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所在平台: Udemy |
课程主页: https://www.udemy.com/course/sglearnfrom-0-to-1-spark-for-data-science-with-python/
课程评论：没有评论
课程名称：SGLearn@From 0 to 1: Spark for Data Science with Python 课程概述： 欢迎来到针对新加坡学习者的SGLearn系列课程。本课程是Janani Ravi及其团队同名课程的改编版，特别为新加坡学习者制作。新加坡公民有资格申请CITREP+资金计划，具体条款和条件适用。 课程团队由4名成员组成，包括2位斯坦福大学毕业、曾在谷歌工作的专业人士和2位曾任Flipkart主要分析师的专家。他们在Java和大数据处理方面拥有数十年的实践经验。利用Spark实现数据分析、机器学习和数据科学的飞速发展。 课程内容包括： - Spark简介：作为分析师或数据科学家，您可能习惯使用多个系统来处理数据。Spark提供了一个单一引擎，可以对大量数据进行探索、运行机器学习算法，并能将代码投入生产。 - 数据分析：使用Spark和Python在交互环境中快速反馈地分析和探索数据，课程将展示如何利用RDD和DataFrame轻松操作数据。 - 机器学习与数据科学：Spark的核心功能和内置库使得实现复杂算法（如推荐系统）变得非常简单。将覆盖多种数据集和算法，包括PageRank、MapReduce和图数据集。 课程涵盖的内容： - 使用交替最小二乘法的音乐推荐（Audioscrobbler数据集） - 使用DataFrame和Spark SQL处理Twitter数据 - 使用PageRank算法处理Google网页图数据集 - 使用Spark流处理进行流式处理 - 使用Marvel社交网络数据集处理图数据 - 以及所有Spark的基本和高级功能，包括弹性分布式数据集（RDDs）、转换（map、filter、flatMap）、动作（reduce、aggregate）、成对RDD、reduceByKey、combineByKey、广播变量和累加变量。 讨论论坛： 请利用本课程的讨论论坛与其他学生互动，互相帮助。由于资源有限，Loonycorn团队不能回应学生的个别问题，但我们会尽力保持课程的质量并以极低的价格提供高质量的课程。非常感谢您的理解和耐心！
Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies. This course is an adaptation of the same course by Janani Ravi and the team and is specially produced in collaboration with Janani for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP+ funding scheme, terms and conditions apply. _____________ Note from the team.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) Using discussion forumsPlease use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(We're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.It is a hard trade-off.Thank you for your patience and understanding!
