Udacity Programming for Data Science with Python Review 2026

Written by Akshay Vikhe, machine learning learner and course reviewer. First published December 2020; the first-person sections are a 2021 graduate’s account, kept as written. Updated and fact-checked by Josh Hutcheson, OnlineCourseing editor. See our review methodology.

Some links on this page are affiliate links: if you enroll through them we may earn a commission at no extra cost to you. It does not change our verdict, and several of the alternatives we name pay us nothing.

Last updated: 13 October 2026 (full refresh; syllabus, projects, prerequisites, ratings and prices re-checked on Udacity’s nd104 program page that day).

Udacity’s Programming for Data Science with Python Nanodegree (nd104) is a 63-hour beginner program in four courses: SQL for data analysis, Python for data science, scripting with the shell and Python, and version control with Git. It has four reviewed projects, was rated 4.8 from 838 reviews on Udacity’s page on 13 October 2026, and was last updated by Udacity on 26 August 2026.

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Programming for Data Science with Python Nanodegree

Here is the program as Udacity sells it today, next to what this review described when it was first written.

Source: Udacity’s nd104 program page, read 13 October 2026.

Overview

Data science combines statistics, programming and domain knowledge to answer questions with data. Almost every data role, from analyst to machine learning engineer, starts with the same four tools: SQL to pull data out of databases, Python to clean and analyze it, the command line to automate repetitive work, and Git to track and share code. This Nanodegree teaches exactly those four and nothing else, which is its strength.

It does not teach statistics, machine learning or dashboards. Those come in the follow-on programs: the Data Analyst Nanodegree for wrangling, statistics and visualization, and the Data Scientist Nanodegree for modeling.

About the Udacity Programming for Data Science with Python Nanodegree

The program is a set of four courses, each ending in a project that a reviewer grades against a rubric. Lessons mix short videos, reading and quizzes, with coding done in browser workspaces and Jupyter notebooks. Udacity lists six instructors: Derek Steer (CEO, Superframe, and author of Mode’s SQL tutorial), David Elliott (data scientist and data engineer), Metin Akyol, PhD (quantitative analyst at Fidelity), Juno Lee (data scientist), Kedar Kodgire (data and cloud engineering lead) and Richard Kalehoff.

What Udacity’s plan card includes

• Unlimited access to Udacity’s catalog while you subscribe
• Hands-on projects with expert feedback
• Personalized career coaching and interview prep
• Program certificates

Related reviews: SQL Nanodegree (if SQL is your main goal), AI Programming with Python (if AI is), and Udacity’s Introduction to Python.

What was my motivation to enroll in Udacity Nanodegree?

The account below is a graduate’s, written in 2021 about the version of the program they took.

“There are more holes to be filled as technology is used by humans more and more. These gaps try to blur the distinction between man and machine by assisting one another in carrying out duties more quickly and easily. Computer Science has always been my passion. Back in High School, I was known for my tech enthusiasm and top scores in programming classes.”

“Later I obtained a bachelor’s degree in Information Systems from Haramaya University in July 2014, and I worked at Wolkite University as an Assistant Lecturer. In September 2016 I got two years of study leave from my employer and joined Addis Ababa University for a Master’s degree in Information Science and Systems (Language Technology). Besides, I was able to attend and present my scientific papers and researches at different conferences, workshops, and summer schools.”

“After I attended and presented my scientific paper in LxMLS 2019 (the 9th edition of Lisbon Machine Learning Summer School) I decided to apply for this Udacity program because I am sure it would strongly enrich my future studies and help me in my prospective career. I saw this program as a great opportunity for hands-on, real-world programs, which includes projects before I made my transition from academia to the industry world.”

That is a common profile for this program: someone with an academic or non-data background who wants the practical tools employers expect, with projects to prove it.

