you how to configure it for Spark
- For the Java section, we assume basic knowledge of Java. An IDE which supports Maven, like IntelliJ IDEA/Eclipse would be helpful
- All examples work with or without Hadoop. If you would like to use Spark with Hadoop, you'll need to have Hadoop installed (either in pseudo-distributed or cluster mode).
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