So you’re considering the Udacity Data Science Nanodegree? I finished this program in 2019, and in this video, I’ll provide my Udacity Data Science Nanodegree review.

First, I’ll describe the courses and projects in the Udacity Data Science Nanodegree. Next, I’ll talk about factors such as the lecture videos, project reviews, and extracurricular courses in the Nanodegree. And finally, I’ll give my honest opinion on the Udacity Data Science Nanodegree.

The nanodegree is divided by five courses.

Courses & Projects

Course 1: Solving Data Science Problems

In this first course, you learn the data science process, how to build effective data visualizations, and how to communicate with various stakeholders.

Project: Write A Data Science Blog Post

The project associated with this course is called “Write a Data Science Blog Post.” It involves writing a Python script using a dataset of your choice and writing a blog post about it on a platform such as or another website of your choice.

This project is a great way to practice the following:

  • Manipulating data
  • Cleaning data
  • Building a simple statistical model to show feature importances; or you could build a clustering algorithm instead

And you’ll do all that using a structured format wit the use of the CRISP-DM format, which is a methodology for structuring data science projects.

You’ll also write a blog post on a content platform of choice and communicate your project in a clear and understandable format.

The Python skills required for this project are closer to the beginner level than intermediate level. And Udacity provides a list of datasets to choose from and you may also have the option of using a dataset off the internet of your choice.

Course 2: Software Engineering for Data Scientists

In the second course, you’ll develop software engineering skills that are essential for data scientists, such as creating unit tests and building classes.

This is good knowledge to have if you work in or you plan to work in an IT department.

There’s no project required for this course.

Course 3: Data Engineering for Data Scientists

In this third course, you’ll learn to work with data throughout the entire data science process. It involves running pipelines, transforming data, building models, and deploying solutions to the cloud.

Project: Build Disaster Response Pipelines with Figure Eight

The project associated with this course is called “Build Disaster Response Pipelines with Figure Eight.” Figure Eight is a machine learning company and in this project, you’ll build a data pipeline with the use of command line arguments and create a database using the SQLlite package in Python. Then, you’ll create a Github repository for your project, which will be reviewed by someone at Udacity.

Before you take on this project, it definitely helps to have some experience with command line arguments. This project involves a lot of moving pieces and can be challenging to some students.

I recommend at least an intermediate-skill in Python before attempting this project.

I’ll offer links below to some beginner-level Python courses that will help you prepare for this project.

I found this to be the 2nd most difficult project in the nanodegree program.

Course 4: Experiment Design and Recommendations

In the fourth course, you’ll learn to design experiments and analyze A/B test results. You’ll also explore approaches for building recommendation systems such as content-based filtering and collaborative filtering.

Project: Design a Recommendation Engine with IBM

The project associated with this course is called “Design a Recommendation Engine with IBM.” In this project, you’ll build a recommendation engine, based on user behavior associated with a social network in IBM Watson Studio’s data platform, to surface content most likely to be relevant to a user.

I found this to be the most difficult project in this  nanodegree program. I think it would really help if you approached this project with at least an intermediate skill level in Python and at least some experience using matrix algebra and matrix factorization in Python scripts. There are videos offered in this course about those subjects as well, but it’ll help you out if you practice those mathematical concepts with Python before going through this project.

Course 5: Data Scientist Capstone

In this course, you’ll leverage what you’ve learned throughout the program to build your own open-ended Data Science project.

Project: Capstone Project

This was one of my favorite parts of the nanodegree program because I could choose my own project from a selection of projects offered. The projects you can choose include:

  • Dog Breed Classification
  • Customer Segmentation with Starbuck data
  • A machine learning project involving data from a financial services company
  • Spark for Big Data

Additionally, you’re free to choose any project you want using any dataset you choose off the internet.

I chose the project that involved using PySpark to predict customer churn. PySpark is a Python package pertaining to big data. And customer churn is a term used to describe the concept of customers deciding to end a relationship with a business.

I published key insights from my project in a article, which was reviewed by someone at Udacity. I also created an associated Github repository for review. The project was very straight-forward and I didn’t have any issues with it.

Other Considerations

It’s important to mention the extracurricular lessons that were included with this nanodegree at the time. They course titles included:

  • Python for Data Analysis
  • SQL
  • Data Visualization
  • Command Line Essentials
  • Git & Github
  • Linear Algebra
  • Practical Statistics

As I mentioned before, I recommend having intermediate-level skills in Python, some knowledge of command line arguments, and basic knowledge of linear algebra before going through this program.

When it comes to the lecture videos, they are among the best I’ve seen and they’re very informative. The presentations from the speakers and the use of animations were incredibly insightful.

When it comes to the project reviews, I typically received a review within a few hours after submitting a project. If submitted a project that needed additional work, my reviewer clearly communicated what needed to be done.

The mentor support is helpful when it comes to challenges that are relatively common in the projects.

And I found the resume and LinkedIn profile review services valuable as well, which are available through their Career Resource Center.

Final Thoughts

Overall, it was a great learning experience and I continued to use many of the Data Science, Machine Learning, and Python concepts from this program in work situations afterwards. Also, I firmly believe that project-based learning is the best way to learn Data Science, Machine Learning, and Python and in that regard, this program is one of the best educational options available.



Udacity Data Science Nanodegree:

Resources to Help you Prepare for the Udacity Data Science Nanodegree:

Udacity “Programming for Data Science with Python” Nanodegree:

Udacity Data Analyst Nanodegree:

Udacity “Intro to Machine Learning with PyTorch” Nanodegree

Python Data Science Handbook: Essential Tools for Working with Data:


Disclaimer: A few of the links in this post are affiliate links.



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