Udacity Data Scientist Nanodegree review, and whether it is worth the huge premium
Alright you may have heard that “data scientist is the sexiest job in the world now” and hence, here you are, to become a bit sexier.
If you are considering taking the Udacity Data Scientist Nanodegree to achieve this, here is my personal experience after graduating from the nanodegree.
By the way, I am a big four accounting firm associate working with data and therefore, I will also briefly share my thoughts from a practical perspective too. I will be talking about the following points (you time is precious and therefore, jump straight to the relevant part if you wish):
- Overall course content and quality
- Pros and cons
- Usefulness from a practical perspective (i.e. if you want to get a job or use the skills for work)
- My personal recommendation (and how to save money if you are determined to take this course)
Overall course content and quality
List of topics covered
If you want to have a high level idea of what areas are covered in this course, here is the list of topics covered:
1. Introduction to Data Science
2. Software Engineering — completed in another course
3. Data Engineering
4. Experimental Design and Recommendations
5. Data Scientist Capstone
6. Congratulations
1. [Capstone Content] Convolutional Neural Networks
2. [Capstone Content] Spark
3. Prerequisite: Linear Algebra
4. Prerequisite: Statistics — completed in another course
5. Prerequisite: SQL
6. Prerequisite: Python for Data Analysis
7. Prerequisite: Data Visualization
8. Prerequisite: Command Line Essentials
9. Prerequisite: Git & Github
Referenced course — Anaconda and Jupyter notebooks
Referenced Udacity course — Command Line
Detailed listing of topics covered
If you want to know the topics covered in more details, feel free to take a quick look of my course I took when taking the course.
Content quality
The course content in general is of OK standard when you considered other alternatives.
Please don’t get me wrong. I do enjoy the course. It is a mixture of videos, exercises, text readings and all instructors are lovable and explain concepts really clearly and concisely. It covers what you need to know without going into too much details. This is actually one of the differentiating factors of this course, where instructors all “show there faces rather than simply sharing your screen” and I think all of them are approachable and friendly.
The overall course content is designed well, starting from the basics and moving to more sophisticated areas. It essentially started with Python programming language and then data engineering (ETL pipelines, NLP pipelines and machine learning pipielines). It then concludes with experimental design and recommendations.
However, if you are like me, I can easily say that those discounted courses on Udemy, which costs roughly $20, is just as good. P.S. No one is paying me anything and this is simply my personal feeling after studying from a few platforms.
Extracurricular content
There are other contents such as linear algebra, SQL, statistics, data visualization, command line essentials, Git&GitHub, Anaconda and Jupyter notebook etc. But please note that these are available for free too, which is why I don’t really consider them as “extra benefits for paying huge premium”.
Projects
Interesting fact, the first project actually forces you to write a Medium blog describing the project. So you can be sure that you will come back here if you decided to take the course. P.S. I fell in love with Medium long before taking the course though.
There are a total of 4 projects and you can read more about the projects in their official website:
To give you a quick example of project and how it is in general, one of the project is an implementation of disaster response pipeline, which essentially use natural language processing to analyze messages sent during disasters and better respond. The rational of the projects are all good but all of them are way too typical and widely available for free all over the internet.
The only good point about project is perhaps that fact that they are reviewed and feedback will be provided by a Udacity reviewer.
Pros and cons
Pros
- Projects are reviewed: all your four submissions will be reviewed by a Udacity reviewer with feedback provided. You will only pass if you meet al project requirements
- Graduate certificate: if you pass all project reviews, you will get a certificate (similar to the first photo in this article, which is my certificate)
- Instructors are lovable and (look) friendly: I am following many of them on Twitter because I do miss their energetic voice
- Learning community: I have to say that the learning community of Udacity is one of the best, partly because there are Udacity mentors answering the questions and most of them have been very good and helpful in answering your questions. The community is very active too, with both students and reviewers actively asking and answering question. It is built within Udacity course and one that most reminds me of stack overflow, which we all know and we all love.
