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所在平台: Udemy |
课程主页: https://www.udemy.com/course/spark-for-data-science-with-python/
课程评论：没有评论
课程名称：从0到1：使用Python进行数据科学的Spark 课程概述：该课程由一支四人团队授课，包括两位斯坦福大学毕业的前谷歌员工和两位前Flipkart首席分析师。这个团队在使用Java和处理数十亿行数据方面拥有数十年的实践经验。通过使用Spark进行分析、机器学习和数据科学，使您的数据飞起来。课程将介绍Spark的概念。如果您是一名分析师或数据科学家，您可能习惯于使用多种系统处理数据，如SQL、Python、R和Java等。而使用Spark，您可以在单一引擎上探索和处理大量数据，运行机器学习算法，并使用同一系统将您的代码投入生产。 数据分析：通过Spark和Python，您可以在互动环境中快速分析和探索数据。课程将展示如何利用RDD和Dataframe的强大功能轻松处理数据。 机器学习和数据科学：Spark的核心功能和内置库使实现复杂算法（如推荐系统）变得简单，只需极少的代码。我们将涵盖多种数据集和算法，包括PageRank、MapReduce和图数据集。 课程内容：课程涉及许多有趣的主题，包括： - 使用交替最小二乘法和Audioscrobbler数据集的音乐推荐 - 使用Dataframes和Spark SQL处理Twitter数据 - 利用PageRank算法处理Google网页图数据集 - 使用Spark Streaming进行流处理 - 使用Marvel社交网络数据集处理图数据 - 以及所有Spark的基本和高级特性：弹性分布式数据集（RDDs）、转换（map、filter、flatMap）、操作（reduce、aggregate）、成对RDD、reduceByKey、combineByKey、广播和累加器变量、Spark的MapReduce、Spark的Java API、Spark SQL、Spark Streaming、MLlib和GraphFrames（GraphX for Python）。 通过本课程，您将掌握使用Spark进行数据科学和机器学习的技能，提高您的数据处理和分析能力。
Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. Get your data to fly using Spark for analytics, machine learning and data science Let's parse that. 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. What's Covered: Lot's of cool stuff.. Music Recommendations using Alternating Least Squares and the Audioscrobbler datasetDataframes and Spark SQL to work with Twitter dataUsing the PageRank algorithm with Google web graph datasetUsing Spark Streaming for stream processing Working with graph data using the Marvel Social network dataset .. and of course all the Spark basic and advanced features: Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate) Pair RDDs , reduceByKey, combineByKey Broadcast and Accumulator variables Spark for MapReduce The Java API for Spark Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python)
