IBM 数据科学 專業證書

• 退款政策是如何规定的？

  如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。

• 我可以只注册一门课程吗？

  可以！点击您感兴趣的课程卡开始注册即可开始学习。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某证书的一部分，系统会自动为您订阅完整的证书。访问您的学生面板，跟踪您的进度。

• 此课程是 100% 在线学习吗？是否需要现场参加课程？

  此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

• How can I earn my IBM Badge?

  Upon completion of this program, you will receive an email from Coursera with directions on how to claim your IBM Badge through Acclaim. Learn more about IBM Badges 

• What is data science?

  D​ata science is the process of collecting, storing, and analyzing data. Data scientists use data to tell compelling stories to inform business decisions. Learn more about what data science is and what data scientists do in the first course of this Professional Certificate, "What is Data Science?"

• What are some examples of careers in data science?

  An understanding of data science and the ability to make data driven decisions is useful in any career, but some careers specifically require a data science background. Some examples of careers in data science include: 

  -​ Business Intelligence Analyst

  -​ Data Analyst

  -​ Data Architect

  -​ Data Engineer

  -​ Data Scientist

  -​ Machine Learning Engineer

  -​ Marketing Analyst

  -​ Operations Analyst

  -​ Quantitative Analyst

• How long does it take to complete the Professional Certificate?

  The Professional Certificate requires completion of 9 courses. Each course typically contains 3-6 modules with an average effort of 2 to 4 hours per module. If learning part-time (e.g. 1 module per week), it would take 6 to 12 months to complete the entire certificate. If learning full-time (e.g. 1 module per day) the certificate can be completed in 2 to 3 months.

• What background knowledge do I need for this program?

  This Professional Certificate is open for anyone with any job and academic background. No prior computer programming experience is necessary, but is an asset, as are familiarity working with computers, high school math, and communication and presentation skills. For the last few courses, knowledge of calculus and linear algebra is an asset but not an absolute requirement.

• Do I need to take the courses in a specific order?

  Yes, it is highly recommended to take the courses in the order they are listed, as they progressively build on concepts taught in previous courses. For example the Data Analysis, Data Visualization, and Machine Learning courses require knowledge of Python covered earlier in the program.

• Will I earn university credit for completing the Professional Certificate?

  No, there is no university credit involved with taking these courses.

• What will I be able to do upon completing the Professional Certificate?

  Become job ready for a career in Data Science. Develop practical skills using hands-on labs in Cloud environments, projects and captsones.

• I already completed some of the other courses in this Professional Certificate. Will I get "credit" for them?

  If you have already completed some of the courses in this Professional Certificate, either individually or as part of another Specialization, they will be marked as "Complete". You do not have to take those courses again, and will be able to finish the Professional Certificate more quickly. You will only need to complete the courses that you have not yet completed.

• I have already completed the Introduction to Data Science Specialization. Can I still enroll in this Professional Certificate?

  Yes, absolutely. Any courses that you have already completed as part of that Specialization will be marked as "Complete". You do not have to take those courses again and will be able to finish the Professional Certificate more quickly.

• Which program should I enroll in - the Introduction to Data Science Specialization, or this Professional Certificate?

  This Professional Certificate consists of 9 courses. The "Introduction to Data Science" Specialization has 4 courses, all of which are also included in this Professional Certificate.

  If you are unsure about your ability to commit to the level of effort and time required to complete this Professional Certificate, we recommend starting with the Introduction to Data Science Specialization, which has fewer courses. If, after completing the Specialization, you are still determined to continue building your data science skills, you can then enroll for this Professional Certificate and then just complete the courses that are not in the Specialization.

• I have already completed the Applied Data Science Specialization. Can I still enroll in this Professional Certificate?

  Yes, absolutely. Any courses that you have already completed as part of that Specialization will be marked as "Complete". You do not have to take those courses again and will be able to finish this Professional Certificate more quickly.

• How can I access job opportunities with IBM and other organizations after completing this Professional Certificate?

  As a Coursera learner who completes this Professional Certificate, you will have special access to join IBM’s Talent Network. Our Talent Network members receive all of the tools you need to land a dream job with IBM - sent directly to your inbox! You will get job opportunities as soon as they are posted, recommendations to apply matched directly to your skills and interests, and tips and tricks to help you stand apart from the crowd.

• How do you share your proof of completion with the educational institutions for transferring credit?

  To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credlybadge, which contains the ACE®️credit recommendation.  Once claimed, you will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed. 

• Where can I find more information on ACE credit recommendations?

  Please see Coursera’s ACE Recommendations FAQ.

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