Fundamentals of Scalable Data Science

所在平台: Coursera

课程主页: https://www.coursera.org/learn/ds

课程评论:没有评论

第一个写评论        关注课程

课程简介

课程名称:可扩展数据科学基础 课程概述: 本课程专注于Apache Spark,这是大规模数据处理的事实标准。它是IBM高级数据科学专业化系列课程的第一门课程,我们认为学习一个可扩展的数据科学平台对于成功至关重要,因为内存和CPU限制是构建高级机器学习模型时面临的主要障碍之一。在课程前两周,我们将介绍Apache Spark的基本概念,并在后两周学习如何应用其进行基本的探索性数据分析和预处理任务。在这个过程中,您还将接触到基本的统计度量和数据可视化技术。 完成本课程后,您将具备在现代环境中担任数据工程师的能力,同时也为向数据科学领域的进一步发展奠定基础。 课程目标: - 描述基本统计度量如何揭示数据中的模式 - 识别数据特征、模式、趋势、偏差或不一致性及潜在异常值 - 确定处理大数据的有效技术,如降维和特征选择方法 - 使用高级工具和图表库: - 提高大数据分析的效率,通过分区和并行分析 - 以多种2D和3D格式可视化数据(如箱形图、运行图、散点图、帕累托图和多维标定图) 先决条件: - 具备基本的Python编程技能 - 具备基本数学知识 - 具备基本SQL知识(如果需要,可以轻松通过以下链接获取:https://www.coursera.org/learn/sql-data-science) 课程将使用以下技术: - Jupyter Notebooks(由IBM Watson Studio免费提供) - Apache Spark(由IBM Watson Studio免费提供) - Python 如果发现课程内容过于高级,可以先查看以下材料,这些材料对许多学习者有帮助: - https://cognitiveclass.ai/learn/spark - https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68 课程时长为四周,每周需要4-6小时学习时间。 课程大纲: 1. 课程介绍及评分环境 2. 支持大数据解决方案的工具 3. 基于Apache Spark的统计学数学扩展 4. 大数据的数据可视化 完成此课程后,您将获得Coursera的课程证书,并有机会获得IBM数字徽章,想了解更多关于IBM数字徽章的信息,请访问:ibm.biz/badging。

课程大纲

Part: 1

Title:Introduction the course and grading environment

Description:

Part: 2

Title:Tools that support BigData solutions

Description:

Part: 3

Title:Scaling Math for Statistics on Apache Spark

Description:

Part: 4

Title:Data Visualization of Big Data

Description:

课程评论(0条)

课程详情

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies. This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science. Please have a look at the full specialization curriculum: https://www.coursera.org/specializations/advanced-data-science-ibm If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging. After completing this course, you will be able to: • Describe how basic statistical measures, are used to reveal patterns within the data • Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. • Identify useful techniques for working with big data such as dimension reduction and feature selection methods • Use advanced tools and charting libraries to: o improve efficiency of analysis of big-data with partitioning and parallel analysis o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling) For successful completion of the course, the following prerequisites are recommended: • Basic programming skills in python • Basic math • Basic SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed) In order to complete this course, the following technologies will be used: (These technologies are introduced in the course as necessary so no previous knowledge is required.) • Jupyter notebooks (brought to you by IBM Watson Studio for free) • ApacheSpark (brought to you by IBM Watson Studio for free) • Python We've been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we've been reported that this really helps. Of course, you can give this course a try first and then in case you need, take the following courses / materials. It's free... https://cognitiveclass.ai/learn/spark https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68 This course takes four weeks, 4-6h per week

课程标签

0人关注该课程

主题相关的课程