Q: Will I earn university credit for completing the Specialization?

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.

Question 1

How long does it take to complete the Specialization?

Accepted Answer

Plan for 13–14 weeks at 3–4 hours per week (≈40–56 total learning hours). A practical pace is: Weeks 1–3 – Octave foundations and visualization (Course 1); Weeks 4–6 – intermediate Octave for ML workflows (Course 2); Weeks 7–9 – advanced plotting, scripting, and control flow (Course 3); Weeks 10–12 – functions, modular design, and numerical computing (Course 4); Weeks 13–14 – logistic regression in R plus applied projects (Course 5). This cadence leaves time for hands-on practice and mini-projects (e.g., diabetes prediction and credit-risk scoring) so you finish with job-ready, portfolio evidence in GNU Octave, data analysis, and machine learning.

Question 2

What background knowledge is necessary?

Accepted Answer

Learners should have a basic understanding of mathematics (algebra and statistics) and some familiarity with programming concepts. Prior exposure to Python, MATLAB, or Octave is helpful but not required, as the courses start with beginner-friendly foundations.

Question 3

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

Accepted Answer

Yes. The specialization is designed in a progressive sequence, where each course builds on the skills gained in the previous one. Starting with Octave basics and moving through advanced programming, visualization, and applied machine learning ensures a structured learning journey that maximizes mastery.

Question 4

What will I be able to do upon completing the Specialization?

Accepted Answer

By the end of the specialization, you will be able to confidently analyze and visualize data using GNU Octave, build reusable functions and scripts for machine learning workflows, handle time-series and numerical data, and apply logistic regression in R to solve real-world problems in domains such as healthcare, finance, and risk modeling. You will also have a portfolio of applied projects that demonstrate your ability to design, evaluate, and implement machine learning solutions.

Question 5

Is this course really 100% online? Do I need to attend any classes in person?

Accepted Answer

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.

Question 6

What is the refund policy?

Accepted Answer

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.

Question 7

Can I just enroll in a single course?

Accepted Answer

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. 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.

Question 8

Is financial aid available?

Accepted Answer

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Question 9

Can I take the course for free?

Accepted Answer

No, you cannot take this course for free. 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 cannot afford the fee, you can apply for financial aid.

Question 10

Will I earn university credit for completing the Specialization?

Accepted Answer

This Specialization doesn&#x27;t carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.