Job Scenario

Demand for data skills is real but more competitive than the old version of this page suggested. The US Bureau of Labor Statistics reports a May 2025 median of $120,230 for data scientists and projects 35% employment growth from 2025 to 2035, against 3.5% for all occupations. BLS has no separate “data analyst” occupation; related roles such as operations research analysts ($88,940) and management analysts ($101,860) also use SQL and Python.

Entry is harder than headline growth implies. A 2026 analysis of over 1,000 data scientist job postings by 365 Data Science found that roles asking for 0 to 2 years of experience were now the least common, and that about two-thirds of postings mentioned a master’s degree or PhD. For a beginner, the realistic first target is a data analyst or reporting role, where SQL and Python fluency plus a portfolio matter more than a degree.

Costs and Duration

Udacity sells access by subscription. On 13 October 2026 the nd104 page listed $249 a month, a four-month bundle at $212 a month ($846 in total), or $999 to buy just this program outright. The page also showed a 40% discount banner for new users; check the terms at checkout because promotions change often.

My thoughts on course pricing

“They gave me 79% off, and I think I paid a reasonable price for what I was able to learn. Without their discount, the original course price is a bit expensive.” Discounts of that size are not on offer today, but Udacity’s new-user and seasonal promotions still change the math, so check the live price. Also read: our Udacity Nanodegree review for how the subscription works.

Check the current nd104 price →

Prerequisites for Udacity’s Programming for Data Science with Python Nanodegree

No programming is required. Udacity lists four prerequisites: basic descriptive statistics, basic spreadsheet use, basic arithmetic, and logarithms and exponentiation. The earlier version of this review said no math was needed; the current page disagrees, though the bar is low.

Quick self-check

• Can you explain the difference between a mean and a median?
• Can you sort and filter a spreadsheet, and write a simple SUM or AVERAGE formula?
• Do you know what 10 squared and log base 10 of 1,000 are?

If yes, you are ready. You also need to be comfortable installing software and working with files and folders, because the shell and Git courses work directly with your file system.

Syllabus of Udacity’s Programming for Data Science with Python Nanodegree

Course 1: SQL for Data Analysis (18.5 hours)

The longest lesson list in the program: 41 short lessons from SELECT, LIMIT, ORDER BY and WHERE through arithmetic and logical operators, entity relationship diagrams, inner and advanced JOINs, aggregation (COUNT, SUM, MIN, MAX, AVG, GROUP BY, DISTINCT, HAVING), dates, CASE statements, subqueries, window functions and data cleaning. The project, Retail Analysis, asks you to answer business questions with SQL, write a data dictionary and draw an ERD.

If SQL is all you want, this course is also sold separately as Udacity’s free SQL for Data Analysis (ud198), without the reviewed project.

Course 2: Introduction to Python for Data Science (21.2 hours)

Lessons: data types and operators, data structures, control flow, functions, an introduction to Python packages for data science, loading and exploring data, computation and statistics, and data visualization. Udacity’s description names pandas and Polars. The project, Are We Alone in Space?, has you load, visualize and analyze NASA’s exoplanet data while practicing responsible exploratory data analysis.

Course 3: Scripting for Data Science (12.3 hours)

Lessons: file navigation and management in the shell, shell scripts for data automation, and Python scripting for data workflows. The project, Automating Data Cleaning with Shell and Python, asks you to organize files, validate JSON, normalize records and merge datasets into analysis-ready output. This course is new since the original review and is the most practical addition.

Course 4: Introduction to Version Control (10.8 hours)

Lessons: what version control is, creating a Git repository, commits, tags and conflicts, remotes and developer repositories, and writing READMEs. The project, Post Your Work on GitHub, has you fork a repository, commit changes and use a .gitignore file.

View the full nd104 syllabus →

How was the project experience?

The graduate’s notes below describe the two projects in the 2021 version: a SQL analysis of the Sakila DVD rental database and a Python analysis of US bike share data. Both have since been replaced, but they show the kind of work and feedback to expect.

