關於此 專項課程

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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.

學生職業成果
67%
完成此 專項課程 後開始了新的職業。
33%
加薪或升職。
可分享的證書
完成後獲得證書
100% 在線課程
立即開始,按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
中級
完成時間大約為5 個月
建議 3 小時/週
英語(English)
字幕:英語(English), 韓語
學生職業成果
67%
完成此 專項課程 後開始了新的職業。
33%
加薪或升職。
可分享的證書
完成後獲得證書
100% 在線課程
立即開始,按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
中級
完成時間大約為5 個月
建議 3 小時/週
英語(English)
字幕:英語(English), 韓語

此專項課程包含 4 門課程

課程1

課程 1

大规模数据处理:系统与算法

4.3
729 個評分
158 條評論
課程2

課程 2

实用预测分析:模型与方法

4.1
293 個評分
56 條評論
課程3

課程 3

Communicating Data Science Results

3.6
131 個評分
36 條評論
課程4

課程 4

大规模数据科学 - 毕业项目

4.1
21 個評分
5 條評論

提供方

华盛顿大学 徽標

华盛顿大学

常見問題

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 5 months.

  • Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over.

  • We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.

  • Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • You will have experience working independently on data science challenges, analyzing real data sources on and off the web, potentially at terabyte-scale. You will be poised to pursue deeper technical study in software systems, scalable algorithms, statistics, machine learning, and visualization.

  • Learners will need intermediate programming experience (roughly equivalent to two college courses) and some familiarity with databases. Programming assignments throughout the Specialization will use a combination of Python, SQL, Scala, R, and Javascript; familiarity with one or more of these languages will be helpful.

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