Data Science at Scale Specialization

开始时间: 06/21/2022 持续时间: Approximately 5 months to complete Suggested pace of 2 hours/week

所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew

课程主页: https://www.coursera.org/specializations/data-science

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课程详情

Learn scalable data management, evaluate big data technologies, and design effective visualizations. This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.

大规模专业化的数据科学：学习可扩展的数据管理，评估大数据技术以及设计有效的可视化。 本专业涵盖数据科学的中间主题。您将获得有关可伸缩SQL和NoSQL数据管理解决方案，数据挖掘算法以及实用的统计和机器学习概念的动手经验。您还将学习如何可视化数据并交流结果，并探讨在处理大数据时出现的法律和道德问题。在与数字实习平台Coursolve合作开发的最终Capstone项目中，您将把新技能应用到现实世界的数据科学项目中。

课程大纲

Course: 1

Course Link: https://www.coursera.org/learn/data-manipulation?specialization=data-science

Title:Data Manipulation at Scale: Systems and Algorithms

Description:Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Course: 2

Course Link: https://www.coursera.org/learn/predictive-analytics?specialization=data-science

Title:Practical Predictive Analytics: Models and Methods

Description:Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Course: 3

Course Link: https://www.coursera.org/learn/data-results?specialization=data-science

Title:Communicating Data Science Results

Description:Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit.

Course: 4

Course Link: https://www.coursera.org/learn/datasci-capstone?specialization=data-science

Title:Data Science at Scale - Capstone Project

Description:In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.

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