“The projects were challenging and interesting. Now you have to apply all the elements you have grasped in one succinct way, which is quite different from the simple lesson-specific exercises. The project reviews I had were personalized and worth the effort.”

Investigate Relational Database

“In this project, I used SQL to explore a database related to movie rentals. I wrote SQL code to run SQL queries and answer interesting questions about the database. As part of the project submission, I ran SQL queries and built visualizations to showcase the output of the queries. For this project, I was querying the database to gain an understanding of the customer base, such as what the patterns in movie watching are across different customer groups, how they compare on payment earnings, and how the stores compare in their count of rental orders.”

Question 1

“We want to understand more about the movies that families are watching. The following categories are considered family movies: Animation, Children, Classics, Comedy, Family, and Music. Create a query that lists each movie, the film category it is classified in, and the number of times it has been rented out.”

Question 2

“Now we need to know how the length of rental duration of these family-friendly movies compares to the duration that all movies are rented for. Can you provide a table with the movie titles and divide them into 4 levels based on the quartiles of the rental duration for movies across all categories?”

Question 3

“Finally, provide a table with the family-friendly film category, each of the quartiles, and the corresponding count of movies within each combination of film categories for each corresponding rental duration category.” Questions like these, which need JOINs, window functions and CASE logic together, are still the core of the current Retail Analysis project.

Explore US Bikeshare Data

“In this project, I used Python to explore data related to bike share systems for three major cities in the United States: Chicago, New York City, and Washington. I wrote code to import the data and answer interesting questions about it by computing descriptive statistics. I also wrote a script that takes in raw input to create an interactive experience in the terminal to present these statistics.”

The Datasets

“Randomly selected data for the first six months of 2017 were provided for all three cities. All three of the data files contain the same core six columns: Start Time, End Time, Trip Duration, Start Station, End Station and User Type. The Chicago and New York City files also have Gender and Birth Year.”

Statistics Computed

“I wrote code to provide the following information: popular times of travel (most common month, day of week and hour of day); popular stations and trips (most common start station, end station and start-to-end trip); trip duration (total and average travel time); and user info (counts of each user type, counts of each gender, and earliest, most recent and most common year of birth).”

My thoughts on the course timeline

“I had to commit more than 4 hours per day because if you don’t finish within the timeline I would have to pay again. I think as long as you paid, it should allow you to go take the program at your pace.” The same pressure exists on today’s monthly plan. If you cannot study steadily, the $999 one-time option removes the deadline.

Pros and Cons of the Udacity’s Programming for Data Science with Python Nanodegree:

Pros:

• The right four tools. SQL, Python, the shell and Git are what every data team uses daily, and few beginner courses cover all four.
• Four graded projects on real data. A retail database, NASA’s exoplanet data and a data-cleaning pipeline make better portfolio pieces than toy exercises.
• Short and affordable for Udacity. 63 hours is about two months, or $498 at list price.
• Current. Last updated by Udacity in August 2026; a scripting course has been added since our original review, and Polars is taught alongside pandas.

Cons:

• Basic if you already code. As the original review put it, experienced programmers may find it boring.
• No statistics or modeling. You need a second program before most data jobs.
• The SQL course is free elsewhere. Udacity also sells a free course with the same name, without the project review.
• Older pages promised more support. The “world-class mentorship” described in earlier reviews is not on today’s plan card; project reviews are the expert help you can count on.

Udacity Programming for Data Science with Python Nanodegree Certificate

When all four projects pass, Udacity issues a Nanodegree certificate. It is not accredited by any university, but it is a clean way to point a recruiter to the four projects behind it. Add the certificate to LinkedIn and pin the project repositories on GitHub; the Git course’s final project is designed to get you there.

The Advance features you get with Udacity

1. Project reviews

Every project is graded against a published rubric. A reviewer either passes it or returns it with comments, and you can resubmit. The graduate above found the reviews “personalized and worth the effort”. One 2020 learner quoted below said a SQL project came back very quickly.

2. Community and discussion forums

Udacity runs a learner community where you can ask questions and see answers to common problems. It is useful for unblocking, but do not expect one-to-one tutoring.

3. Projects and content

Content is project-centric. The current projects use realistic datasets and tasks, and the program was refreshed in August 2026.

4. Career coaching

Udacity’s plan card lists personalized career coaching and interview prep. For a foundations program, the useful part is turning the four projects into a portfolio.

Instructors for Udacity Programming for Data Science with Python Nanodegree

Udacity’s page listed six instructors on 13 October 2026. Two names from the earlier version of this review, Josh Bernhard and Karl Krueger, are no longer listed for this program.

1. Derek Steer (CEO, Superframe)

Derek founded Mode Analytics, built analytics foundations at Facebook and Yammer, and wrote the widely used SQL tutorial at Mode. He teaches the SQL course.

2. David Elliott (Data Scientist, Data Engineer)

Listed by Udacity as a data scientist and data engineer.

3. Metin Akyol, PhD (Quantitative Analyst, Fidelity)

Listed by Udacity as a quantitative analyst at Fidelity.

4. Juno Lee (Data Scientist)

Juno led Udacity’s School of Data Science curriculum and built recommendation, computer vision and NLP models as a data scientist.

5. Kedar Kodgire and Richard Kalehoff

Kedar Kodgire is listed as a data and cloud engineering lead; Richard Kalehoff is a long-time Udacity instructor who teaches the Git material.

Reviews from graduates of Udacity’s Programming for Data Science with Python Nanodegree

These reviews were collected in 2020 and 2021 and describe the earlier, three-course version.

I really like it and think things are very well explained. The exercises on each chapter make you identify clearly the things you got clearly and the ones you didn’t, making you revise and understand everything.
Isabel C., learner review (2020)

Additional positive notes are that the technical mentor responded to my question very quickly, and my SQL project was reviewed very quickly as well.
Dean A., learner review (2020)

Toheeb Ayoola Abolade

About a month ago, I took the first huge step to transitioning into a new career, Data Science, by enrolling in Udacity’s Nanodegree Program. It was a great experience, and I got the chance to sharpen my mind and pick up new skills along the way.
Toheeb Ayoola Abolade, data analyst at Redot Agency, via LinkedIn

Vivek Chavan

This course gave me a great learning curve. I learned how to manipulate large datasets, perform version control, and access modern databases. I got to know about the fundamental programming tools for data professionals: Python, SQL, the Terminal and Git.
Vivek Chavan, via LinkedIn

David Torres

In this course I learned how to extract and manipulate data using SQL and Python through Pandas Dataframes; furthermore, I learned version control with Github. Today I’m focusing on expanding my Python skills and understanding business perspectives to generate actionable insights.
David Torres, data analyst, global logistics procurement at Johnson Controls, via LinkedIn

Antonio Riccelli

I would recommend it to anyone looking to expand their skill set in this direction. Had a lot of fun with the Python project, in particular, it was good practice in that it requires the student to write functions that collect the user’s input and use it to filter a DataFrame before showing the required statistics.
Antonio Riccelli, supply chain analyst, via LinkedIn

Recent reviews and criticism

Recent reviews on Udacity’s page (December 2025 to August 2026) are four and five stars, praising the structure and hands-on projects. The sharpest criticism we found is older: a January 2022 r/learnprogramming post by a learner who took this program alongside two other Nanodegrees said the Python projects asked for far more than the lessons taught and that support replies were slow. The program has been rebuilt since, but plan on using outside documentation, as the graduate above did.

Read recent reviews on Udacity →

Explore More Courses

Looking for more options? Browse our best Python courses and best data science courses roundups to compare across platforms.

Conclusion – Is the Udacity Programming for Data Science with Python Nanodegree Worth it?

4.0 out of 5. Yes, for a beginner who wants SQL, Python, the shell and Git in one structured program with four graded projects, and who can finish in about two months for roughly $498. It is one of the more efficient Udacity programs: little filler, practical projects, and a scripting course added since our original review.