Cons
- So expensive! There are two options: three month and pay as you go and I will highly recommend, if you are a beginner, to learn from free courses first and then, you can finish this course the assignments much quicker if you choose pay as you go option. For example, there are way too many equally good free alternatives for Python courses and there is no reason why you should pay so much for learning python. I will do a review of the alternative free courses too and make sure you follow me if you are interested in that too.
- Course content is good but only “equally good compared to cheaper alternatives”. There are too many alternatives such as Udemy, Coursera, Edx etc. I personally spent most of my time on Udemy, partly due to its cheaper price (usually $20 for most best selling courses if you get the discount) and I also love a few instructors, which draw me to the platform (big shout out to Angela Yu if you are listening).
- Too much emphasis on Python and machine learning: After taking the course, I feel like this is more of a Python course with huge emphasis on machine learning. While I agree that Python is a widely used language and machine learning can be more exciting, but I do believe a data science degree should have more diversity. I will talk more on this later.
Usefulness from a practical perspective (working in the industry)
I am a big four associate who working with data and I can safely say that there is a huge disconnect from this course to what you will really use in the industry.
Emphasis on Python (which is good or bad?)
This course focus primary on Python programming language (with some SQL course in extracurricular section, which is also free on Udacity). However, my personal experience is that when you are working in the industry, Python alone is rarely good enough unless the organization or role you are working for use Python exclusively. Very often, you are expected to have a more diverse skillset, including SQL, SQLite, software such as Alteryx, PowerBI, Tableau etc. You get the idea.
But I do believe that Python is a good programming language to start with especially if you are a beginner. This is because you will find the concepts you learn in Python is largely transferable, or at least useful when learning other languages such as SQL, which is heavily used.
As someone working in the industry, having exposure to Python is definitely good but please be reminded that exposure to a more diverse skillet is beneficial too. You don’t have to be an expert in everything but if your boss asks you whether you know what is a database, you are expected to know such general concepts too.
Emphasis on machine learning
I personally believe this course focuses way too much on machine learning, which from my personal experience, is rarely used unless your organization is very big and very cutting edge.
However, most of the work as a data scientist is much less exciting, such as data cleansing, working with databases etc. Machine learning is exciting but its usefulness and reliability is very limited in practice. I would certainly hope that more “boring skill” are taught in this course and more practices which are useful in real life is introduced in this course. I guess this is why they have included the SQL module towards the end.
If you are looking for a job
If you are looking for a job and rely on the Udacity certificate to polish your resume, then I do believe that the fact that Udacity project submissions are marked and reviewed by a reviewer and you only get the certificate if you pass the projects, does make this certificate a bit more convincing and creditable. However, if this is the case, feel free to familiarize yourself with fundamental Python concepts and data engineering pipeline concepts, if you want to quickly power through the course and get the certificate.
It is worth noting that if you are relying on this resume to get a job, it is worth considering other alternative such as online courses offered by college. There are quite a number of options offered by reputable universities, especially after the Covid challenge started. They could be more expensive but I do believe that they could be more credible.
It is also important to mention that a good portfolio is just as important to showcase your skill if you are looking for a job. For example, a good portfolio on GitHub and a good review score on stack overflow helps a lot too. More on this in my later articles.
My personal recommendations
If price is a major factor, I would say don’t take the Udacity course considering its ridiculously high price tag.
Unless you care about that certificate and unless you really want someone to provide feedback for your projects, this nanodegree is certainly not good value for the money you pay.
It was not even the case when I started my university but nowadays, there are simply so much cheaper alternatives which is just as good. I did talk about this in the con section if you want to take a read. Some popular alternative online course platform include Udemy, coursera, Edx, brilliant etc.
If money is not a limiting factor here, please skip this paragraph and enjoy the course. But if it is, and if you want to pass the course and get that certificate, I would recommend you to at least cover Python fundamental concepts first before purchasing. This is because the fundamental Python concepts can take quite a while to go through and let the knowledge soak in. If you are already familiar, and if you choose the pay as you go option, as if you power through, I think it is possible to finish the course in just one month. This then gives you a vastly different price tag.
Lastly, happy learning. Stay hungry, stay foolish.
