So, you are interested in Data Science and you have come across this free course offered by WQU and you are evaluating whether this course might be worth your time. In this post I will share my experience from having taken this course six months ago. WQU shared a list of key skills which are used on this course which can be found here. At first, the course starts with basic concepts of programming such as: defining variables, data types, loops, functions, classes, etc. It then builds on these core concepts and introduces key packages such as NumPy and Pandas which have ready made modules to read in data. I would say that the rest of module 1 can be summarised in transforming this data and querying it; it’s no coincidence that WQU put ‘Data Wrangling’ at the top of the skills list.
Applying for the Course
The application to the course is relatively straightforward. After clicking on “APPLY NOW” on their homepage, you will be redirected to a page which has details of the application start and deadline for the upcoming two sessions. WQU also indicates that you should be able to commit around 8-10 hours per week. Once you click on “Apply” you can then sign in using your Github, Google or Linkedin account.
You can go to https://wqu-apply.thedataincubator.com/status to check your status, and you will also be notified via email once admission decisions have been made.
How is the Course Taught
Once you get accepted on the course, you will be sent a username and password to login to their Jupyter hub. Here all students will have the same environment and dataset to be able to practice and work on course work. You will be using Jupyter notebooks to run and execute your code. I thought this was a clever option chosen by WQU as it eliminates unnecessary setup time and discussions relating to getting the working environment up and running.
The teaching is a mix of recordings and online office hours which are streamed over YouTube. These seminars provide students the opportunity to ask questions to the instructor via the chat whilst going through course materials and assignments. Students also get to see the instructor code in realtime and understand the thought process that goes behind writing the actual code which was definitely the value added part of the course.
How are you assessed?
In the Jupyter hub directory there will be a number of mini projects that need to be completed before the end of the course. All the projects are made available at the start of the course should students want to get a head start. These mini projects can be found in Jupyter notebooks and require the student to import the data, manipulate it and pass the results to the grader. The grader then checks the result that was passed and assigns a score of 0-1 (sometimes you will see scores higher than 1). Students need to complete all mini projects if they want to get the Credibly Badge at the end of the course. A score of at least 0.9 is required for all projects if the student also wants to get honours.
Difficulty & Personal Experience?
So, how difficult is it to complete the course? The short answer is that since the instructor puts a lot of time and effort in going through the projects during the office hours (which are also recorded and readily available for students) anyone who can just follow along can pass the course so getting the Credibly Badge is not difficult. However, there is a big difference between passing vs understanding and internalising the concepts, especially when it comes to coding. There is no substitute in coding other than building up experience on a daily basis. The difficulty will obviously depend on the student’s prior knowledge of coding but for someone who is relatively new I would say that there is a lot of material that gets covered and 8-16 weeks might not be enough, especially for module II. Once the course finishes, students will lose access to their accounts which means that they will no longer be able to access the learning materials after the course. WQU provides steps in downloading materials prior to the end of the course, something that I strongly recommend students do.
Personally, I found a lot of value in putting into practice the concepts that I learned throughout the course. At my work I deal with large volumes of data and being able to use Python in retrieving and manipulating it is an invaluable skill. Becoming good at coding is like building up muscle, by solving harder problems you will get better at it and this course gives you a good starting point for developing your own skills. It does cover a lot of the basics of coding in python that you would find in other introductory courses, so in my case I can confirm that the time in taking this course was well spent (not to mention that the course is free).
Overall, I must say that I found this course very helpful and I strongly recommend it. Some students, especially ones with little coding experience might find the information a bit overwhelming so I strongly recommend downloading the videos after the course in order to have the option to revisit the material. As for the qualification’s brand value, given that this is a free course (no proctoring, etc.) I don’t foresee it adding too much value on your CV. However, when it comes to coding, qualifications are not important, rather a portfolio of your projects that demonstrate your skills. If you are relatively new to coding I think this might be a good option worth considering.