No, if you already code, or if you only need SQL. Here is how it compares with the main alternatives:

After this program, the natural next step at Udacity is the Data Analyst Nanodegree. For machine learning, see AWS Machine Learning Engineer, Generative AI or Agentic AI, and for algorithms practice the Data Structures and Algorithms review.

Start Programming for Data Science →

FAQ’s of Udacity Programming for Data Science in Python Nanodegree

1. Why should I enroll?

Enroll if you want a structured, graded introduction to the tools data teams use, and plan to continue into analysis or data science. Do not enroll expecting the certificate alone to get you hired; the four projects are what you will show.

2. What jobs will this program prepare me for?

It prepares you for the next step, not a specific job. The skills apply to data analyst, reporting and junior data engineering roles, but you will need statistics, visualization and a larger portfolio for most of them.

3. How do I know if this program is right for me?

It is right if you have never written SQL or Python, can study about 10 hours a week, and want someone to grade your work. Try Udacity’s free SQL for Data Analysis course first: if you enjoy it, the Nanodegree adds three more courses and project reviews.

About the author

Akshay Vikhe is a machine learning learner with a strong interest in technology who reviews courses that add real value to students’ careers. About OnlineCourseing · How we review · Every Udacity Nanodegree (hub).

Frequently asked questions

Is the Udacity Programming for Data Science Nanodegree worth it?

For a genuine beginner it is a well-structured, 63-hour introduction to SQL, Python, the command line and Git, with four projects a reviewer grades. Finished in two months, it costs about $498 at list price. If you already code, or only need SQL, it will feel basic: Udacity’s free SQL for Data Analysis course covers SQL on its own.

What do you need to know before starting?

No programming. Udacity lists four prerequisites: basic descriptive statistics, basic spreadsheet use, basic arithmetic, and logarithms and exponentiation. If you can calculate an average and a median and sort a spreadsheet, you are ready.

How long does the Programming for Data Science Nanodegree take?

Udacity estimates 63 hours across four courses and 58 lessons. At 10 hours a week that is six to seven weeks, plus a few days for each of the four project reviews. The older estimate of three months at 10 hours a week no longer applies.

How much does it cost?

On 13 October 2026 Udacity charged $249 a month, $846 for four months (about $212 a month) or $999 to buy the program outright, and showed a 40% discount banner for new users. Most learners finish in two months, so $498 at list price is the typical cost.

What projects are in the program?

Four: Retail Analysis (SQL, with a data dictionary and an entity relationship diagram), Are We Alone in Space? (Python analysis of NASA exoplanet data), Automating Data Cleaning with Shell and Python, and Post Your Work on GitHub. The movie rental and US bike share projects in older reviews have been replaced.

Does it teach pandas?

Yes. The Introduction to Python for Data Science course covers Python packages for data science, loading and exploring data, computing statistics and visualizing data, and Udacity’s description names pandas and Polars.

What can it lead to?

It is a foundation for data analyst roles and the first step into Udacity’s Data Analyst and Data Scientist Nanodegrees. On its own it is too short to make you job-ready, but its four projects are a good start on a GitHub portfolio.

Is it the same as the Data Analyst Nanodegree?

No. Programming for Data Science teaches the tools (SQL, Python, shell, Git). The Data Analyst Nanodegree assumes them and focuses on data wrangling, statistics and visualization. Many learners take them in that order.

Can I get a refund?

Udacity’s Terms of Use (updated January 16, 2026) give seven days from your first purchase to cancel for a full refund. After that, fees are not refunded, and canceling a monthly plan keeps access until the end of the month you paid for.

Is the certificate accredited?

No. It is a Udacity completion certificate, not academic credit. Its value is the four projects behind it, which you can link from LinkedIn and GitHub.

Related: Udacity Intro to Programming review · Best Udacity courses