WorkShop Survey

Please visit our online survey so that we can get a greater feel of your existing knowledge on Python and Machine Learning in order for us to pitch this workshops content at the right level, there are no right or wrong answers:

Recommended Further Reading

Before attending our workshop it may be useful to try refresh your skills in Python above and beyond what you covered at university. If you are not aware already, as a student you can freely access all of LinkedIn Learning’s training material online by signing up with your student email address. For many of these online courses completing them will enable you to gain an online certificate which you can attach to your LinkedIn profile – something we highly recommend for your future employment prospects.

Whilst we endeavour to cover as much as we can in the 2 day workshop we are aware that many students will come with differing levels of experience. The main Python skills that we want to try cover during this course are using these 3 main libraries:

  • Pandas: for reading and manipulating data.
  • Matlplotlib: library for visualising data.
  • Scikit-Learn: library for applying machine learning algorithms to data.

We will also be trying to cover some useful tips on how to write Python code effectively employing best practices of developers in industry introducing concepts such as:

  • Collaborative coding using version control such as GIT
  • Coding using Integrated Development Environments (IDE’s)
  • Debugging code using an IDE

Given below here is a list of useful online courses that could be useful to look through before attending the course. Please do not worry if any of the material looks overly complicated we will try to make sure you gain that understanding on the day. You are welcome to reach out to us before then if you would like further advice on

Useful Lessons on Coding In Python
If you are looking for a complete beginner’s guide to coding in Python here is a great course:

If you are looking to learn about developing with Python in IDE’s, in particular PyCharm and compare it to other tools such as Jupyter which is commonly used to teach coding in Python:

Also a course on how to develop Python code using the Visual Studio IDE, which is a another popular choice in industry:

To get a better picture of why debugging code is a necessary/useful to get the most out of coding see this great course which is language agnostic:

Here is a part of a course that explain debugging code in Python using the Visual Studio IDE:

Understanding how GIT works and setting up a personal GitHub account see:

This is another GIT course, has lots of setup for all operating systems:

Useful Lessons for Practical Data Science and Machine Learning
For a great explanation of how to wrangle data reading in via Pandas and visualising using Matplotlib see:

For a useful overview of practical machine learning (data mining) in Python especially covering clustering and classification see:

For an excellent overview of the core concepts of applying Machine Learning techniques on real world data see: