Data Scientist Nanodegree
by Udacity Team · Udacity
Our Verdict
Worth it — with caveatsUdacity's Data Scientist Nanodegree (ND025) is a strong, genuinely project-based program that earns its place if you can use it for under a month or land a scholarship, but it is hard to justify at full price given the same material is freely available elsewhere. Based on the official syllabus, it bundles six courses (18 lessons) culminating in four real portfolio projects: a data-science blog post, an employee-performance dashboard, an end-to-end scikit-learn/NLP pipeline, and an IBM Watson recommendation system. Its standout feature is human, reviewed deliverables plus mentor feedback, the differentiator most free MOOCs lack. The honest catch is the subscription pricing (USD 249/month) and a thin 2-day refund window, plus recurring complaints about slow support, occasional workspace instability, and inconsistent mentor turnaround. Independent aggregate sentiment is positive (Course Report rates Udacity 4.7/5 across 627 reviews), and the program is best treated as a structured project gym rather than a resume credential by itself.
The curriculum is real, current (updated Mar 2026), and project-heavy with mentor-reviewed deliverables, which is the main reason to pay over a free course. But at USD 249/month with only a 2-day refund window, value hinges entirely on finishing fast or getting a discount/scholarship. It assumes you already know Python, pandas, matplotlib, and linear regression, so it is not an entry point, and reviewers widely note the certificate carries limited weight on a resume by itself.
Best for: Working professionals or career-switchers who already know Python, pandas, NumPy, matplotlib, basic statistics, and linear regression, and who want guided, mentor-reviewed projects to build a portfolio fast. It suits visual learners who prefer short, well-produced videos, people who can commit to finishing in 1-2 months to control cost, and anyone who values structured deadlines plus human feedback over self-paced free courses.
Skip if: Complete beginners with no Python or math background (the prerequisites are real and enforced by difficulty), learners on a tight budget who cannot finish quickly or qualify for a scholarship, anyone seeking an academically rigorous or theory-deep ML treatment (Stanford-style), and people expecting the certificate alone to land a job. Those who want the same project practice for free should start with the open courses below instead.
About This Course
Master data science skills including software engineering, data engineering, experimentation, and ML with real-world projects.
What You'll Learn
Curriculum
Program orientation, how the Nanodegree works, and how to get help.
The data science process, supervised ML algorithms, model evaluation, interpretability and fairness, and stakeholder communication. Project: Data Science Blog Post.
Object-oriented programming, code reproducibility, and data science dashboards. Project: an employee-performance monitoring Data Science Dashboard.
Scikit-learn pipelines, computer vision pipelines, and NLP pipelines. Project: a Data Science Pipeline predicting customer product recommendations.
Clustering, dimensionality reduction, recommendation systems, and unsupervised model evaluation. Project: a Recommendation System on IBM Watson technical articles.
Closing course and next steps after completing the four projects.
Prerequisites
- Python programming proficiency
- Pandas and NumPy for data manipulation
- Data visualization with matplotlib
- Linear regression and basic supervised ML concepts
- Matrix operations / basic linear algebra
- Git and GitHub for version control
- Fluent written and spoken English (per Udacity prerequisites)
Instructor
Udacity Team
Instructor · Udacity
Pros & Cons
Pros
- Four genuinely portfolio-grade, real-data projects (blog post, dashboard, ML pipeline, IBM Watson recommender) rather than toy exercises
- Human mentor feedback and personalized project reviews against rubrics, the main thing free MOOCs lack
- Strong software-engineering emphasis (OOP, reproducibility, unit testing, dashboards) that many pure-ML courses skip
- Content is in-house produced, current (syllabus updated March 2026), and well-paced with short videos suited to visual learners
- One subscription unlocks Udacity's full catalog of ~100 Nanodegree programs, so motivated learners can do more in the same month
Cons
- Expensive at USD 249/month with only a 2-day refund window (14 days for EU residents), so value collapses if you do not finish quickly
- Much of the underlying material is freely available elsewhere; reviewers question whether content alone justifies the price
- Recurring complaints about slow customer support (72+ hour responses), occasional workspace/session instability, and inconsistent mentor turnaround
- Assumes real prerequisites (Python, pandas, matplotlib, linear regression) and is not suitable for true beginners; the certificate carries limited standalone resume value
Alternatives To Consider
Frequently Asked Questions
Is Data Scientist Nanodegree free?
Data Scientist Nanodegree is $249/mo. USD 249/month, or USD 846 for a 4-month bundle (about 15% off); a single subscription unlocks Udacity's full catalog. Refunds only within 2 days of purchase (14 days for EU residents). Udacity offers free preview/standalone courses and periodic scholarships (with Google, IBM, Amazon, Microsoft) but accepts no federal/state financial aid. To control cost, finish in one billing month or secure a discount/scholarship.
Who is Data Scientist Nanodegree for?
Working professionals or career-switchers who already know Python, pandas, NumPy, matplotlib, basic statistics, and linear regression, and who want guided, mentor-reviewed projects to build a portfolio fast. It suits visual learners who prefer short, well-produced videos, people who can commit to finishing in 1-2 months to control cost, and anyone who values structured deadlines plus human feedback over self-paced free courses.
What will you learn in Data Scientist Nanodegree?
The end-to-end data science process (CRISP-DM) and communicating results to stakeholders via a data-science blog post; Supervised machine learning algorithms, model evaluation, and model interpretability and fairness; Software engineering for data scientists: object-oriented programming, code reproducibility, unit testing, and building data dashboards; Building production-style data science pipelines with scikit-learn, including NLP and computer-vision pipelines.
What are the prerequisites for Data Scientist Nanodegree?
Python programming proficiency; Pandas and NumPy for data manipulation; Data visualization with matplotlib; Linear regression and basic supervised ML concepts; Matrix operations / basic linear algebra; Git and GitHub for version control; Fluent written and spoken English (per Udacity prerequisites).
Is Data Scientist Nanodegree worth it?
The curriculum is real, current (updated Mar 2026), and project-heavy with mentor-reviewed deliverables, which is the main reason to pay over a free course. But at USD 249/month with only a 2-day refund window, value hinges entirely on finishing fast or getting a discount/scholarship. It assumes you already know Python, pandas, matplotlib, and linear regression, so it is not an entry point, and reviewers widely note the certificate carries limited weight on a resume by itself.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Udacity's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official Udacity Data Scientist Nanodegree (nd025) syllabus, prerequisites, instructors
- Course Report - Udacity reviews and aggregate rating (4.7/5, 627 reviews)
- Oscar Leo - My Review of Udacity After Completing 7 Nanodegrees
- UpskillWise - Udacity Pricing 2026 (USD 249/mo, USD 846 bundle, refund policy)
- Class Central course listing for Data Scientist Nanodegree (nd025)